process mining phd topic

• A Conceptualisation of Process Mining Impacts

This PhD research project focuses on conceptualising the notion of impact in the process mining domain. Process mining is a field of techniques that automatically discover process models and monitor performance indicators, bottlenecks, resource constraints, and regulatory performance. It draws from computational intelligence, data mining and process science. As the domain sees growing interest within many industry sectors, several tools and techniques have been developed and applied in various contexts with promising results. Insights from industry practitioners and consultants estimate that the process mining market will continue to grow and expand financially. While many non-adopters plan to conduct pilot runs, current global-scale users intend to expand their initiatives as they believe process mining delivers value. Using qualitative and case study research approaches, this study seeks to provide a detailed understanding of the nature of impact in process mining and what factors contribute to the success of process mining in an organisation. This project seeks to offer an alternative to existing anecdotal approaches to assessing process mining impacts by proposing an empirically validated process mining impacts framework for both industry and academia.

The objectives of the study are to:

• identify the key dimensions and elements of process mining impact
• determine the success factors and contextual nuances that could influence process mining impact in an organisation
• understand how process mining impact may differ based on different contextual elements

PhD Student and Supervisory Team

Azumah Mamudu PhD student Prof. Moe Wynn Principal supervisor Assoc. Prof. Wasana Bandera Dr Sander Leemans

Project Outcomes

The primary artefact for this research will include an empirically validated process mining impacts framework that captures the key dimensions and sub-elements of process mining impact. An extended version of this framework will also capture process mining success factors and guiding principles for applying the proposed framework.

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Process mining in flexible environments

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Research output : Thesis › Phd Thesis 1 (Research TU/e / Graduation TU/e)

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• Process Mining Computer Science 100%
• Event Computer Science 100%
• Model Computer Science 41%
• Algorithms Computer Science 19%
• Process Mining Technique Computer Science 16%
• Information System Computer Science 16%
• Transformations Computer Science 12%
• Log Data Computer Science 12%

T1 - Process mining in flexible environments

AU - Günther, C.W.

N1 - Proefschrift.

N2 - Processes are an integral part of nearly all organizations, driving their daily operations and support activities. Increasingly, these business processes are supported by some information system, e.g. Workflow Management Systems (WfMSs), or Enterprise Resource Planning (ERP) systems. Once a process is supported by an information system, it becomes possible to observe and record its execution, in the form of event logs. The field of process mining is concerned with the analysis of event log data from a process perspective. Process mining techniques can aim at the discovery of process models, e.g. there are process mining techniques that can extract a Petri net of the control flow of a process, or a social network describing the handover of work among people involved in this process. Process mining also includes analyzing conformance, i.e. how well a previously available process model describes the actual observations in the event log. Finally, process mining can be used for extension, i.e. to augment previously available models with additional information, as extracted from event logs. This thesis presents our research into the application of process mining in the context of flexible environments. Thus, in contrast to traditional approaches, which expect processes to be well-structured, limited in scope, and tightly controlled by an information system, our research aims to extend the applicability of process mining to a considerably wider range of processes. Many information systems for supporting processes nowadays allow their users to deviate significantly from prescribed process definitions, or to change these on the fly. Furthermore, there are many processes which are not strictly enforced, but merely observed by an information system. Finally, every sufficiently complex activity or piece of machinery implements a process, be it explicitly encoded or prescribed, or emerging implicitly from patterns of use or external constraints. The objective of our research is to develop process mining techniques that are suitable for the analysis also of these less-structured, flexible processes. In this context, the results of process mining have the potential to be far more useful and beneficial, since the actual behavior of flexible processes is often not well-understood in practice, but captured in event logs. Event logs are the general starting point for any process mining analysis. We present a comprehensive methodological framework for handling and analyzing event logs in the context of process mining. Our general process and event log taxonomy provides a reference to better align event logs from diverse processes to the generic, fixed expectations of process mining algorithms. We also provide a set of structural log metrics that can be used to obtain an abstract characterization of an event log. Further, this thesis presents fundamental guidelines for the elicitation of event logs from existing sources, and for their transformation, so that they fit our general taxonomy and meta-model. These guidelines are complemented by an architecture of a generic framework for log elicitation and transformation. To create suitable data for testing a process mining approach, we present a framework for artificial log synthesis, which enables the creation of event logs under controlled conditions. The storage and management of realistically-sized event log data is a non-trivial task, typically facing all kinds of performance problems due to logs that may contain terabytes of event data. Since the performance, and thus the applicability, of process mining algorithms is directly related to this problem, we have designed a framework for the efficient storage and management of event log data. Another methodological contribution of this thesis is our analysis of the problems typically faced when applying process mining to flexible environments. When event logs from flexible processes are analyzed by traditional process mining algorithms, they typically yield large, highly unstructured, and essentially useless "spaghetti" models. We have traced these undesirable results back to a number of implicitlyheld assumptions. Most traditional approaches either assume noise-free event logs, or regard noise only as the result of errors in the logging functionality. We extend this notion of noise with other, commonly-found artifact types. Another assumption of traditional approaches is that mining results should strive for precision. We can discriminate precision of behavior and precision of scope, which both result in large and overly complex process models. Further, we have identified an attitude of entitlement in traditional approaches, which manifests in their singularity (i.e., only one singular mining result), their immutability (i.e., static results which cannot be modified), and their non-interactivity (i.e., no ability to focus and explore the result). Finally, the purity of traditional approaches, i.e. their sole reliance on traditional process representations, fails to communicate mining results in an appropriately efficient manner. Based on our methodological analysis of real-life event logs, and the problems faced by process mining in flexible environments, we have developed a number of approaches. An event log that has been extracted from a source system, and has properly been transformed to the general taxonomy and meta-model, can be analyzed in a number of ways. We introduce a set of techniques for event log schema transformation, which aims to re-align the information found in event logs for specific analysis purposes. Event class projection is a straightforward technique to cluster subsequences of low-level events into higher-level entities, such that they better correspond to the perceived process. While event class projection relies on explicit mappings, another approach, trace segmentation, can discover coherent subsequences of lower-level events automatically. These subsequences can either be collapsed into higher-level events (activity discovery), or they can be regarded as traces of an implicitly-contained subprocess (trace discovery). We introduce both a local (i.e., bottom-up) and a global (i.e., top-down) approach for trace segmentation. Another technique is process type discovery, which can discover tacit process types from a set of traces, by clustering these into more homogeneous subsets. A notable strength of event log schema transformation approaches is that they can be applied independently of the analysis goal and method, and thus leverage existing and future process mining techniques. One major contribution of this thesis is the approach of adaptive process simplification, which is directly based on the problems identified with traditional algorithms. This approach explicitly abandons the goals of precision and entitlement, and also introduces a novel type of interactive result visualization, which departs from the purity identified in earlier approaches. Adaptive process simplification has been inspired by (road) maps, as simple and intuitive representations of large, complex topologies.We have identified a number of concepts and visual metaphors from maps, which can be adapted for the description of flexible processes. Another novelty of this approach is that the event log is analyzed from multiple perspectives, as opposed to only focusing on the sequence of event names. From this extensive information we derive the significance and correlation metrics, which more appropriately describe the observed behavior. For representing mining results, we introduce fuzzy models, whose relaxed executional semantics allow us to describe complex behavior in a compact fashion. A set of visual metaphors derived from maps is used to increase the density of information in fuzzy models. Our algorithm for adaptive graph visualization can be used to derive a fuzzy model from the significance and correlation metrics, on an arbitrary level of abstraction. Therefore, the user can generate a map of the observed process, which can be as complex or as compact as desired. To evaluate the usefulness of fuzzy models, we introduce two quality and authority metrics, namely detail and conformance. These metrics provide the analyst with a quick and reliable feedback, indicating how representative the current model is with respect to the actual, observed behavior. In order to be able to leverage the results of adaptive process simplification also with other analysis methods, we show how fuzzy models can be converted into other modeling formalisms. Also we show how these models can be projected onto a log, simplifying its events onto the current level of abstraction. We argue that a large part of the problems experienced by process mining in flexible environments is not only due to algorithms, or the intelligence of the analysis, but that results are not communicated efficiently. The presentation of complex knowledge, most importantly its visualization, has a large impact on the understandability and clarity of analysis tools. We introduce two approaches for process mining analysis, relying on new ways of information visualization. For the efficient exploration and characterization of event log data, we have applied and adapted the dotplot visualization, as known from bioinformatics. In a second approach, we introduce fuzzy model animation, which projects the behavior of a process over time onto a static process model, thereby making it more intuitive to understand and analyze. The work presented in this thesis is supported and accompanied by concrete implementations, which have been integrated in the ProM and ProMimport frame works. These implementations have been crucial in enabling a number of real-life case studies with major corporations, of which four are discussed in this thesis. The results presented in this thesis have been presented in more than ten peer-reviewed scientific publications. Furthermore, the process mining techniques developed in the context of this thesis have been adopted by, and are actively used in, a number of large commercial enterprises.

AB - Processes are an integral part of nearly all organizations, driving their daily operations and support activities. Increasingly, these business processes are supported by some information system, e.g. Workflow Management Systems (WfMSs), or Enterprise Resource Planning (ERP) systems. Once a process is supported by an information system, it becomes possible to observe and record its execution, in the form of event logs. The field of process mining is concerned with the analysis of event log data from a process perspective. Process mining techniques can aim at the discovery of process models, e.g. there are process mining techniques that can extract a Petri net of the control flow of a process, or a social network describing the handover of work among people involved in this process. Process mining also includes analyzing conformance, i.e. how well a previously available process model describes the actual observations in the event log. Finally, process mining can be used for extension, i.e. to augment previously available models with additional information, as extracted from event logs. This thesis presents our research into the application of process mining in the context of flexible environments. Thus, in contrast to traditional approaches, which expect processes to be well-structured, limited in scope, and tightly controlled by an information system, our research aims to extend the applicability of process mining to a considerably wider range of processes. Many information systems for supporting processes nowadays allow their users to deviate significantly from prescribed process definitions, or to change these on the fly. Furthermore, there are many processes which are not strictly enforced, but merely observed by an information system. Finally, every sufficiently complex activity or piece of machinery implements a process, be it explicitly encoded or prescribed, or emerging implicitly from patterns of use or external constraints. The objective of our research is to develop process mining techniques that are suitable for the analysis also of these less-structured, flexible processes. In this context, the results of process mining have the potential to be far more useful and beneficial, since the actual behavior of flexible processes is often not well-understood in practice, but captured in event logs. Event logs are the general starting point for any process mining analysis. We present a comprehensive methodological framework for handling and analyzing event logs in the context of process mining. Our general process and event log taxonomy provides a reference to better align event logs from diverse processes to the generic, fixed expectations of process mining algorithms. We also provide a set of structural log metrics that can be used to obtain an abstract characterization of an event log. Further, this thesis presents fundamental guidelines for the elicitation of event logs from existing sources, and for their transformation, so that they fit our general taxonomy and meta-model. These guidelines are complemented by an architecture of a generic framework for log elicitation and transformation. To create suitable data for testing a process mining approach, we present a framework for artificial log synthesis, which enables the creation of event logs under controlled conditions. The storage and management of realistically-sized event log data is a non-trivial task, typically facing all kinds of performance problems due to logs that may contain terabytes of event data. Since the performance, and thus the applicability, of process mining algorithms is directly related to this problem, we have designed a framework for the efficient storage and management of event log data. Another methodological contribution of this thesis is our analysis of the problems typically faced when applying process mining to flexible environments. When event logs from flexible processes are analyzed by traditional process mining algorithms, they typically yield large, highly unstructured, and essentially useless "spaghetti" models. We have traced these undesirable results back to a number of implicitlyheld assumptions. Most traditional approaches either assume noise-free event logs, or regard noise only as the result of errors in the logging functionality. We extend this notion of noise with other, commonly-found artifact types. Another assumption of traditional approaches is that mining results should strive for precision. We can discriminate precision of behavior and precision of scope, which both result in large and overly complex process models. Further, we have identified an attitude of entitlement in traditional approaches, which manifests in their singularity (i.e., only one singular mining result), their immutability (i.e., static results which cannot be modified), and their non-interactivity (i.e., no ability to focus and explore the result). Finally, the purity of traditional approaches, i.e. their sole reliance on traditional process representations, fails to communicate mining results in an appropriately efficient manner. Based on our methodological analysis of real-life event logs, and the problems faced by process mining in flexible environments, we have developed a number of approaches. An event log that has been extracted from a source system, and has properly been transformed to the general taxonomy and meta-model, can be analyzed in a number of ways. We introduce a set of techniques for event log schema transformation, which aims to re-align the information found in event logs for specific analysis purposes. Event class projection is a straightforward technique to cluster subsequences of low-level events into higher-level entities, such that they better correspond to the perceived process. While event class projection relies on explicit mappings, another approach, trace segmentation, can discover coherent subsequences of lower-level events automatically. These subsequences can either be collapsed into higher-level events (activity discovery), or they can be regarded as traces of an implicitly-contained subprocess (trace discovery). We introduce both a local (i.e., bottom-up) and a global (i.e., top-down) approach for trace segmentation. Another technique is process type discovery, which can discover tacit process types from a set of traces, by clustering these into more homogeneous subsets. A notable strength of event log schema transformation approaches is that they can be applied independently of the analysis goal and method, and thus leverage existing and future process mining techniques. One major contribution of this thesis is the approach of adaptive process simplification, which is directly based on the problems identified with traditional algorithms. This approach explicitly abandons the goals of precision and entitlement, and also introduces a novel type of interactive result visualization, which departs from the purity identified in earlier approaches. Adaptive process simplification has been inspired by (road) maps, as simple and intuitive representations of large, complex topologies.We have identified a number of concepts and visual metaphors from maps, which can be adapted for the description of flexible processes. Another novelty of this approach is that the event log is analyzed from multiple perspectives, as opposed to only focusing on the sequence of event names. From this extensive information we derive the significance and correlation metrics, which more appropriately describe the observed behavior. For representing mining results, we introduce fuzzy models, whose relaxed executional semantics allow us to describe complex behavior in a compact fashion. A set of visual metaphors derived from maps is used to increase the density of information in fuzzy models. Our algorithm for adaptive graph visualization can be used to derive a fuzzy model from the significance and correlation metrics, on an arbitrary level of abstraction. Therefore, the user can generate a map of the observed process, which can be as complex or as compact as desired. To evaluate the usefulness of fuzzy models, we introduce two quality and authority metrics, namely detail and conformance. These metrics provide the analyst with a quick and reliable feedback, indicating how representative the current model is with respect to the actual, observed behavior. In order to be able to leverage the results of adaptive process simplification also with other analysis methods, we show how fuzzy models can be converted into other modeling formalisms. Also we show how these models can be projected onto a log, simplifying its events onto the current level of abstraction. We argue that a large part of the problems experienced by process mining in flexible environments is not only due to algorithms, or the intelligence of the analysis, but that results are not communicated efficiently. The presentation of complex knowledge, most importantly its visualization, has a large impact on the understandability and clarity of analysis tools. We introduce two approaches for process mining analysis, relying on new ways of information visualization. For the efficient exploration and characterization of event log data, we have applied and adapted the dotplot visualization, as known from bioinformatics. In a second approach, we introduce fuzzy model animation, which projects the behavior of a process over time onto a static process model, thereby making it more intuitive to understand and analyze. The work presented in this thesis is supported and accompanied by concrete implementations, which have been integrated in the ProM and ProMimport frame works. These implementations have been crucial in enabling a number of real-life case studies with major corporations, of which four are discussed in this thesis. The results presented in this thesis have been presented in more than ten peer-reviewed scientific publications. Furthermore, the process mining techniques developed in the context of this thesis have been adopted by, and are actively used in, a number of large commercial enterprises.

U2 - 10.6100/IR644335

DO - 10.6100/IR644335

M3 - Phd Thesis 1 (Research TU/e / Graduation TU/e)

SN - 978-90-386-1964-4

T3 - Beta dissertations

PB - Technische Universiteit Eindhoven

CY - Eindhoven

Improving PhD Student Journeys with Process Mining: Insights from a Higher Education Institution

Goel, Kanika , Leemans, Sander , Wynn, Moe Thandar , ter Hofstede, Arthur , & Barnes, Janne (2021) Improving PhD Student Journeys with Process Mining: Insights from a Higher Education Institution. In de Leoni, Massimiliano , Song, Minseok , & Roeglinger, Maximilian (Eds.) Proceedings of the Industry Forum (BPM IF 2021) co-located with 19th International Conference on Business Process Management (BPM 2021). Sun SITE Central Europe (CEUR), Germany, pp. 39-49.

Open access copy at publisher website

Description

The socioeconomic consequences of not successfully completing PhD studies have motivated universities to expend dedicated efforts on improving student journeys. These journeys leave traces in a variety of university IT systems and this trace data can be exploited to derive insights through the application of process mining. Process mining is a form of data-driven process analytics, where process data, collated from different IT systems, is analysed to uncover the real behaviour and performance of processes. Despite its potential application, process mining hitherto has not been applied to visualise, analyse, and improve PhD student journeys, to the best of our knowledge. This paper reports on the findings of a process mining case study conducted at an Australian University that had espoused a digital transformation initiative to improve PhD student journeys. The case study utilised interactive and comparative process mining techniques and focused on clarifying the way a PhD student journey eventuates, visualising the differences between the real (actual) and prescribed (recommended) processes, comparing the performance of different cohorts, identifying root causes for adverse outcomes, and providing evidence-based recommendations for the digital transformation initiative. The findings from this study resulted in restructuring of HDR services and the introduction of a new research management system.

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Taulli, T. (2020). Process Mining. In: The Robotic Process Automation Handbook. Apress, Berkeley, CA. https://doi.org/10.1007/978-1-4842-5729-6_12

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Process Mining Conference 2024

6th International Conference on Process Mining, October 14-18, 2024

Call for Research Papers

The International Conference on Process Mining (ICPM) is the premium forum for researchers, practitioners, and developers in process mining. The objective is to explore and exchange knowledge in this field through scientific talks, industry discussions, contests, technical tutorials, and panels. The conference covers all aspects of process mining research and practice, including theory, algorithmic challenges, applications, and connections with other fields.

The conference is technically co-sponsored by the IEEE Computational Intelligence Society and supported by the IEEE Task Force on Process Mining.

Process mining is an innovative research field that focuses on extracting business process insights from transactional data commonly recorded by IT systems, with the ultimate goal of analyzing and improving organizational productivity along performance dimensions such as efficiency, quality, compliance, and risk. By relying on data rather than perceptions gained from interviews and workshops, process mining shifts the way of thinking from “confidence-based” to “evidence-based” business process management. Thus, process mining distinguishes itself within the information systems domain by its fundamental focus on understanding, analyzing, and improving business processes based on process data.

Current process mining challenges include scalability, i.e., dealing with volume, velocity, veracity, and variability of input data, especially in real-time/online settings using event streams; approximation, i.e., balancing computation time with accuracy; understandability and explainability, i.e., providing easy-to-understand and explainable analytics; multi-perspective analysis, i.e., considering data, resources and time beyond the process control flow; measurability, e.g., providing a comprehensive framework for measuring differences between observed and modelled process behavior, and ethical and confidential aspects of process mining, i.e., how to ensure that process mining procedures and results do not violate ethical and privacy principles.

Topics for Research Papers

ICPM 2024 encourages papers on new methodologies, techniques, and applications for process mining, as well as case studies coming from industrial scenarios. Also, papers describing novel tools, fundamental research, and empirical studies on process mining are expected. For the sake of replicability of the presented studies, the addition of supplementary resources is strongly encouraged, such as used datasets, publicly accessible implementations of new techniques, and experimental packages for empirical studies. The use of novel, previously unpublished datasets is most welcome. Research on existing datasets must clearly showcase the novelty or unprecedented results of the applied analysis.

Selected, accepted research papers will be considered for publication in an extended and revised form in a special issue of the flagship journal Process Science, edited by Springer ( https://link.springer.com/journal/44311 ).

The thematic areas in which contributions are sought include, but are not limited to, those listed below.

Process mining techniques

• Automated Discovery of Process Models
• Conformance/Compliance Analysis
• Construction of Event Logs
• Event Log Quality Improvement
• Decision Mining for Processes
• Rule/constraint-based Process Mining
• Mining from non-process-aware systems
• Analyzing Event Streams
• Object-centric and Multi-instance Process Mining
• Data-centric Process Mining
• Multi-perspective Process Mining
• Simulation/optimization for Process Mining
• Predictive Process Analytics
• Prescriptive Process Analytics and Recommender Systems
• Responsible Process Mining
• Privacy-preserving Process Mining
• Process Model Repair
• Process Performance Mining
• Variants/deviance Analysis and Root-cause Analysis
• Visual Process Analytics
• Process Monitoring
• Process Querying and Repositories

Process mining fundamental research

• Formal Foundations of Process Mining
• Comparative and Benchmark Studies on Process Mining
• Conceptual Models Related to Process Mining
• Human-centered Studies on Process Mining
• Process Mining Quality Measures
• Process Mining Guidelines

Process mining applications and case studies in

• Artificial Intelligence
• Blockchain Technologies
• Robotic Process Automation (RPA)
• Chatbots for Process Mining
• Business Activity Monitoring and Business Intelligence
• (Cyber) Security and Privacy
• Operations Management and Lean Six Sigma
• Process Performance Measurement
• Process Reengineering
• Resource Management
• Risk Management
• Sensors, Internet-of-Things (IoT) and Wearable Devices
• Industry 4.0
• Specific domains (such as accounting, finance, government, healthcare, manufacturing, education)

Diversity, Equity, and Inclusion

The Process Mining community welcomes the advancement of diversity, equity, and inclusion (DEI) across our professional endeavors. We celebrate the diversity in our community and foster an environment that welcomes individuals irrespective of age, gender identity, race, ethnicity, socioeconomic status, nationality, beliefs, sexual orientation, physical capabilities, education, and professional background. We urge all participants to uphold DEI principles in their written work, reviews, presentations, and any engagement linked to the ICPM conference.

Open Science Principles

The ICPM conference encourages authors of research papers to follow the principles of transparency, reproducibility, and replicability. In particular, the conference supports the adoption of open data and open source principles and encourages authors to disclose (anonymized and curated) data in order to increase reproducibility and replicability.

Authors are encouraged to make research artefacts (e.g., prototypes, interview protocols, questionnaires) or the datasets (used in, or produced by, the empirical evaluation) reported in the paper available in a suitable form. To facilitate this, we kindly ask authors to include links in their manuscripts to private or public repositories where reviewers can access the associated research artefacts. This information may be presented in a dedicated section, such as  “Data availability” or “Reproducibility”. This requirement does not apply to papers that neither involve an empirical study nor a prototype implementation.

Authors who are unable or choose not to share their research artefacts and datasets with the program committee are encouraged to provide an explanation within their submitted manuscript, detailing the reasons behind their decision. This statement may be removed from the final version of the paper if it gets accepted. Possible reasons may involve privacy restrictions or non-disclosure agreements. While sharing research artefacts is not mandatory for submission or acceptance, the program committee members may use this information to inform their decision.

To enhance the accessibility of research artefacts and datasets, authors are advised to make them accessible via public repositories (e.g., Zenodo, Figshare, GitHub, or institutional archives) under an open data license such as the CC0 dedication or the CC-BY 4.0 license. Making research artefacts and datasets available via cloud services such as Dropbox or Google Docs is discouraged due to the volatility of the links associated with these services.

Finally, authors are encouraged to self-archive their pre- and post-prints in open, preserved repositories, such as their institutional preprint repository, arXiv, or other non-profit services, in line with IEEE’s copyright agreement (see IEEE Preprint Policy ).

Submission Instructions

Submissions must be original contributions that have not been published previously, nor submitted elsewhere while being submitted to ICPM 2024. All files must be prepared using the latest IEEE Computational Intelligence Society conference proceedings guidelines (8.5′′ × 11′′ two-column format). The page limit is set to 8 pages (IEEE Format). All papers must be in English. The use of artificial intelligence (AI)–generated text in an article shall be disclosed in the acknowledgments section of any paper submitted to an IEEE Conference or Periodical. The sections of the paper that use AI-generated text shall have a citation to the AI system used to generate the text.

Templates are available for LaTeX and Word at the following link: https://www.ieee.org/conferences/publishing/templates.html .

The paper should be submitted through the ICPM 2024 submission system, which is reachable at https://easychair.org/conferences/?conf=icpm2024 where one should select “ICPM 2024”.

Each paper will be reviewed by at least 3 program committee members. Afterwards, there will be a discussion period to finalize the decisions.

At least one author of each accepted contribution is expected to register for the conference to present the paper and sign a copyright release form.

Important dates

• Abstract submission : May 23, 2024 May 30, 2024 (AOE)
• Paper submission: May 30, 2024 June 6, 2024 (AOE)
• Notification : July 16, 2024
• Camera-ready : August 23, 2024
• Conference start : October 14, 2024

Program chairs

• Xixi Lu , University of Utrecht, Netherlands
• Luise Pufahl , Technical University of Munich, Germany
• Minseok Song , Pohang University of Science and Technology, South Korea

Program committee

• Daniel Amyot , University of Ottawa, Canada
• Michael Arias , Universidad de Costa Rica, Costa Rica
• Abel Armas Cervantes , The University of Melbourne, Australia
• Ahmed Awad , The British University in Dubai, UAE
• Hyerim Bae , Pusan National University, South Korean
• Iris Beerepoot , Utrecht University, Netherlands
• Robin Bergenthum , Fern Universität in Hagen, Germany
• Andrea Burattin , Technical University of Denmark, Denmark
• Cristina Cabanillas , University of Seville, Spain
• Paolo Ceravolo , University of Milan, Italy
• Thomas Chatain , LSV, ENS Paris-Saclay, Cachan, France
• Minsu Cho , Kwangwoon University, South Korea
• Ouyang Chun , Queensland University of Technology, Australia
• Marco Comuzzi , Ulsan National Institute of Science and Technology, South Korea
• Benjamin Dalmas , Centre de Recherche Informatique de Montreal, Canada
• Zahra Dasht Bozorgi , University of Melbourne, Australia
• Massimiliano de Leoni , University of Padua, Italy
• Johannes De Smedt , KU Leuven, Belgium
• Jochen De Weerdt , KU Leuven, Belgium
• Andrea Delgado , Universidad de la República, Uruguay
• Pavlos Delias , International Hellenic University, Greece
• Benoit Depaire , Hasselt University, Belgium
• Claudio Di Ciccio , Sapienza University of Rome, Italy
• Chiara Di Francescomarino , DISI – University of Trento, Italy
• Marlon Dumas , University of Tartu, Estonia
• Joerg Evermann , Memorial University of Newfoundland, Canada
• Dirk Fahland , Eindhoven University of Technology, Netherlands
• Stephan Fahrenkrog-Petersen , Weizenbaum Institute for the Networked Society, Germany
• Carlos Fernandez-Llatas , Universitat Politècnica de València, Spain
• Francesco Folino , ICAR-CNR, Italy
• Walid Gaaloul , Computer Science Department Telecom SudParis, France
• Avigdor Gal , Technion, Israel
• Luciano García-Bañuelos , Tecnológico de Monterrey, Mexico
• Laura Genga , Eindhoven University of Technology, Netherlands
• Alessandro Gianola , University of Lisbon, Portugal
• Oscar Gonzalez-Rojas , Universidad de los Andes, Colombia
• Gianluigi Greco , University of Calabria, Italy
• Daniela Grigori , Laboratoire LAMSADE, University Paris-Dauphine, France
• Antonella Guzzo , University of Calabria, Italy
• Marwan Hassani , Eindhoven University of Technology, Netherlands
• Mieke Jans , Hasselt University, Belgium
• Gert Janssenswillen , Hasselt University, Belgium
• Anna Kalenkova , The University of Adelaide, Australia
• Agnes Koschmider , Kiel University, Germany
• Sander J.J. Leemans , RWTH Aachen, Germany
• Henrik Leopold , Kühne Logistics University, Germany
• Francesco Leotta , Sapienza University of Rome, Italy
• Cong Liu , Shandong University of Technology, China
• Irina Lomazova , National Research University Higher School of Economics, Russia
• Orlenys Lopez-Pintado , University of Tartu, Estonia
• Felix Mannhardt , Eindhoven University of Technology, Netherlands
• Andrea Marrella , Sapienza University of Rome, Italy
• Fabrizio Maria Maggi , Free University of Bozen-Bolzano, Italy
• Niels Martin , Hasselt University, Belgium
• Raimundas Matulevicius , University of Tartu, Estonia
• Massimo Mecella , Sapienza University of Rome, Italy
• Jan Mendling , Humboldt-Universität zu Berlin, Germany
• Giovanni Meroni , Technical University of Denmark, Denmark
• Marco Montali , Free University of Bozen-Bolzano, Italy
• Jorge Munoz-Gama , Pontificia Universidad Católica de Chile, Chile
• Artem Polyvyanyy , University of Melbourne, Australia
• Luigi Pontieri , ICAR, National Research Council of Italy (CNR), Italy
• Mahsa Pourbafrani , RWTH Aachen University, Germany
• Jana-Rebecca Rehse , University of Mannheim, Germany
• Hajo A. Reijers , Utrecht University, Netherlands
• Manuel Resinas , University of Seville, Spain
• Stefanie Rinderle-Ma , Technical University of Munich, Germany
• Andrey Rivkin , Technical University of Denmark, Denmark
• Massimiliano Ronzani , Fondazione Bruno Kessler, Italy
• Lorenzo Rossi , University of Camerino, Italy
• Maximilian Röglinger , University of Bayreuth, Germany
• Arik Senderovich , York University, Canada
• Marcos Sepùlveda , Pontificia Universidad Católica de Chile, Chile
• Tijs Slaats , University of Copenhagen, Denmark
• Pnina Soffer , University of Haifa, Israel
• Arthur ter Hofstede , Queensland University of Technology, Australia
• Han van der Aa , University of Vienna, Austria
• Wil van der Aalst , RWTH Aachen University, Germany
• Andrea Vandin , Scuola Superiore Sant’Anna, Italy
• Boudewijn van Dongen , Eindhoven University of Technology, Netherlands
• Sebastiaan van Zelst , Celonis, Germany
• Eric Verbeek , Eindhoven University of Technology, Netherlands
• Barbara Weber , University of St.Gallen, Switzerland
• Matthias Weidlich , Humboldt-Universität zu Berlin, Germany
• Lijie Wen , Tsinghua University, China
• Karolin Winter , Eindhoven University of Technology, Netherlands
• Moe Thandar Wynn , Queensland University of Technology, Australia
• Francesca Zerbato , Eindhoven University of Technology, Netherlands

• ICPM 2024 Workshop list announced!

• Special Issue: Process mining meets visual analytics (Information Systems, Elsevier)

• ICPM 2024: Several calls for contributions out (papers, workshops, demos, discovery algorithms)

More and more people, both in industry and academia, consider process mining (see the promotional video for an introduction) as one of the most important innovations in the field of business process management. It joins ideas of process modeling and analysis on the one hand and data mining and machine learning on the other. Therefore, the IEEE has established a Task Force on Process Mining . This Task Force is established in the context of the Data Mining Technical Committee (DMTC) of the Computational Intelligence Society (CIS) of the Institute of Electrical and Electronic Engineers, Inc. (IEEE) .

The goal of this Task Force is to promote the research, development, education and understanding of process mining. More concretely, the goal is to:

• make end-users, developers, consultants, and researchers aware of the state-of-the-art in process mining,
• promote the use of process mining techniques and tools and stimulating new applications,
• play a role in standardization efforts for logging event data,
• the organization of tutorials, special sessions, workshops, panels,
• the organization of Conferences/Workshop with IEEE CIS Technical Co-Sponsorship, and
• publications in the form of special issues in journals, books, articles (e.g., in the IEEE Computational Intelligence Magazine).

Note that process mining includes (automated) process discovery (extracting process models from an event log), conformance checking (monitoring deviations by comparing model and log), social network/organizational mining , automated construction of simulation models , case prediction , and history-based recommendations .

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Process Mining Summer School

Aachen, 4-8 July 2022

The first official Process Mining Summer School has concluded.

The lectures of the summer school have been recorded, and are now available freely on youtube , the process mining handbook collects the lectures in a more detailed technical way, and allow you to dive deep into state-of-the-art process mining topics. out with open access.

The first Summer School on Process Mining organized by the IEEE Task Force on Process Mining took place in Aachen, Germany from 4 to 8 July 2022 . 130 participants and 20 speakers from all over the world gathered in Aachen.

The course was given by renowned experts in the field. Wil van der Aalst and Josep Carmona are the course directors, and the event is supported by the Alexander von Humboldt Foundation and RWTH Aachen University. It took place in the Super C , one of the most modern and representative buildings of RWTH Aachen University.

Young researchers (such as PhD students and Postdocs) are the primary target audience, but the school and resulting materials will also be very interesting for practitioners, students, and senior researchers. The summer school has introductory lectures and lectures focusing on applications in healthcare, auditing, and robotic process automation. Moreover, different process discovery and conformance checking techniques are presented in detail. Lectures on data preprocessing, data quality, performance analysis, predictive analytics, and responsible process mining complete the picture. Next to the lectures, there’s also hands-on sessions with (open-source) process mining tools.

The first iteration of the Process Mining Summer School will take place in the beautiful setting of the Super C , established within RWTH Aachen University , in North Rhine-Westphalia.

This building, home of the RWTH Student Services, is the beating heart of the academic life in Aachen. Situated in the city center, the Super C offers a splendid view of the skyline of the city and its many historical landmarks. Powered by a 2500 meters deep geothermal borehole, the Super C connects RWTH’s past innovations in mining engineering with state-of-the-art green energy technologies.

For more information on the Super C, please refer to the official website .

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Process Mining in Education: Use cases, Pros & Cons in 2024

Covid-19 enforced countries to adopt online or hybrid learning in order to catch up to expected learning targets. Yet, many countries remain inefficient at moving to online or hybrid education. Also, though some countries manage to boost students progress (like Italy increased their progress with online tutoring by 4.7 % compared to traditional schooling), some others fail to generate the same outcome from the online learning. However, recently, education industry leaders have started identifying use cases of process mining to improve online learning platforms, teaching methodologies and learning habits of students.

In this article, we explain what is educational process mining, what are the use cases, benefits and challenges of applying process mining to educational domains.

What is educational process mining?

Educational process mining (EPM) is the practice of leveraging tools to discover insights in educational data which comes from various sources and is stored in different formats. Today, with the increasing use of information communication technology (ICT) in education, online learning solutions have gained popularity, generating large data volumes.

Educational data contains information on educational processes consisting from organizational and didactic measures. This data provides information about the educational performance based on the state standards of education.

For example, educational data can include information about a student-related processes such as:

• Educational materials student access overtime on a learning management system (LMS) (e.g. topic, resources)
• Student interactions with the educational platform overtime (e.g. login, logout, video, test)
• Student’s process of problem-solving (e.g. use of a calculator, answer elimination, resetting question)

Figure 1 below shows the general overview of educational process mining.

What is the difference between educational process mining and educational data mining?

Educational process mining (EPM) falls under educational data mining (EDM) to obtain insights from educational data to find out patterns and relationship. However, EDM is not process-centric and it does not focus on event data as EPM does. It implements classical data mining techniques (e.g. classification, clustering, regression) which does not help mapping control-flows and processes. Process mining meets this gap by providing visual representations of the educational processes.

What are the use cases of educational process mining?

Some of the applications of educational process mining in the academic literature includes:

Improving online learning platforms

Process mining can discover UI navigation processes on learning platforms. This data can be leveraged by platform developers to improve user experience accordingly. For example, if a certain amount of students log out after a series of events, it indicates a pattern of dissatisfaction. Process mining can help users understand the pitfalls (e.g. examples videos longer than 20 min, lack of playback speed, unorganized course materials) in the system and underlying events in order to improve user experience on the platform.

Finding effective learning processes

Process mining can be applied to understand students’ learning processes. For instance, by separating students based on their performance and growth measures, learning processes that bring higher output can be detected and recommended to other students as well. Such personalized recommendations might help improve students’ learning efficiency and programs’ effectiveness.

In a case study conducted at a university in Thailand , researchers leveraged process mining to compare low and high performance students’ study behaviors. The study finds that high performance students study in smaller teams (2-4 people) and they have less loopbacks and bottlenecks than the low performance students. Low performance students are reported as prolonging to move onto a new subject.  

Improving problem solving 

Educational process mining can help discover and improve problem solving skills of students. Problem solving data can include the steps taken until the task is done and which tools used during the process (e.g. calculator, google). Based on findings, students can be encouraged to follow the more beneficial strategies.

For example, in a case study published by OECD , computer-based assessment data is discovered by using process mining techniques. Researches analyzes the human-computer interaction to find out if the problem-solving strategies of students who received most correct answers in the given tasks. They found out that among three student clusters, those who checked all the sources efficiently are the most successful ones.

Improving higher degree research / PhD students journey

By implementing educational process mining, universities can improve student-supervisor experience which can lead to restructuring and standardizing high degree research (HDR) services. Across the world, PhD candidates are expected to complete their studies in 3-5 years. Yet, PhD students have high rates of withdrawal since PhD journey is quite unstructured process . The reasons behind the unstructured nature of PhD journey includes varied number of activities, research uncertainties and a long time period.

For instance, in a case study at the Queensland University of Technology in Australia , researchers leveraged process mining tools to examine the reasons why PhD students withdraw by analyzing the educational process data of PhD student journey. The main goal of this enterprise project was the improving PhD journey experiences and increasing efficiency of research management.

What are the benefits of educational process mining?

Some benefits of exploring educational data with process mining includes:

• Learning habits at personalized level
• Performance KPIs
• Skills students gain with education
• Management of learning objects
• Providing advice for students and teachers.
• Optimizing educational materials and curriculum.

What are the challenges of implementing educational process mining?

Some of the challenges researches experience when they implement process mining in educational domains list as:

Data volume

Challenge: Event logs might contain massive amounts of fine granular events in educational data. Fine-grained events are expressive events with detailed information. One way to overcome the fine granular events issue is to break these large event logs into smaller sections and then cluster them to analyze. 

Solution: Organizations can leverage machine learning clustering algorithms, such as decision trees or k-mean clustering, to create clusters of event logs which can be used as input to the process mining tool instead of the entire dataset.

Data heterogeneity & complexity

Challenge: Educational processes’ data tend to come in unstructured formats (e.g. images, videos, PDFs) with many exceptions because there is a high diversity among learning habits and paths of students. Therefore, when these processes are discovered, the models are spaghetti models, which are complex and relatively difficult to understand. 

Solution: Organizations can leverage tools to generate machine-readable data from the unstructured format, such as optical character recognition (OCR) or natural language processing (NLP) algorithms. The resulting structured data can be used as input to the process mining tool.

Further reading

To learn how more about digital transformation in education:

• 12 Digital transformation trends & use cases in education
• Top 13 Use Cases of RPA in Education
• Top 5 Use Cases of Conversational AI in Education

If you believe your business can benefit from process mining tools, you can check our data-driven list of process mining software and other automation solutions .

Check out comprehensive and constantly updated list of process mining case studies to learn more real-life examples for process mining in education.

And you can let us find you the right vendor:

Next to Read

Top uipath process mining alternatives in 2024, top 3 celonis alternatives for process mining in 2024, 6 process mining trends to watch for in 2024.

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• Doctor of Philosophy in Mining Engineering (PhD)
• Graduate School
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Canadian Immigration Updates

Applicants to Master’s and Doctoral degrees are not affected by the recently announced cap on study permits. Review more details

Go to programs search

Backed by an unparalleled reputation for expertise and innovation in mineral extraction, mineral processing and environmental protection, the graduate program in Mining Engineering has two types of students in mind:

• Those from industry who wish to improve their workplace skills; and
• Those who wish to pursue research leading to advances in state-of-the-art or state-of-the-practice mining and mineral process engineering.

In order to best meet the needs of these two groups, the program encourages interaction between universities in North America and other countries. In many cases, this collaborative outlook leads to joint research projects and student exchanges.

For specific program requirements, please refer to the departmental program website

What makes the program unique?

In keeping with the collaborative approach of the NBK Institute of Mining Engineering, one of the Department’s greatest strengths lies in its ties with Canada’s mining industry.

Most of our students have opportunities for industry employment and participation in research activity at working mines. This hands-on approach helps our students develop practical skills and gain exposure to valuable case histories. Also, many of our faculty members are active within industry through consulting activities and involvement in professional societies relating to mining.

The department provides opportunities for interdisciplinary work on social, economic as well as engineering research. Other advantages are international research and travel opportunities and connections to CIRDI. Vancouver is a centre for Mining Activity in Canada with its abundance of junior mining companies, finance for mining companies, and law for mining companies.

The end result is an innovative, industry-responsive and internationally recognized graduate program of the highest caliber.

It was a delightful surprise to know about the amount of support available for students at UBC. On several occasions, I have found out that the staff members, Faculties, and supervisors are genuinely supportive.

Durjoy Baidya

Quick Facts

Program enquiries, admission information & requirements, 1) check eligibility, minimum academic requirements.

The Faculty of Graduate and Postdoctoral Studies establishes the minimum admission requirements common to all applicants, usually a minimum overall average in the B+ range (76% at UBC). The graduate program that you are applying to may have additional requirements. Please review the specific requirements for applicants with credentials from institutions in:

• Canada or the United States
• International countries other than the United States

Each program may set higher academic minimum requirements. Please review the program website carefully to understand the program requirements. Meeting the minimum requirements does not guarantee admission as it is a competitive process.

English Language Test

Applicants from a university outside Canada in which English is not the primary language of instruction must provide results of an English language proficiency examination as part of their application. Tests must have been taken within the last 24 months at the time of submission of your application.

Minimum requirements for the two most common English language proficiency tests to apply to this program are listed below:

TOEFL: Test of English as a Foreign Language - internet-based

Overall score requirement : 90

IELTS: International English Language Testing System

Overall score requirement : 6.5

Other Test Scores

Some programs require additional test scores such as the Graduate Record Examination (GRE) or the Graduate Management Test (GMAT). The requirements for this program are:

The GRE is not required.

2) Meet Deadlines

September 2024 intake, application open date, canadian applicants, international applicants, january 2025 intake, deadline explanations.

Deadline to submit online application. No changes can be made to the application after submission.

Deadline to upload scans of official transcripts through the applicant portal in support of a submitted application. Information for accessing the applicant portal will be provided after submitting an online application for admission.

Deadline for the referees identified in the application for admission to submit references. See Letters of Reference for more information.

3) Prepare Application

Transcripts.

All applicants have to submit transcripts from all past post-secondary study. Document submission requirements depend on whether your institution of study is within Canada or outside of Canada.

Letters of Reference

A minimum of three references are required for application to graduate programs at UBC. References should be requested from individuals who are prepared to provide a report on your academic ability and qualifications.

Statement of Interest

Many programs require a statement of interest , sometimes called a "statement of intent", "description of research interests" or something similar.

Supervision

Students in research-based programs usually require a faculty member to function as their thesis supervisor. Please follow the instructions provided by each program whether applicants should contact faculty members.

Instructions regarding thesis supervisor contact for Doctor of Philosophy in Mining Engineering (PhD)

Citizenship verification.

Permanent Residents of Canada must provide a clear photocopy of both sides of the Permanent Resident card.

4) Apply Online

All applicants must complete an online application form and pay the application fee to be considered for admission to UBC.

Research Information

Research focus.

1. Mining (mine ventilation and mine services, simulation and optimization, mining operations research, rock mechanics and geotechnics, mine valuation and production economics) 2. Mineral Processing (process control, modelling, simulation and optimization, fine particle technology, surface chemistry of flotation, plant design and economics, coal preparation technology) 3. Social-economic aspects and sustainability (mine waste management, environmental aspects of mining)

Research Facilities

Our facilities are specifically designed to ensure that our faculty, staff and students are prepared to meet the demands on the mining industry. Our instructional building, the Frank Forward building and our research facility, the Coal & Mineral Processing Laboratory, are fully equipped to provide a positive research and educational framework. Much of the equipment has been obtained through the generosity of donors and the initiative of faculty who seek out and obtain research grants. As a result, the UBC Department of Mining Engineering is able to maintain its’ reputation for producing first rate mining engineers and research. In 2003 we underwent a major renovation that gave us a state-of-the-art classroom, a larger conference room, and a redesigned main office that includes more work space, quiet nooks, and a coffee room.

Tuition & Financial Support

Financial support.

Applicants to UBC have access to a variety of funding options, including merit-based (i.e. based on your academic performance) and need-based (i.e. based on your financial situation) opportunities.

Program Funding Packages

Some types of financial assistance are available for the winter session and may be supplemented by summer research and/or teaching assistantships to the registered students.

Financial support for non-Canadian students is limited and high academic standings are required to obtain support [Grade Point Averages exceeding 3.7 (maximum 4)].

We suggest that you have financial support to finance at least the first year of studies. In the event that a sponsor is willing to provide you with financial support, we will require a letter from him/her noting the amount of financial aid available and its duration.

We regret that we cannot process your application without this document. The department will not be responsible for foreign students’ financial.

The University of British Columbia may offer a Partial Tuition Scholarship up to $3,200 each year.

From September 2024 all full-time students in UBC-Vancouver PhD programs will be provided with a funding package of at least $24,000 for each of the first four years of their PhD. The funding package may consist of any combination of internal or external awards, teaching-related work, research assistantships, and graduate academic assistantships. Please note that many graduate programs provide funding packages that are substantially greater than $24,000 per year. Please check with your prospective graduate program for specific details of the funding provided to its PhD students.

Average Funding

• 8 students received Teaching Assistantships. Average TA funding based on 8 students was $3,579.
• 16 students received Research Assistantships. Average RA funding based on 16 students was $27,557.
• 4 students received Academic Assistantships. Average AA funding based on 4 students was $14,651.
• 20 students received internal awards. Average internal award funding based on 20 students was $7,649.
• 3 students received external awards. Average external award funding based on 3 students was $11,667.

Scholarships & awards (merit-based funding)

All applicants are encouraged to review the awards listing to identify potential opportunities to fund their graduate education. The database lists merit-based scholarships and awards and allows for filtering by various criteria, such as domestic vs. international or degree level.

Graduate Research Assistantships (GRA)

Many professors are able to provide Research Assistantships (GRA) from their research grants to support full-time graduate students studying under their supervision. The duties constitute part of the student's graduate degree requirements. A Graduate Research Assistantship is considered a form of fellowship for a period of graduate study and is therefore not covered by a collective agreement. Stipends vary widely, and are dependent on the field of study and the type of research grant from which the assistantship is being funded.

Graduate Teaching Assistantships (GTA)

Graduate programs may have Teaching Assistantships available for registered full-time graduate students. Full teaching assistantships involve 12 hours work per week in preparation, lecturing, or laboratory instruction although many graduate programs offer partial TA appointments at less than 12 hours per week. Teaching assistantship rates are set by collective bargaining between the University and the Teaching Assistants' Union .

Graduate Academic Assistantships (GAA)

Academic Assistantships are employment opportunities to perform work that is relevant to the university or to an individual faculty member, but not to support the student’s graduate research and thesis. Wages are considered regular earnings and when paid monthly, include vacation pay.

Financial aid (need-based funding)

Canadian and US applicants may qualify for governmental loans to finance their studies. Please review eligibility and types of loans .

All students may be able to access private sector or bank loans.

Foreign government scholarships

Many foreign governments provide support to their citizens in pursuing education abroad. International applicants should check the various governmental resources in their home country, such as the Department of Education, for available scholarships.

Working while studying

The possibility to pursue work to supplement income may depend on the demands the program has on students. It should be carefully weighed if work leads to prolonged program durations or whether work placements can be meaningfully embedded into a program.

International students enrolled as full-time students with a valid study permit can work on campus for unlimited hours and work off-campus for no more than 20 hours a week.

A good starting point to explore student jobs is the UBC Work Learn program or a Co-Op placement .

Tax credits and RRSP withdrawals

Students with taxable income in Canada may be able to claim federal or provincial tax credits.

Canadian residents with RRSP accounts may be able to use the Lifelong Learning Plan (LLP) which allows students to withdraw amounts from their registered retirement savings plan (RRSPs) to finance full-time training or education for themselves or their partner.

Please review Filing taxes in Canada on the student services website for more information.

Cost Estimator

Applicants have access to the cost estimator to develop a financial plan that takes into account various income sources and expenses.

Career Outcomes

31 students graduated between 2005 and 2013: 1 is in a non-salaried situation; for 6 we have no data (based on research conducted between Feb-May 2016). For the remaining 24 graduates:

Sample Employers in Higher Education

Sample employers outside higher education, sample job titles outside higher education, phd career outcome survey, career options.

Our graduates have gone into academic environments to become university professors and instructors or moved into industry for positions such as being a technical expert for a mining company, consulting company or supply company as well as mining industry advisors for the financial and banking sector.  

Enrolment, Duration & Other Stats

These statistics show data for the Doctor of Philosophy in Mining Engineering (PhD). Data are separated for each degree program combination. You may view data for other degree options in the respective program profile.

ENROLMENT DATA

Completion rates & times, upcoming doctoral exams, wednesday, 29 may 2024 - 1:30pm, monday, 3 june 2024 - 9:00am - 506, frank forward building, 6350 stores road.

• Research Supervisors

Advice and insights from UBC Faculty on reaching out to supervisors

These videos contain some general advice from faculty across UBC on finding and reaching out to a supervisor. They are not program specific.

This list shows faculty members with full supervisory privileges who are affiliated with this program. It is not a comprehensive list of all potential supervisors as faculty from other programs or faculty members without full supervisory privileges can request approvals to supervise graduate students in this program.

• Dunbar, W Scott (Industrial biotechnology; Economics and business administration; biotechnology applications in mineral resources; mining industry business model innovation)
• Elmo, Davide (philosophy of engineering; rock engineering; geosciences; Numerical modelling; Machine Learning)
• Holuszko, Maria (minerals characterization as it applies to mineral processing; recovery of metals from industrial and municipal waste streams)
• Klein, Bern (processing of precious minerals; processing of industrial metals, Ultrafine grinding, high pressure grinding rolls, hydraulic transport of non-Newtonion mineral slurries, industrial minerlas, mine-mill integration, continuous centrifugal gravity concentration, improved technologies for artisinal andsmall scale gold miners, metal leaching from waste rock, rheology of mineral suspensions)
• Kunz, Nadja (Mining engineering; Public administration; Public policy; Public security policy; Decision Analysis; Environmental engineering; Hydrology; Risk management; Systems engineering; water resources management)
• Madiseh, Ali (Mining engineering; Numerical modelling and mechanical characterisation; Heat and mass transfer operations; energy systems; Renewable energy systems; Energy storage; Energy decarbonization; Computational Fluid Mechanics and Heat Transfer)
• Miskovic, Sanja (Mining engineering; Mineral Processing; multiphase systems; Production and Process Optimization; Optimization, Control and Operations Research; Prefeasibility and Pilot Scale; R&D and Innovation; Technological Innovations; Sensors and Devices; Computational Fluid Dynamics; Critical Elements Extraction; Embedded Sensors; Experimental Fluid Dynamics; High performance computing; IIoT; Industrial Big Data; Minerals Processing; Multiphase Flows)
• Miskovic, Ilija (Multi-physics of Geo-materials, Big Data)
• Pawlik, Marek (Surface chemistry, Adsorption of polymers and surfactants, Process water and reagent chemistry, rheology of mineral suspensions, interparticle and interfacial phenomena)
• Steen, John Thomas (Strategy; Innovation; Network analysis; Projects; Mining industry)

Doctoral Citations

Sample thesis submissions.

• Study of fluid flow and heat transfer for carbon mineralization in mine wastes
• Improving water management in mining regions through understanding stakeholders’ views and perspectives on integrated water resources management
• Driving tailings management through interdisciplinary approaches : high-level modelling to bridge stakeholder knowledge gaps
• Analysis of coaxial borehole heat exchanger for geothermal heat and power
• An evaluation of bulk ore sorting potential in a copper-gold panel cave mine
• Numerical and experimental investigation of mine exhaust heat recovery systems
• Segregation and hindered settling behavior of mine tailings suspension
• Study of unconventional techniques to eliminate mercury use from artisanal gold mining operations
• A greenhouse study on phytostabilization of sulfidic mine tailings
• Development of awaruite flotation conditions on serpentinite ores
• Numerical and experimental study of the performance of bulk-air spray coolers and renewable cooling systems for application in mine ventilation
• Composition and structure in flocculated mineral systems
• Characterization and extraction of rare earth elements from metallurgical coal-based source

Related Programs

Same specialization.

• Master of Applied Science in Mining Engineering (MASc)
• Master of Engineering in Mining Engineering (MEng)

Further Information

Specialization.

Mining Engineering offers opportunity for study in the fields of mining and mineral processing, including mine environment and coal preparation. Areas of research interest are:

• Mining: Mine economics and valuation, mine design, drilling and blasting methods, rock mechanics and slope stability, optimization and simulation of mining operations, advanced mining methods, mine services (particularly mine ventilation), and climatic control.
• Mineral processing: Unit operations, comminution, process modelling and optimization, expert systems, instrumentation and computer control. Flotation, surface chemistry, fines recovery, coal recovery, treatment of fine and oxidized coal, and precious metals recovery.
• Mining and Environment: Acid rock drainage, environmental protection, effluent control and treatment. Social and legal aspects of sustainable mining practices, small-scale mining in developing countries.

UBC Calendar

Program website, faculty overview, academic unit, program identifier, classification, social media channels, supervisor search.

Departments/Programs may update graduate degree program details through the Faculty & Staff portal. To update contact details for application inquiries, please use this form .

Melanie Mackay

I graduated from UBC Geological Sciences and so I was aware that UBC is one of the best universities in Canada for research in geoscience and mining engineering. The smaller department sizes allow for rich collaboration among professors and graduate students.

Sally Innis

UBC has always been a part of my life. My mother worked at UBC, and all my siblings and I completed undergraduate degrees here. My mother, who always had a hoard of graduate students, taught me that a student’s relationship to their supervisor is a major component to success in graduate school. UBC...

M. Ryan MacIver

The motivation to pursue a PhD was different than the motivation to pursue a masters degree. During my masters research, I had a good experience with the subject matter and my supervisor so I decided to stay.

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Home > Statler College of Engineering and Mineral Resources > MININGENG > Mining Engineering Graduate Theses and Dissertations

Mining Engineering Graduate Theses and Dissertations

Theses/dissertations from 2024 2024.

CHARACTERIZATION AND EVALUATION OF VARIOUS BIOCHAR TYPES AS GREEN ADSORBENTS FOR RARE EARTH ELEMENT RECOVERY FROM AQUEOUS SOLUTIONS , Oluwaseun Victor Famobuwa

Selective Recovery of Various Critical Metals from Acid Mine Drainage Sludge , Gorkem Gecimli

Theses/Dissertations from 2023 2023

Development of A Hydrometallurgical Process for the Extraction of Cobalt, Manganese, and Nickel from Acid Mine Drainage Treatment Byproduct , Alejandro Agudelo Mira

Selective Recovery of Rare Earth Elements from Acid Mine Drainage Treatment Byproduct , Zeynep Cicek

Identification of Rockmass Deformation and Lithological Changes in Underground Mines by Using Slam-Based Lidar Technology , Francisco Eduardo Gil Hurtado

Analysis of the Brittle Failure Mechanism of Underground Stone Mine Pillars by Implementing Numerical Modeling in FLAC3D , Rosbel Jimenez

Analysis of the root causes of fatal injuries in the United States surface mines between 2008 and 2021. , Maria Fernanda Quintero

AUGMENTED REALITY AND MOBILE SYSTEMS FOR HEAVY EQUIPMENT OPERATORS IN SURFACE MINING , Juan David Valencia Quiceno

Theses/Dissertations from 2022 2022

Integrated Large Discontinuity Factor, Lamodel and Stability Mapping Approach for Stone Mine Pillar Stability , Mustafa Baris Ates

Noise Exposure Trends Among Violating Coal Mines, 2000 to 2021 , Hanna Grace Davis

Calcite depression in bastnaesite-calcite flotation system using organic acids , Emmy Muhoza

Investigation of Geomechanical Behavior of Laminated Rock Mass Through Experimental and Numerical Approach , Qingwen Shi

Static Liquefaction in Tailing Dams , Jose Raul Zela Concha

Experimental and Theoretical Investigation on the Initiation Mechanism of Low-Rank Coal's Self-Heating Process , Yinan Zhang

Development of an Entry-Scale Modeling Methodology to Provide Ground Reaction Curves for Longwall Gateroad Support Evaluation , Haochen Zhao

Size effect and anisotropy on the strength of shale under compressive stress conditions , Yun Zhao

Theses/Dissertations from 2021 2021

Evaluation of LIDAR systems for rock mass discontinuity identification in underground stone mines from 3D point cloud data , Mario Alejandro Bendezu de la Cruz

Implementing the Empirical Stone Mine Pillar Strength Equation into the Boundary Element Method Software LaModel , Samuel Escobar

Recovery of Phosphorus from Florida Phosphatic Waste Clay , Amir Eskanlou

Optimization of Operating Conditions and Design Parameters on Coal Ultra-Fine Grinding Through Kinetic Stirred Mill Tests and Numerical Modeling , Francisco Patino

The Effect of Natural Fractures on the Mechanical Behavior of Limestone Pillars: A Synthetic Rock Mass Approach Application , Mustafa Can Süner

Evaluation of Various Separation Techniques for the Removal of Actinides from A Rare Earth-Containing Solution Generated from Coarse Coal Refuse , Deniz Talan

Geology Oriented Loading Approach for Underground Coal Mines , Deniz Tuncay

Various Operational Aspects of the Extraction of Critical Minerals from Acid Mine Drainage and Its Treatment By-product , Zhongqing Xiao

Theses/Dissertations from 2020 2020

Adaptation of Coal Mine Floor Rating (CMFR) to Eastern U.S. Coal Mines , Sena Cicek

Upstream Tailings Dam - Liquefaction , Mladen Dragic

Development, Analysis and Case Studies of Impact Resistant Steel Sets for Underground Roof Fall Rehabilitation , Dakota D. Faulkner

The influence of spatial variance on rock strength and mechanism of failure , Danqing Gao

Fundamental Studies on the Recovery of Rare Earth Elements from Acid Mine Drainage , Xue Huang

Rational drilling control parameters to reduce respirable dust during roof bolting operations , Hua Jiang

Solutions to Some Mine Subsidence Research Challenges , Jian Yang

An Interactive Mobile Equipment Task-Training with Virtual Reality , Lazar Zujovic

Theses/Dissertations from 2019 2019

Fundamental Mechanism of Time Dependent Failure in Shale , Neel Gupta

A Critical Assessment on the Resources and Extraction of Rare Earth Elements from Acid Mine Drainage , Christopher R. Vass

Time-dependent deformation and associated failure of roof in underground mines , Yuting Xue

Theses/Dissertations from 2018 2018

Parametric Study of Coal Liberation Behavior Using Silica Grinding Media , Adewale Wasiu Adeniji

Three-dimensional Numerical Modeling Encompassing the Stability of a Vertical Gas Well Subjected to Longwall Mining Operation - A Case Study , Bonaventura Alves Mangu Bali

Shale Characterization and Size-effect study using Scanning Electron Microscopy and X-Ray Diffraction , Debashis Das

Behaviour Of Laminated Roof Under High Horizontal Stress , Prasoon Garg

Theses/Dissertations from 2017 2017

Optimization of Mineral Processing Circuit Design under Uncertainty , Seyed Hassan Amini

Evaluation of Ultrasonic Velocity Tests to Characterize Extraterrestrial Rock Masses , Thomas W. Edge II

A Photogrammetry Program for Physical Modeling of Subsurface Subsidence Process , Yujia Lian

An Area-Based Calculation of the Analysis of Roof Bolt Systems (ARBS) , Aanand Nandula

Developing and implementing new algorithms into the LaModel program for numerical analysis of multiple seam interactions , Mehdi Rajaeebaygi

Adapting Roof Support Methods for Anchoring Satellites on Asteroids , Grant B. Speer

Simulation of Venturi Tube Design for Column Flotation Using Computational Fluid Dynamics , Wan Wang

Theses/Dissertations from 2016 2016

Critical Analysis of Longwall Ventilation Systems and Removal of Methane , Robert B. Krog

Implementing the Local Mine Stiffness Calculation in LaModel , Kaifang Li

Development of Emission Factors (EFs) Model for Coal Train Loading Operations , Bisleshana Brahma Prakash

Nondestructive Methods to Characterize Rock Mechanical Properties at Low-Temperature: Applications for Asteroid Capture Technologies , Kara A. Savage

Mineral Asset Valuation Under Economic Uncertainty: A Complex System for Operational Flexibility , Marcell B. B. Silveira

A Feasibility Study for the Automated Monitoring and Control of Mine Water Discharges , Christopher R. Vass

Spontaneous Combustion of South American Coal , Brunno C. C. Vieira

Calibrating LaModel for Subsidence , Jian Yang

Theses/Dissertations from 2015 2015

Coal Quality Management Model for a Dome Storage (DS-CQMM) , Manuel Alejandro Badani Prado

Design Programs for Highwall Mining Operations , Ming Fan

Development of Drilling Control Technology to Reduce Drilling Noise during Roof Bolting Operations , Mingming Li

The Online LaModel User's & Training Manual Development & Testing , Christopher R. Newman

How to mitigate coal mine bumps through understanding the violent failure of coal specimens , Gamal Rashed

Theses/Dissertations from 2014 2014

Effect of biaxial and triaxial stresses on coal mine shale rocks , Shrey Arora

Stability Analysis of Bleeder Entries in Underground Coal Mines Using the Displacement-Discontinuity and Finite-Difference Programs , Xu Tang

Experimental and Theoretical Studies of Kinetics and Quality Parameters to Determine Spontaneous Combustion Propensity of U.S. Coals , Xinyang Wang

Bubble Size Effects in Coal Flotation and Phosphate Reverse Flotation using a Pico-nano Bubble Generator , Yu Xiong

Integrating the LaModel and ARMPS Programs (ARMPS-LAM) , Peng Zhang

Theses/Dissertations from 2013 2013

Column Flotation of Subbituminous Coal Using the Blend of Trimethyl Pentanediol Derivatives and Pico-Nano Bubbles , Jinxiang Chen

Applications of Surface and Subsurface Subsidence Theories to Solve Ground Control Problems , Biao Qiu

Calibrating the LaModel Program for Shallow Cover Multiple-Seam Mines , Morgan M. Sears

The Integration of a Coal Mine Emergency Communication Network into Pre-Mine Planning and Development , Mark F. Sindelar

Factors considered for increasing longwall panel width , Jack D. Trackemas

An experimental investigation of the creep behavior of an underground coalmine roof with shale formation , Priyesh Verma

Evaluation of Rope Shovel Operators in Surface Coal Mining Using a Multi-Attribute Decision-Making Model , Ivana M. Vukotic

Theses/Dissertations from 2012 2012

Calculating the Surface Seismic Signal from a Trapped Miner , Adeniyi A. Adebisi

Comprehensive and Integrated Model for Atmospheric Status in Sealed Underground Mine Areas , Jianwei Cheng

Production and Cost Assessment of a Potential Application of Surface Miners in Coal Mining in West Virginia , Timothy A. Nolan

The Integration of Geomorphic Design into West Virginia Surface Mine Reclamation , Alison E. Sears

Truck Cycle and Delay Automated Data Collection System (TCD-ADCS) for Surface Coal Mining , Patricio G. Terrazas Prado

New Abutment Angle Concept for Underground Coal Mining , Ihsan Berk Tulu

Theses/Dissertations from 2011 2011

Experimental analysis of the post-failure behavior of coal and rock under laboratory compression tests , Dachao Neil Nie

The influence of interface friction and w/h ratio on the violence of coal specimen failure , Simon H. Prassetyo

Theses/Dissertations from 2010 2010

A risk management approach to pillar extraction in the Central Appalachian coalfields , Patrick R. Bucks

The Impacts of Longwall Mining on Groundwater Systems -- A Case of Cumberland Mine Panels B5 and B6 , Xinzhi Du

Evaluation of ultrafine spiral concentrators for coal cleaning , Meng Yang

Theses/Dissertations from 2009 2009

Development of a coal reserve GIS model and estimation of the recoverability and extraction costs , Chandrakanth Reddy Apala

Application and evaluation of spiral separators for fine coal cleaning , Zhuping Che

Weak floor stability in the Illinois Basin underground coal mines , Murali M. Gadde

Design of reinforced concrete seals for underground coal mines , Rajagopala Reddy Kallu

Employing laboratory physical modeling to study the radio imaging method (RIM) , Jun Lu

Influence of cutting sequence and time effects on cutters and roof falls in underground coal mine -- numerical approach , Anil Kumar Ray

Implementing energy release rate calculations into the LaModel program , Morgan M. Sears

Modeling PDC cutter rock interaction , Ihsan Berk Tulu

Analytical determination of strain energy for the studies of coal mine bumps , Qiang Xu

Improvement of the mine fire simulation program MFIRE , Lihong Zhou

Theses/Dissertations from 2008 2008

Program-assisted analysis of the transverse pressure capacity of block stoppings for mine ventilation control , Timothy J. Batchler

Analysis of factors affecting wireless communication systems in underground coal mines , David P. McGraw

Analysis of underground coal mine refuge shelters , Mickey D. Mitchell

Theses/Dissertations from 2007 2007

Dolomite flotation of high magnesium phosphate ores using fatty acid soap collectors , Zhengxing Gu

Evaluation of longwall face support hydraulic supply systems , Ted M. Klemetti II

Experimental studies of electromagnetic signals to enhance radio imaging method (RIM) , William D. Monaghan

Analysis of water monitoring data for longwall panels , Joseph R. Zirkle

Theses/Dissertations from 2006 2006

Measurements of the electrical properties of coal measure rocks , Nikolay D. Boykov

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Making steel with electricity

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Steel is one of the most useful materials on the planet. A backbone of modern life, it’s used in skyscrapers, cars, airplanes, bridges, and more. Unfortunately, steelmaking is an extremely dirty process.

The most common way it’s produced involves mining iron ore, reducing it in a blast furnace through the addition of coal, and then using an oxygen furnace to burn off excess carbon and other impurities. That’s why steel production accounts for around 7 to 9 percent of humanity’s greenhouse gas emissions worldwide, making it one of the dirtiest industries on the planet.

Now Boston Metal is seeking to clean up the steelmaking industry using an electrochemical process called molten oxide electrolysis (MOE), which eliminates many steps in steelmaking and releases oxygen as its sole byproduct.

The company, which was founded by MIT Professor Emeritus Donald Sadoway, Professor Antoine Allanore, and James Yurko PhD ’01, is already using MOE to recover high-value metals from mining waste at its Brazilian subsidiary, Boston Metal do Brasil. That work is helping Boston Metal’s team deploy its technology at commercial scale and establish key partnerships with mining operators. It has also built a prototype MOE reactor to produce green steel at its headquarters in Woburn, Massachusetts.

And despite its name, Boston Metal has global ambitions. The company has raised more than $370 million to date from organizations across Europe, Asia, the Americas, and the Middle East, and its leaders expect to scale up rapidly to transform steel production in every corner of the world.

“There’s a worldwide recognition that we need to act rapidly, and that’s going to happen through technology solutions like this that can help us move away from incumbent technologies,” Boston Metal Chief Scientist and former MIT postdoc Guillaume Lambotte says. “More and more, climate change is a part of our lives, so the pressure is on everyone to act fast.”

A decades-long search

Since the 1980s, Sadoway had conducted research on the electrochemical process by which aluminum is produced. The focus of the research was to find a replacement for the consumable anode used in that process, which makes carbon dioxide as a by-product. During that work, he began to conceptualize a similar electrochemical process to make iron, the precursor to steel.

But it wasn’t until around 2012 that Sadoway and Allanore, then a postdoc at MIT, discovered an iron-chromium alloy that could serve as a cheap enough anode material to make the process commercially viable and produce oxygen as a byproduct. That's when the pair partnered with James Yurko, a former student, to found Boston Metal.

“All of the fundamental studies and the initial technologies came out of MIT,” Lambotte says. “We spun out of research that was patented at MIT and licensed from MIT’s Technology Licensing Office.”

Lambotte joined the company shortly after Sadoway’s team published a 2013 paper in Nature describing the MOE platform.

“That’s when it went from the lab, with a coffee cup-sized experiment to prove the fundamentals and produce a few grams, to a company that can produce hundreds of kilograms, and soon, tons of metal,” Lambotte says.

Boston Metal’s molten oxide electrolysis process takes place in modular MOE cells, each the size of a school bus. Iron ore rock is fed into the cell, which contains the cathode (the negative terminal of the MOE cell) and an anode immersed in a liquid electrolyte. The anode is inert, meaning it doesn’t dissolve in the electrolyte or take part in the reaction other than serving as the positive terminal. When electricity runs between the anode and cathode and the cell reaches around 1,600 degrees Celsius, the iron oxide bonds in the ore are split, producing pure liquid metal at the bottom that can be tapped. The byproduct of the reaction is oxygen, and the process doesn’t require water, hazardous chemicals, or precious-metal catalysts.

The production of each cell depends on the size of its current. Lambotte says with about 600,000 amps, each cell could produce up to 10 tons of metal every day. Steelmakers would license Boston Metal’s technology and deploy as many cells as needed to reach their production targets.

Boston Metal is already using MOE to help mining companies recover high-value metals from their mining waste, which usually needs to undergo costly treatment or storage. Lambotte says it could also be used to produce many other kinds of metals down the line, and Boston Metal was recently selected to negotiate grant funding to produce chromium metal — critical for a number of clean energy applications — in West Virginia.

“If you look around the world, a lot of the feedstocks for metal are oxides, and if it’s an oxide, then there’s a chance we can work with that feedstock,” Lambotte says. “There’s a lot of excitement because everyone needs a solution capable of decarbonizing the metal industry, so a lot of people are interested to understand where MOE fits in their own processes.”

Gigatons of potential

Boston Metal’s steel decarbonization technology is currently slated to reach commercial-scale in 2026, though its Brazil plant is already introducing the industry to MOE.

“I think it’s a window for the metal industry to get acquainted with MOE and see how it works,” Lambotte says. “You need people in the industry to grasp this technology. It’s where you form connections and how new technology spreads.”

The Brazilian plant runs on 100 percent renewable energy.

“We can be the beneficiary of this tremendous worldwide push to decarbonize the energy sector,” Lambotte says. “I think our approach goes hand in hand with that. Fully green steel requires green electricity, and I think what you’ll see is deployment of this technology where [clean electricity] is already readily available.”

Boston Metal’s team is excited about MOE’s application across the metals industry but is focused first and foremost on eliminating the gigatons of emissions from steel production.

“Steel produces around 10 percent of global emissions, so that is our north star,” Lambotte says. “Everyone is pledging carbon reductions, emissions reductions, and making net zero goals, so the steel industry is really looking hard for viable technology solutions. People are ready for new approaches.”

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• Donald Sadoway
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List of Research Topics in Data Mining for PhD

Data mining is denoted as the extraction of beneficial data from a large amount of data based on heterogeneous sources . The techniques based on data mining are used to acquire the data that is used for data analysis and future prediction. If you are looking for list of research topics in data mining for phd.

Introduction to Data Mining

Data mining is considered the logical process that is deployed to find beneficial data . After the determination of patterns and information, data mining is deployed to make the decisions. The data mining process is enabling the following functions such as.

• Simulate the speed of creating the informed decisions
• In data, all the repetitive and chaotic noises are examined
• The relevant data is used for the access

Similarly, the elevation of IoT is to increase the vision of real-time data mining processes with billions of data for instance drug detection in the medical field.

How does it work?

Measure the opinion and sentiment of users, fraud detection, spam email filtering, database marketing, credit risk management and more are the notable uses in the data mining process. It is deployed to analyze and explore large quantities of data for the derivation of adequate patterns.

If you are looking for reliable and trustworthy research guidance in data mining projects in addition to on-time project delivery, then reach us and team up with our research experts for the best results. We provide 24/7 support and in-depth research knowledge for research scholars. The research scholars can contact us for more references in data mining. It’s time to discuss the developments of components in data mining.

Components of Data Mining

• Data has to exist in a beneficial format similar to the table or graph
• Application software is used for the data analysis process
• It is used to regulate and store the data in the multidimensional database system
• Data mining is deployed in the process of extraction, transformation, and load transaction of data toward the data warehouse system
• Data access is provided to business analysts and professionals based on information technology

With the help of all these research components of data mining, you may precede your data mining PhD projects. We have a lot of recent research techniques, tools, and protocols to provide the finest list of research topics in data mining for PhD. In addition, here we offer a list of real-time applications in data mining for your reference. Let us check out the novel applications based on data mining.

Applications in Data Mining

• Predictive agriculture to track the crop’s health
• Sentiment analysis for the intention prevention
• Network intrusion detection and prevention
• Online transaction fraud detection system
• Opinion mining from social network

For add-on information, all the research field has their research issues or challenges. Similarly, the research problems in data mining are highlighted by our research experts with the appropriate analysis in the following.

Challenges in Data Mining

• Information about integration is required from the heterogeneous database and the global information systems
• The result of data mining is not accurate when the data set is not different
• Some modifications are essential in the business practices for the determination to utilize the uncovered data
• Large databases are required for the data mining process and often it is hard to manage
• Overfitting
• The training database is a small size so it won’t fit the future states in the process
• Data mining queries have to be formulated through the skilled experts

Research Solutions in Data Mining

Predictive analytics is denoted as the collection of statistical techniques that are deployed to analyze the existing and historical data that results in the prediction of future events. In the following, we have enlisted the techniques of predictive analysis.

• Data mining
• Predictive modeling
• Machine learning

Oracle data mining is abbreviated as ODM and it is one of the elements in oracle’s advanced analytics database. It is deployed to provide powerful data mining algorithms which are assistive for the data analyst to acquire the treasured insights in data for the prediction process. In addition, it is used to predict the behavior of the customers and that is used to direct the finest customer and cross-selling. The SQL functions are deployed in the algorithm and that is to excavate the data tables.

Types and Taxonomy of Data Mining

The data mining process is using various techniques to determine the type of mining, pattern detection, data recovery operation, and knowledge discovery. The implementation of the data mining thesis is listed as the process in the following along with its specifications.

• Weighted hierarchical clustering
• Hierarchical clustering
• Logistic regression
• K-Nearest neighbor
• Artificial neural network (ANN)
• Support vector machine (SVM)
• Decision tree
• Naive Bayes

We have successfully delivered several project topics based on data mining with the best quality and novelty. Our research team and developers are highly qualified and are intended uniquely to establish effective research ideas with authenticity. So, the research scholars can enthusiastically contact our research experts anytime on the subject of the doubts and requirements related to data mining. Below, we have stated the significant process of data mining.

Process of Data Mining

The process of data mining is to understand the data via the models such as database systems, machine learning, and statistics, finding patterns, and cleaning the raw data. In the following, we have enlisted the data mining research concepts.

• Data warehousing
• Data Analytics
• Artificial intelligence
• Data preparation and cleansing

We have an in-depth vision in all the areas related to this field. We will make your work stress free through preceding your research in the list of research topics in data mining for PhD. As well as, we made all hard topics easy with our smart work. You can find our keen help for your PhD research. Now, the research scholars can refer to the following research areas based on data mining.

Research Areas in Data Mining

• Market basket analysis
• Intrusion detection
• Future healthcare

Although you can find the above information with ease it is hard to choose and find significant research topics in data mining. Thus, we have listed down a vital list of research topics in data mining for PhD and it is beneficial for the research scholars to develop their recent research.

Research Topics in Data Mining

• Research on data mining of physical examination for risk factors of chronic diseases based on classification decision tree
• Empowerment of digital technology to improve the level of agricultural economic development based on data mining
• A quality evaluation scheme for curriculum in ideological and political education based on data mining
• Massive AI-based cloud environment for smart online education with data mining
• In-depth data mining method of network shared resources based on k means clustering
• Data analysis on the performance of students based on health status using genetic algorithm and clustering algorithms
• A Markov chain model to analyze the entry and stay states of frequent visitors to Taiwan
• Optimization of the average travel time of passengers in the Tehran metro using data mining methods
• Collaborative learning for improving the intellectual skills of dropout students using data mining techniques
• Towards a machine learning and data mining approach to identify customer satisfaction factors on Airbnb

If you require more list of research topics in data mining of PhD to discuss and to shape your research knowledge you can approach our research experts. Above we have discussed the major topics in data mining. Our well-experienced research and development experts have listed down some of the research trends to support the innovative research project using bethe low-mentioned trends. To add information, we assist with your ideas to obtain better results.

Research Trends in Data Mining

• Privacy protection and information security in data mining
• Multi-databases data mining
• Biological data mining
• Visual data mining
• Standardization of data mining query language
• Integration of data mining with database systems, data warehouse systems, and web database systems
• Scalable and interactive data mining methods
• Application exploration

So far, we have discussed the up-to-date enhancements in data mining to select novel research projects. All the above-mentioned trends help to select the most appropriate research topic for the research and we do not skip any of them in the list of research topics in data mining for PhD Here, we have listed some of our innovative methods and approaches based on data mining.

Algorithms in Data Mining

• Locally estimated in scatter plot smoothing
• Logistic and stepwise regression
• Multivariate adaptive regression splines
• Ordinary least squares regression
• Generalized linear models
• Computational learning theory
• Grammar induction
• Meta-learning
• Soft computing
• Dynamic programming
• Sparse dictionary learning
• Inductive in logic programming
• Association rule learning
• Genetic algorithm
• Bayesian networks
• Reinforcement learning
• Deep learning
• FCM, FPCM and SPCM
• Possibility C means the algorithm
• Ordering points to identify clustering structure(OPTICS)
• Farthest first algorithm
• Expectation maximization (EM)
• K-Means clustering
• Cobweb clustering algorithm
• Density-based spatial clustering algorithm
• Deep convolutional networks
• Deep belief networks
• Recurrent neural networks
• Feed forward the artificial neural network
• Learning vector quantization
• Self-organizing map
• Clonal selection algorithm
• Artificial immune recognition system

The following is the list of research protocols that are used in the implementation of data mining research projects. More than that there are several protocols are available in this field, so the research scholars can contact us to grab more data about the data mining protocols.

Notable Protocols for Data Mining

• It is deployed for the homomorphic encryption scheme for the ElGamal encryption
• Privacy, effectiveness, and efficiency degree are the three notable parameters that are deployed in the determination performance of the PPDDM protocol

Thus far we have seen the details about the protocols that are used in data mining projects and their most important uses. For more details on the functions of data mining, the research scholars can take a look at our website. The following is the list of simulation tools that are used in the projects based on data mining.

Simulation Tools in Data Mining

• Oracle data mining

Performance Metrics in Data Mining

Above mentioned are notable parameters based on the performance metrics in the data mining process. Along with that, our experienced research professionals in data mining have highlighted the datasets that are essential for the implementation of data mining-based research projects in the following.

Datasets in Data Mining

• Disease diagnosis and recommended remedy
• Annotated Arabic extremism tweets

We hope you receive a clear interpretation of data mining research projects. In addition, our teams of experts are creating more ideas in data mining for your ease. Therefore, we are willing to assist you to produce an excellent research project topic in data mining for your Ph.D. research within a stipulated period. So, the research scholars can contact us for additional data about the topical list of research topics in data mining for phd.

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PhD Topics in Computer Science Data Mining

      PhD Topics in Computer Science Data Mining is your definitive solution for all your research related issues. When it comes to Computer Science Data Mining, we suggest choosing Weka for data mining as it is platform-independent and possesses language portability, i.e., Java. Topics in Data Mining are an attractive field because of their growing relevance. We have the best team made up of vibrant experts and qualified developers who are working on the development of Data mining tools for more than a decade. Usually, scholars prefer projects using Data Mining. We can mine the best possible novel and an original concept for PhD Computer Science Data Mining.

Topics in Computer Science Data Mining

      PhD Topics in Computer Science Data Mining will remove all your stress and it will help you also to explore the field of data mining along with some aid from GUI environment. Data mining is also often considered an interdisciplinary field that falls under various domains such as  Statistics, databases, Machine learning, Mathematics, visualization, and High-Performance Computing.  Among various other tools, Weka can be considered the greatest tool that can also execute data mining concept, which has inbuilt data pre-processing tools and learning algorithms. We guide researchers and scholars from around the world to thoroughly explore this domain.  Now let us also have a sneak peek regarding Computer Science Data Mining.

Important features of Data Mining

• Holistic collection of Modeling and also data processing techniques
• Platform supported are also Windows, MAC OS X, Linux
• Execute various data mining operations such as data processing, classification, clustering, regression, feature selection and also visualization.
• Possess features also for adding up new algorithms
• Database connectivity using SQL
• Grants a variety of algorithms for Data mining and also Machine learning approach
• Open source and also Platform independent as it is written in Java
• No need also for data mining specialist for handling it and also Provides flexibility for scripting
• GUI Interfacing makes it user friendly
•  Used along with R, Eclipse IDE, Matlab and also many more
• Primarily used also for research and also educational purpose

It’s Objective:

• Associative rule to associate data
• Calculating methods
• Clustering data
• Categorization of data
• Regression analysis and also prediction
• Implementing Learning algorithms

Required Algorithms

Classification of algorithm:.

• Artificial Immune Recognition system
• Clonal selection algorithm
• Self organizing Map
• Learning Vector Quantization
• Feed forward also Artificial Neural Network
• Recurrent Neural Networks
• Deep Belief Networks
• Deep Convolutional also Networks

Clustering Algorithms:

• Density based spatial clustering algorithm
• Cobweb Clustering algorithm
• K-Means clustering
• EM(also Expectation maximization)
• Farthest first algorithm
• Ordering points also to identify clustering structure(OPTICS)
• Possibilistic C Means Algorithm
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Deep-sea mining forms ‘dust clouds’ that devastate marine life

R ecent PhD research conducted at the bottom of the Pacific Ocean has revealed new insights into the potential impacts of deep-sea mining on marine life .

Marine geologist Sabine Haalboom's findings illustrate that while much of the debris from mining activities -- referred to as 'dust clouds' -- settles relatively close to its source, a notable portion spreads far into the water.

This research, carried out in the Clarion Clipperton Zone, provides a crucial understanding of how mining operations could affect these pristine environments. Haalboom defended her dissertation on this topic at Utrecht University , highlighting significant concerns for deep-sea ecosystems.

Deep-sea mining and marine life

The depths of the ocean harbor unique ecosystems, with conditions and life forms that remain largely mysterious to scientists. These ecosystems are often fragile and sensitive to changes in their environment.

Deep-sea mining, particularly the extraction of valuable metals like manganese nodules, disturbs the ocean floor's silt. This process can create extensive dust clouds, clouding the water over vast areas and potentially impacting these pristine habitats.

Given our limited understanding of deep-sea life, these disturbances could have unforeseen effects on the delicate underwater communities. The organisms living in these depths rely on specific environmental conditions for survival. Any alteration, even minor, could disrupt their way of life, leading to unknown consequences.

The potential impact of deep-sea mining on biodiversity and marine ecosystem functions remains a significant concern. Therefore, deep-sea mining's environmental footprint needs careful consideration.

Researchers stress the importance of thorough studies to understand the full implications before engaging in large-scale mining operations in these unexplored and vulnerable areas.

Deep-sea mining's environmental footprint

Haalboom utilized various instruments to measure the quantity and size of suspended particles in the ocean water. Her experiments took place in the Clarion Clipperton Zone, an area rich in manganese nodules.

She dragged a 500-kilogram grid of steel chains across a 500-meter stretch of the seabed. This action stirred up a significant amount of sediment, resulting in immediate murkiness in the water.

Initially, most of this stirred-up material settled quickly, within a few hundred meters of the disturbance site. This quick settling suggested that the immediate impact of mining activities might be localized. However, further observations revealed a different aspect.

A small fraction of the sediment did not settle quickly and remained suspended in the water. This suspended sediment was visible even hundreds of meters away from the initial disturbance.

These findings highlight the potential for deep-sea mining activities to affect broader areas of the marine environment, emphasizing the need for thorough research before large-scale mining operations proceed.

Persistent clouds

Further studies have shown that these "dust clouds" can travel up to five kilometers from the original mining site. This persistence poses a potential threat to the clarity of the water, which is typically crystal clear and vital for the survival of local marine life .

The scarce food available in these clear waters is crucial for the organisms that inhabit the deep sea, making even small changes to their environment potentially impactful.

Additional consequences

Deep-sea mining poses additional risks. It can disrupt habitats, leading to the loss of biodiversity. The noise and vibrations from mining equipment can affect marine life, particularly species reliant on echolocation.

Deep-sea mining activities can release toxic substances trapped in seabed sediments, contaminating the water and harming marine organisms. The physical removal of substrate can destroy slow-growing deep-sea corals and sponges, critical for ecosystem health.

Additionally, the increased human activity could introduce invasive species, further threatening native marine life. These potential impacts underscore the need for cautious, well-informed approaches to deep-sea mining.

Need for research in deep-sea mining

Haalboom's co-promoter, NIOZ oceanographer Henko de Stigter, has expressed concern over the rapid commercial interest in deep-sea mining. He argues that the initial findings of quick sediment settling do not capture the full potential impact of these mining activities on deep-sea ecosystems .

The long-term effects of even minimal sediment dispersal are still unknown, prompting both Haalboom and De Stigter to advocate for more extensive research before proceeding with large-scale mining operations.

In conclusion, while deep-sea mining presents a tempting opportunity to extract valuable resources, the potential risks to unknown marine ecosystems and the broader environmental impacts demand careful consideration.

The call for further study is clear: we must fully understand the consequences of our actions in these remote, unexplored parts of our planet before making irreversible decisions.

Like what you read? Subscribe to our newsletter for engaging articles, exclusive content, and the latest updates. 

Check us out on EarthSnap , a free app brought to you by Eric Ralls and Earth.com.

• Open access
• Published: 21 May 2024

Contribution to the harm assessment of darknet markets: topic modelling drug reviews on Dark0de Reborn

• Ákos Szigeti   ORCID: orcid.org/0000-0003-3641-1648 1 ,
• Richard Frank 2 &
• Tibor Kiss 3  

Crime Science volume  13 , Article number:  13 ( 2024 ) Cite this article

24 Accesses

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Amid the global opioid crisis, the volume of drug trade via darknet markets has risen to an all-time high. The steady increase can be explained by the reliable operation of darknet markets, affected by community-building trust factors reducing the risks during the process of the darknet drug trade. This study was designed to explore the risk reduction efforts of the community of a selected darknet market and therefore contribute to the harm assessment of darknet markets. We performed Latent Dirichlet Allocation topic modelling on customer reviews of drug products ( n  = 25,107) scraped from the darknet market Dark0de Reborn in 2021. We obtained a model resulting in 4 topics (coherence score = 0.57): (1) feedback on satisfaction with the transaction; (2) report on order not received; (3) information on the quality of the product; and (4) feedback on vendor reliability. These topics identified in the customer reviews suggest that the community of the selected darknet market implemented a safer form of drug supply, reducing risks at the payment and delivery stages and the potential harms of drug use. However, the pitfalls of this form of community-initiated safer supply support the need for universally available and professional harm reduction and drug checking services. These findings, and our methodological remarks on applying text mining, can enhance future research to further examine risk and harm reduction efforts across darknet markets.

Introduction

In the 2010s, opioid overdoses have dramatically increased drug-related deaths in North America (Mattson et al., 2021 ) and are now a global health challenge (Krausz et al., 2021 ). The increase has been primarily due to the emergence of fentanyl and other synthetic opioids in the illicit drug market (Pardo et al., 2019 ). These substances are typically distributed already mixed with other drugs (such as heroin), often without the knowledge of the consumer. Mixing can cause an overdose, as the lethal dose of synthetic opioids is significantly lower than that of their non-synthetic counterparts.

In parallel with the opioid overdose crisis, darknet markets selling drugs on the non-indexed part of the internet have emerged and flourished. Although darknet markets accounted for only a tiny slice of the global illicit drug market even in their most prosperous years, figures show that their share had steadily increased in the second half of the 2010s (The United Nations Office on Drugs and Crime, 2020 ). Data showed that, in the short term, the restrictions caused by the COVID-19 pandemic have even boosted this increase in the number of darknet drug purchases (European Monitoring Centre for Drugs and Drug Addiction & Europol, 2020 ; Hawdon et al., 2022 ). However, recent reports suggest that in the long term, the pandemic may have contributed to a decrease in the volume of the darknet drug trade (European Monitoring Centre for Drugs and Drug Addiction., 2022 ) since delivery difficulties due to the lockdowns have destroyed the reliable link between vendors and customers (Bergeron et al., 2022 a). The decreasing trend may have also been significantly influenced by the emergence of encrypted messaging applications and social media, providing channels for the online drug trade (Childs et al., 2020 ; Moyle et al., 2019 ; van der Sanden et al., 2022 ). While these platforms are typically used for retail, darknet markets have increasingly been used for wholesale (The United Nations Office on Drugs and Crime, 2023 ). Recent trends suggest an increased hybridisation between the surface web, the deep web (i.e. messenger applications), and the darknet regarding their usage for drug trade (Tzanetakis & South, 2023 ).

The reliable operation of darknet markets over a decade has been based on a number of interrelated factors. Foremost among these is the anonymity of vendors and customers (Bancroft & Reid, 2017 ), provided by various encryption techniques. These include anonymisation software (e.g. The Onion Router), encryption protocols (e.g. Pretty Good Privacy), operational security measures (e.g. non-suspicious packaging), encrypted messaging services, and cryptocurrencies (e.g. Bitcoin, Monero) (Basheer, 2022 ). Although Bitcoin, the most widely traded cryptocurrency, is still present in the darknet markets, the deterioration of its untraceability has led to the rise of altcoins such as Monero and Ethereum on these platforms (Bahamazava & Nanda, 2022 ; European Union Agency for Law Enforcement Cooperation., 2021 ). In addition to cryptocurrencies, the payment transaction is usually secured by an escrow system in which the funds are withheld from the vendor until the customer confirms receipt of the product (Janze, 2017 ). Customers can also opt for the so-called finalizing early (FE) option, meaning that the vendor receives the payment when the order is placed, which exposes the customers to fraud (Moeller, 2022 ). A recent study reported that escrow reduces the number of drug sales, while in the case of high-value transactions by drug traffickers, escrow increases sales (Andrei et al., 2023 ). The process of purchasing via darknet markets is crucially affected by vendor reliability (Holt et al., 2016 ; Kamphausen & Werse, 2019 ; Laferrière & Décary-Hétu, 2023 ). The reputation based on this reliability was proven to be transferrable among darknet markets (Norbutas et al., 2020 ). Social ties and repeated exchanges between vendors and customers were named as the key elements of trust building between the actors (Munksgaard, 2023 ; Norbutas et al., 2020 ). Sales are concentrated on a small number of sellers, whose close relationships with their customers help them move to a new market when law enforcement intervenes (Décary-Hétu & Giommoni, 2017 ). While vendor trustworthiness was found to be a better predictor of vendor selection than product diversity or affordability (Duxbury & Haynie, 2018 ), when purchasing drugs, the quality of products, including their potency and purity, is of paramount importance (Bancroft & Reid, 2016 ; Caudevilla, 2016 ; Munksgaard et al., 2022 ). Finally, the transaction would not be completed without the timely and stealthy delivery of the products (Aldridge & Askew, 2017 ; Bancroft & Reid, 2016 ; Espinosa, 2019 ). The waiting time (in addition to the time it takes for the payment transaction to be completed, the vendor’s response time, and sometimes the time taken for dispute) can significantly affect the purchase decision of an addicted customer (Bancroft, 2023 ).

The reliability of the whole process is manifested in the built-in reputation systems of darknet markets (Laferrière & Décary-Hétu, 2023 ; Masson & Bancroft, 2018 ; Przepiorka et al., 2017 ). Similarly to surface web markets, most darknet markets allow users to write textual feedback (reviews) about products and vendors (Brinck et al., 2023 ). In addition to the reviews, in most cases, users can also rate the vendors, which directly affects the prices that vendors can charge: higher ratings mean higher prices (Espinosa, 2019 ; Janetos & Tilly, 2017 ). Reputation data can provide vendors with some predictability for their business model (Kelly, 2023 ), and customers with information on the reliability of specific vendors and products (Brinck et al., 2023 ). Darknet markets face competitive pressure to inform customers about the trustworthiness of vendors in their reputation systems (Janetos & Tilly, 2017 ). Therefore, the data of these reputation systems provide the opportunity for researchers to better understand the operation of darknet markets (Brinck et al., 2023 ; Jardine, 2019 ; Szigeti et al., 2023 ).

Trust among vendors, customers, and darknet market operators form a community of interest (Masson & Bancroft, 2018 ), a social figuration of the cooperating and interdependent actors, which involve undercover law enforcement agencies (Kamphausen & Werse, 2019 ) and delivery providers as well (Szigeti et al., 2023 ). These communities have the potential to make the process more secure and reduce the harms of drug use, even if some of the actors are motivated by economic interests. Darknet markets can reduce the physical violence associated with drug trafficking by removing face-to-face meetings from the crime script (Bergeron et al., 2022b ; Martin, 2014 ; Shortis et al., 2020 ). Furthermore, the community of darknet markets can reduce the harms associated with drug consumption by providing advice on safer use and information on the purity of the products (Aldridge et al., 2018 ; Caudevilla, 2016 ; Shortis et al., 2020 ). Previous user experience-based research also suggested that darknet markets have the potential to provide a drug supply that is both “clean” and “safe” (Goodyear et al., 2020 ). Finally, although the reputation system and escrow services of darknet markets can reduce the risk of financial victimisation to some extent, customers of this platform are still exposed to scams (Bergeron et al., 2022b ).

If it is technically possible for law enforcement agencies to shut down entire darknet markets, it could directly cut off this drug supply, but in such cases, customers typically migrate to another darknet market (Décary-Hétu & Giommoni, 2017 ; ElBahrawy et al., 2020 ; Ladegaard, 2019 ; Tavabi et al., 2019 ). The research, therefore, questions the long-term success of such law enforcement actions (Horton-Eddison & Cristofaro, 2017 ), presenting them as the extension of the (failed) war on drugs approach to online drug markets (Martin et al., 2023 ). Targeted interventions might be more effective and can be implemented at any stage during the crime script of the darknet drug trade (Jardine, 2021 ). The first stage of the script is informational accumulation, during which users become familiar with the darknet, TOR network, cryptocurrencies, and darknet markets. This is mostly done by surface web searches that are not anonymous and can therefore be leveraged by the authorities. The second stage is account formation when prospective users create cryptocurrency wallets and customer or vendor accounts. An example of intervening at this stage was Operation Bayonet, where Dutch law enforcement agencies took over an entire darknet market and gained direct access to user data. The kind of operation which damages trust among the actors by actual data or financial loss of the users is more effective than simply removing a market from the darknet (Bradley & Stringhini, 2019 ). However, these types of interventions have been criticised for using an extraterritorial surveillance strategy based on questionable legal tactics to collect the data of darknet market users from various geographical locations (Martin et al., 2023 ). The third stage of the crime script is the actual market use when vendors advertise themselves and their products while customers select and order what they are looking for (Jardine, 2021 ). Notifying individuals that they have been observed engaging in darknet market activities could deter them and other users from future use of the platform by showcasing the intelligence-gathering power of law enforcement. An example of this was also Operation Bayonet, where the Dutch agency, after takedown of AlphaBay and Hansa, posted user account details of some accounts they were tracking. The final stage is the delivery and receipt of the products. Even if encrypted, shipping addresses of the recipients are shared during the process, allowing law enforcement to detect and intercept the packages, which, however, is typically only effective in disrupting transnational supply and allows only for interception of an individual package, making a small impact in the war against drugs (Martin et al., 2023 ).

In sum, the above-mentioned displacement and the previously presented harm assessment of darknet markets together suggest that interventions aimed at darknet markets should take into account the risk reduction efforts of the given darknet markets and their communities (Shortis et al., 2020 ). This study was designed to explore these risk reduction efforts by directly assessing the large amount of customer reviews scraped from a selected darknet market. Ultimately, we aimed to contribute to the development of a methodology to systematically measure the harm caused by darknet markets.

Directly exploring the darknet drug trade is challenging due to the difficulty of reaching its anonymous participants, making it difficult to apply traditional methods such as survey questionnaires (Karden & Strizek, 2022 ). However, scraping textual data from darknet markets provides an opportunity for the direct observation of vendor reputation data, which Jardine ( 2019 ) suggested should be used as an element of darknet threat metrics. Unstructured textual data scraped from darknet markets can be analysed by various text analytics methods based on natural language processing algorithms. Since no previous research has, to our knowledge, examined customer reviews on darknet markets by any natural language processing method, we aimed to assess these customer reviews using an exploratory approach, namely Latent Dirichlet Allocation (LDA) topic modelling. LDA topic modelling represents the documents of a corpus (in our case, the reviews) as a set of a fixed number of topics, identifying the topics based on the distribution of words in the corpus (Blei et al., 2003 ).

The data analysed in this study was scraped from the Dark0de Reborn darknet market between June 10 and June 27, 2021. The darknet market was scraped in its entirety, and all products available at the time of data capture were scraped. The darknet was scraped by a custom-written crawler (The Dark Crawler), which allowed the entire platform, product per product, to be scraped. All content on each page was captured, including product name, description, price, and vendor instructions. Vendors placed the products into categories, which were clearly displayed on the page of each product and were captured as part of the data capture, and then used later to select the products for analysis.

The Dark0de Reborn darknet market, whose predecessor was a hacker forum that operated until 2015, opened in May 2020 and closed in February 2022. The closure was presumably the result of an exit scam, i.e. the intentional shutting down of the market by its operators to acquire the funds in deposit. While it existed, this darknet market was a dominant player among illegal online drug markets based on daily minimum sales (The United Nations Office on Drugs and Crime, 2023 ). Thus, although Dark0de Reborn was only a slice of the darknet drug markets, the data scraped from it provided an opportunity to directly examine community factors behind the operation of darknet drug markets on one of its flagship platforms.

Since this research focused on the darknet drug trade, the collected data was filtered to the drug category based on the product categories provided by the users, resulting in 34,445 valid (not blank) reviews. Non-English reviews were then filtered out of the database using the Langdetect package in Python (Danilak, 2014 ), resulting in 26,728 reviews. During the cleaning process, we removed duplicates, since users often posted the same review for different products and orders. We filtered out reviews with the same content that were longer than 30 characters, were written about the same product, or were written by the same user. Finally, we manually went through the first one thousand longest reviews that potentially influenced the analysis the most due to their high token count and deleted the flawed reviews, such as reviews that contained only a word or phrase repeatedly, or non-English items that remained in the sample despite the language detection algorithm. Duplicate removing and manual filtering resulted in a total of 1,621 reviews being deleted and the finalized analysis sample consisted of 25,107 user reviews. The product subcategories in the sample are presented in Table  1 .

We implemented the pre-processing of the data in Python packages such as NLTK (Bird et al., 2009 ) and spaCy (Honnibal & Montani, 2017 ). In addition to the removal of non-textual elements and stopwords, pre-processing included the application of lemmatization as well as bi- and tri-gram algorithms. The topic modelling procedure was implemented with Gensim’s (Rehurek & Sojka, 2011 ) default LDA parameters, and the analysis included nouns, verbs, adjectives, adverbs, and proper nouns.

For topic modelling, the number of topics must be specified in advance, where this number was chosen based on the C v coherence value (Röder et al., 2015 ). After running the model with 2 to 100 topics assumed, the model showed the highest C v coherence score (0.57) in the case of 4 topics. A list of the coherence scores for the different number of topics set for each topic model is presented in Fig.  1 . The topics obtained in the case of 4 topics are summarised in Table  2 . Although the tokens of the corpus were not evenly distributed among the topics obtained in the model with the highest C v value, the subjects of the topics were identifiable. Thus, taking into account both this qualitative assessment and the coherence score, we analysed the model with 4 topics.

The C v coherence score by the number of topics from 2 to 100 set for each topic model

Topic 1: General satisfaction

Topic one (T1) contained the largest share of tokens (51.6%) and reflected general satisfaction with purchasing. Based on both the number of tokens and the content of the texts, this topic represented typical reviews in which buyers briefly described what they were satisfied within the process:

Perfect transaction. Excellent service, product, stealth and very fast shipping. Will continue to come back. Thanks so much Very reliable and honest seller (Quote of user review #1 representing T1).

Customers also used the review to directly express their gratitude to the vendors and recommend them to others:

Super high quality product as usual, good stealth and fast delivery!!! Vendor more than professional, I recommend!!! Thanks a lot and see you soon ;-)  (Quote of user review #2 representing T1).

In addition, praising the quality of the product and emphasising the speed and stealth of delivery were also identifiable elements of the reviews that represented this topic:

Bought a few times from this vendor. Always quick delivery but this time stealth was VERY good. Good quality tabs too, using these for micro-dosing. Thanks for the 5-star service! (Quote of user review #3 representing T1)

Topics 2: Order not received

The topic (T2) with the second largest share of tokens (20.3%) was reports about orders not being received. The reason why these reports contained such a large amount of tokens was that they were typically longer, with users describing the process of their order in detail and explaining their interactions with the vendor:

Ordered on 7th April, vendor accepted/sent order on 8th. Order has NOT arrived. I messaged vendor 4 days ago saying it had not arrived, and sent a follow-up message yesterday. The vendor has not responded to my messages. I checked their profile and can see that they have logged on every day for the past 4 days, so it seems like they are deliberately ignoring my messages. Hopefully, this gets their attention! Update: I have still received no response. (Quote of user review #4 representing T2)

The unresponsiveness of the given vendors was often mentioned, pointing out that customers tried to solve the issues directly with the vendors:

Order sent on 07/05/2021 as of today nothing received contacted methbusters on 3 occasions asking for refund and was told to wait after nearly a month I doubt anything will turn up. So only option is to leave a negative review very unsatisfactory dealer. (Quote of user review #5 representing T2)

At the same time, customers often showed their patience and understanding, and in some cases they expressed willingness to update their review if the vendor does send the product:

Has been over 2 weeks since marked sent and hasn’t arrived yet. I didn’t order a lot so I know it’s a longer wait time but I’ve messaged the vendor twice asking for tracking and any info on the package and got no response. I know he’s a trusted vendor so I’m gonna give it another week but as of right now I’m disappointed. Will change review if package ever arrives. (Quote of user review #6 representing T2)

Topic 3: Product quality

Topic three (T3) was about the quality of the products. In the reviews representing this topic, users typically shared their own experiences of consuming the drug, including describing the drug’s form, smell, taste, cleanliness, and effect:

For the price, you get what you pay for. My pack came with a lot of trimmings, stems, and it was really brown and dry. It smokes decent, but there’s not much of a nose or visual to it. A couple of joints will get you stoned though (Quote of user review #7 representing T3).

Reviewers also shared information about the originality of the drugs, i.e. whether the product delivered matched the product advertised. Some reviewers based their assessments on the look of the products if they had not tried them out yet, but sometimes even included the results of drug tests that they had carried out:

Tested with eztest mdma test kit. Was maximum only medium mdma content. We both got massive headaches. Nearly no positive effect, even after a lot of mg on that evening. (Quote of user review #8 representing T3)

Topic 4: Vendor reliability

Topic four (T4) was about the reliability of the vendor, based on the trust that comes from a long-term reliable relationship between the given customer and vendor. These reviews were typically short and contained only a few phrases, which is why this topic contained the smallest proportion of tokens. The reviews representing this topic were aimed directly at the vendor and often referred to the vendor’s previous presence in other darknet markets:

Reliable long-term DN vendor from many previous sites. Did business with him then and will continue to do so here moving forward. (Quote of user review #9 representing T4)

The authors also often referred to themselves as repeat customers by using phrases like “always” and “usual”, and they stated that they are going to purchase again, demonstrating the already established relationship with the given vendor:

If you want the real deal and to be treated right these guys have always been my go to take care of business guys!!! I guarantee it!! (Quote of user review #10 representing T4)

This research study was designed to explore the operation of darknet markets by implementing topic modelling on customer reviews collected from a selected darknet market. Findings show that the community of the darknet market under study made efforts to deliver a safer form of drug supply. Based on the customer reviews, the platform appears to be able to reduce risks during the payment transaction and the delivery stage, as well as the potential harms of drug use.

The reliable relationship between vendors and customers was mirrored in customer feedback on vendor reliability which often manifested in users declaring themselves as repeat customers (T4). These results support the hypothesis that the reliable operation of darknet markets relies on the trust-based relationship between vendors and customers (Holt et al., 2016 ; Kamphausen & Werse, 2019 ; Laferrière & Décary-Hétu, 2023 ), which is built on the success of repeated transactions (Munksgaard, 2023 ; Norbutas et al., 2020 ). The reported issues about vendors not sending the product (T2) confirm that the conflicts that challenge the vendor-customer relationship are manifested in the financial victimisation of customers (Bergeron et al., 2022 b). Furthermore, emphasising the time and stealth of delivery (T1) is also consistent with previous studies highlighting the role of delivery in maintaining trust between the actors (Aldridge & Askew, 2017 ; Andrei & Veltri, 2024 ; Espinosa, 2019 ; Szigeti et al., 2023 ). These results suggest that risk awareness campaigns should focus on the risks of payment transactions and product delivery (Bradley & Stringhini, 2019 ; Jardine, 2021 ). Informing (potential) darknet market customers about the risks arising during product delivery and exposure to scams could contribute to effective prevention. Evidence suggests that warning darknet market users about a potential scam can reduce vendor and customer activity in the given market (Howell et al., 2022 ). While users may migrate to another market in response, in some cases (for example, a market selling mixed substances), this displacement may be beneficial from a public health perspective. Detecting fentanyl traffickers, and uncovering and dismantling hidden fentanyl networks should be a priority in the strategic planning of darknet market interventions (Maras et al., 2023 ).

The exploration of reputational data also discovered that in addition to praising the products in general (T1), customers use the reviews to share information on the products’ quality and originality (T3). These results support that quality assurance in darknet markets is not only about access to potent drugs but also about safer substance use and consuming pure drugs (Bancroft, 2017 ; Munksgaard et al., 2022 ). Darknet markets, therefore, seem to provide a community-initiated response to the need for safer supply programmes, which recent studies widely emphasised (Bonn et al., 2020 ; Fleming et al., 2020 ; Ivsins et al., 2020 ; Pauly et al., 2022 ). Policing drug markets should focus on the characteristics causing the most problems to the communities, following the model of harm reduction policing (Bacon & Spicer, 2023 ). Hence, policing should take into account the potential of darknet markets in mitigating the harms associated with drug trade and consumption (Shortis et al., 2020 ). However, the implementation of safer supply by the communities of darknet markets raises concerns beyond its illegality. First, the fact that purchasing on the darknet is only available for users with appropriate digital literacy, who thus typically belong to a higher social class (Tzanetakis, 2018 ), results in the exclusion of the most vulnerable groups of drug users. Second, the shift of online drug trafficking from darknet markets to encrypted instant messaging applications and social media removes the quality assurance provided by reputation systems (Demant et al., 2019 ), which can potentially increase the risk of overdoses caused by purchasing unknown substances. Likewise, the lack of assurances on the reliability of vendors and the transaction may also increase the risk of financial losses due to scams in this new form of online drug trafficking. Finally, while there is already some evidence of the high quality of the drugs sold on darknet markets (Caudevilla et al., 2016 ), up-to-date research is needed in this regard and on the quality of harm reduction measures provided by the actors as well. Although peer involvement within harm reduction programmes can have positive impacts on health outcomes (Chang et al., 2021 ), relying on the darknet market’s community to ensure quality assurance and harm reduction is not risk-free (Aldridge et al., 2018 ). For instance, there is no agreement among the users of darknet markets about the meaning of terms such as purity, predictability, or potency (Bancroft, 2020 ). The above-mentioned potential pitfalls of community-based harm reduction support the need for developing web outreach on darknet platforms implemented by professional harm reduction organisations (Davitadze et al., 2020 ). In addition, although darknet markets appear to be able to provide some form of safer supply, their ability to do so is limited, therefore we argue that universal access to drug checking for the general public is also needed to tackle the overdose crisis (Wallace et al., 2022 ).

Limitations

The exploratory analysis of textual data scraped from the darknet market allowed us to examine the characteristics of the online illicit drug trade directly. However, our approach had some limitations regarding data quality, analysis method, and generalisability of the results. First, despite the darknet market’s complex user identification process, bots may registered on the site and create fake reviews. Vendors may also use false reviews to build their reputation or to damage the reputation of others, as they are reportedly prone to do (Kamphausen & Werse, 2019 ). In the data cleaning process, we only filtered out longer reviews with repetitive negative words that would significantly influence the model, so shorter, potentially fake reviews might have been included in the sample. Furthermore, by filtering the sample for English language reviews, we may have removed reviews that could contribute to different results. In addition, we applied Latent Dirichlet Allocation topic modelling, which cannot account for correlations between the topics. The results suggest a correlation between the topics analysed, in which case the Correlated Topic Model (CTM) is recommended (Blei & Lafferty, 2007 ). Therefore, the use of CTM should be considered in future research, but we argue that the implemented LDA process significantly contributed to the understanding of the phenomenon under study. Finally, since this study examined data from only one selected darknet market, our sampling method limits the generalisability of the results. Each darknet market contributes to safer supply to different degrees; for example, a more bounded psychedelic drug user community may reduce the harms associated with substance use to a greater extent (Bancroft et al., 2020 ).

By implementing text analytics on data directly scraped from the darknet, this study not only contributed empirical results to our understanding of the operation of darknet markets but also provided methodological remarks for their harm assessment. The results of this text-mining study can be used as a basis for future research: either for cross-platform comparisons or for further topic-targeted research on the identified topics. In addition, the risk reduction efforts explored by topic modelling suggest that the darknet market under study (among others that we have not examined) provided a platform for safer drug supply during the opioid crisis. Regardless of its quality, the realisation of community-initiated safer supply in this online space provides a glimpse into the digital transformation of our society. However, we argue that this form of safer supply is problematic for a number of reasons, and calls for policy attention regarding the need for improved access to harm reduction and drug checking services.

Data availability

The datasets used and/or analysed during the current study are available from the corresponding author on reasonable request.

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Acknowledgements

The authors are grateful to Katalin Parti (Virginia Tech, USA), Gábor Héra (HUN-REN, Hungary) and György Körmendi (Statistical Products Hungary Ltd., Hungary) for supervising this research.

This article was prepared with the professional support of the doctoral student scholarship program of the Co-Operative Doctoral Program of the Ministry of Culture and Innovation financed from the National Research, Development and Innovation Fund, Hungary.

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ÁSZ conceptualized the study, designed the analysis method, cleaned the data, implemented the analysis, drafted the manuscript, and approved the final submission.

FR collected the data, contributed to the analysis process, revised the drafted manuscript, and approved the final submission.TK conceptualized the study, contributed to the analysis process, revised the drafted manuscript, and approved the final submission.

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Szigeti, Á., Frank, R. & Kiss, T. Contribution to the harm assessment of darknet markets: topic modelling drug reviews on Dark0de Reborn. Crime Sci 13 , 13 (2024). https://doi.org/10.1186/s40163-024-00211-z

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DOI : https://doi.org/10.1186/s40163-024-00211-z

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