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11 Powerful DBMS PPTs to Accelerate Your Learning Journey in Database Management Systems

Table of Contents

Learn Database Management Systems (DBMS PPTs)

Are you ready to embark on a journey into the world of Database Management Systems (DBMS)? Whether you’re a student, a professional, or simply someone intrigued by the inner workings of databases, having access to high-quality learning resources is essential. In this article, we present 11 powerful DBMS PowerPoint presentations (PPTs) that will supercharge your understanding of DBMS concepts and accelerate your learning process. So, let’s dive in and explore these invaluable resources together.

1. DBMS – Introduction to Database Management Systems

In this comprehensive PPT, we lay the foundation by providing an overview of Database Management Systems. From the basics of data storage to the advantages of using a DBMS, this presentation sets the stage for your DBMS journey.

Download Introduction to DBMS PPT

2. DBMS – Data Models

Understanding data models is crucial for effective database management. This PPT delves into various data models, such as hierarchical, network, and relational models, offering insights into their strengths and weaknesses. Gain a solid understanding of the different data models and their applications.

Download Data Models PPT

3. DBMS – Data Abstraction Techniques

Data abstraction is a fundamental concept in DBMS that enables users to interact with databases without worrying about the underlying complexities. This presentation explores data abstraction techniques, including the three levels of data abstraction: physical, logical, and view levels.

Download Data Abstraction Technique PPT

4. DBMS – Database Architecture

Get ready to explore the inner workings of a DBMS with this enlightening PPT on database architecture. Discover the components of a DBMS, such as the data dictionary, query processor, and storage manager. Gain insights into how these components work together to ensure efficient data management.

Download Database Architecture PPT

5. DBMS – Database Design & Entity Relationship – ER Model

Effective database design is crucial for building robust and efficient systems. This presentation focuses on the Entity Relationship (ER) model, a popular approach to database design. Learn how to identify entities, relationships, and attributes, and create an ER diagram to represent the database structure.

Download Database design & ER Model PPT

6. DBMS – ER Design Issues

As you delve deeper into ER modeling, you’ll encounter various design challenges. This PPT addresses common issues such as entity generalization, specialization, and aggregation. Master these concepts to create well-structured databases that accurately represent real-world scenarios.

Download ER Design Issues PPT

7. DBMS – Enhanced / Extended ER Model

The Enhanced/Extended ER model takes ER modeling to the next level by introducing additional concepts and constructs. Explore this powerful extension through this informative presentation, which covers topics like weak entities, ternary relationships, and specialization constraints.

Download Extended ER Model PPT

8. DBMS – Relational Data Model

The relational data model is widely used in modern database systems. This PPT provides a comprehensive overview of the relational model, including key concepts like relations, attributes, and integrity constraints. Understand how data is organized and manipulated in a relational database.

Download RDBMS PPT

9. DBMS – Conversion of ER Model to Relational Model

Transforming an ER model into a relational model can be a complex task. However, with this presentation’s guidance, you’ll learn systematic methods for converting ER diagrams to relational schemas. Master the art of mapping entities, relationships, and attributes into tables, keys, and relationships.

Download PPT for Concerson of ER Model to Relational Data Model

10. DBMS – Relational Algebra

Relational algebra serves as the foundation for manipulating and querying data in a relational database. This PPT explores the core operations of relational algebra, including selection, projection, join, and set operations. Enhance your query-building skills and unleash the power of relational databases.

Download Relational Alegebra PPT

11. DBMS – Different Types of Joins

Join operations are essential for combining data from multiple tables. In this final PPT, we focus on different types of joins, such as inner join, outer join, and self-join. Understand the nuances of each join type and leverage them to extract meaningful insights from your database.

Download PPT on Joins

If you’re seeking visual content to complement your learning experience, consider exploring the DBMS tutorials available on YouTube, particularly those recommended by NPTEL lecture videos. These tutorials offer a captivating way to grasp the intricacies of DBMS, combining informative explanations with engaging visuals. Adding this dynamic element to your learning journey can further enhance your understanding and make the process even more enjoyable.

In conclusion, these 11 powerful DBMS PPTs offer a wealth of knowledge and guidance for your learning journey in Database Management Systems. From understanding the core concepts to mastering advanced topics, these presentations will empower you to become a confident DBMS practitioner. So, start exploring these resources, dive deep into the world of databases, and unlock new possibilities in data management.

Remember, knowledge is power, and with these DBMS PPTs, you’re on the path to unlocking the full potential of Database Management Systems!

*Disclaimer: This article is for informational purposes only. The PPTs mentioned in this article may be available through various educational platforms and websites. Ensure to respect copyright laws and terms of use when accessing and utilizing these resources.

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CS44800: Introduction to Database Systems, Spring 2019

Lecture Schedule

Chapter 1 - Introduction ( ppt / pdf )

Chapter 2 -Database System Concepts and Architecture ( ppt / pdf )

Chapter 13 - Disk Storage, Basic File Structures and Hashing ( ppt )

Chapter 14 - Indexing Structures for Files ( ppt )

Chapter 3 - Data Modeling Using the Entity Relationship (ER) Model ( ppt / pdf )

• Hierarchical model-slides
• Network Model-slides
• Handout on Natural and Outer Joins & Relational Calculus

Chapter 6 - The Relational Algebra and Calculus ( ppt / pdf )

Chapter 7 - Relational Database Design by ER-and EERR-to-Relational Mapping ( ppt / pdf )

Chapter 15 - Algorithms for Query Processing and Optimization ( ppt )

Chapter 8 - SQL-99: Schema Definition, Constraints, and Queries and Views ( ppt )

• JDBC Tutorial
• PL/SQL Tutorial

Chapter 10 - Functional Dependencies and Normalization for Relational Databases ( ppt )

• Example of proving loss less join
• Normalization and lossless joins and FD preservations

Chapter 12 - Practical Database Design Methodology and Use of UML Diagrams ( ppt )

Chapter 16 - Practical Database Design and Tuning ( ppt )

• Concurrency Control in Database Systems , Bharat Bhargava, IEEE Trans on Knowledge and Data Engineering,11(1), Jan.-Feb. 1999
• The Transaction Concept: Virtues and Limitations , Jim Gray, VLDB, 1981
• 2PL and Conflict Graph. ( pdf )
• Locking-Serializability. ( pdf )
• Optimistic-timestamps-Failure-Commitment. ( pdf )
• It will help you learn about CC ideas on 2PL, optimistic, time stamps and about transaction failures to help you do the homework. You may read the papers on CC under handouts and check sections 2 up to 2.3.2. ( pdf )
• You may read slides on chapter 21 (7th edition) up to slides 21-22 and then 27-29 for answers to homework. ( pdf )
• Logs, failures, serializability is discussed in slides for chapter 20. ( pdf )
• Concurrency Control in Database Systems, Bharat Bhargava, IEEE Trans on Knowledge and Data Engineering,11(1), Jan.-Feb. 1999. ( pdf )
• The Transaction Concept: Virtues and Limitations , Jim Gray, VLDB, 1981. ( pdf )

Chapter 19 - Database Recovery Techniques ( ppt )

• Feature analysis of selected database recovery techniques, Bharat Bhargava and Leszek Lilien, Proceedings of the May 4-7, 1981, National Computer Conference . ACM, 1981.( pdf )
• Secure Data Warehouse - Privacy

Chapter 24 - Enhanced Data Models for Advanced Applications ( ppt )

• Introduction to Cloud Computing
• Privacy and Identity Management in Cloud Computing
• Complete Tutorial

Chapter 26 - Web Database Programming using PHP ( ppt )

Chapter 27 - XML-Extensible Markup Language ( ppt )

Chapter 29 - Overview of Data Warehousing and OLAP ( ppt )

Chapter 30 - Emerging Database Technologies and Applications ( ppt )

CS44800, Spring 2019 Purdue University, Computer Science Department

We provide a set of slides to accompany each chapter. Click on the links below to download the slides in Powerpoint format. We also provide zip files of the all Powerpoint files , PDF files , and all figures used in the text

Copyright Note

Errors and Additions

Please bring any errors to our notice, using the chapter number and the slide title to identify the slide. If you have any talks that you would like to share with other users of the book, do let us know.

What is a Database Model

What are your database diagram needs, i want to make my own database diagram in lucidchart., i want to make a database diagram from a lucidchart template..

A database model shows the logical structure of a database, including the relationships and constraints that determine how data can be stored and accessed. Individual database models are designed based on the rules and concepts of whichever broader data model the designers adopt. Most data models can be represented by an accompanying database diagram.

8 minute read

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Types of database models

There are many kinds of data models. Some of the most common ones include:

• Hierarchical database model

Relational model

Network model, object-oriented database model, entity-relationship model.

• Document model
• Entity-attribute-value model
• Star schema
• The object-relational model, which combines the two that make up its name

You may choose to describe a database with any one of these depending on several factors. The biggest factor is whether the database management system you are using supports a particular model. Most database management systems are built with a particular data model in mind and require their users to adopt that model, although some do support multiple models.

In addition, different models apply to different stages of the database design process. High-level conceptual data models are best for mapping out relationships between data in ways that people perceive that data. Record-based logical models, on the other hand, more closely reflect ways that the data is stored on the server.

Selecting a data model is also a matter of aligning your priorities for the database with the strengths of a particular model, whether those priorities include speed, cost reduction, usability, or something else.

Let’s take a closer look at some of the most common database models.

The most common model, the relational model sorts data into tables, also known as relations, each of which consists of columns and rows. Each column lists an attribute of the entity in question, such as price, zip code, or birth date. Together, the attributes in a relation are called a domain. A particular attribute or combination of attributes is chosen as a primary key that can be referred to in other tables, when it’s called a foreign key.

Each row, also called a tuple, includes data about a specific instance of the entity in question, such as a particular employee.

The model also accounts for the types of relationships between those tables, including one-to-one, one-to-many, and many-to-many relationships. Here’s an example:

Within the database, tables can be normalized, or brought to comply with normalization rules that make the database flexible, adaptable, and scalable. When normalized, each piece of data is atomic, or broken into the smallest useful pieces.

Relational databases are typically written in Structured Query Language (SQL). The model was introduced by E.F. Codd in 1970.

Hierarchical model

The hierarchical model organizes data into a tree-like structure, where each record has a single parent or root. Sibling records are sorted in a particular order. That order is used as the physical order for storing the database. This model is good for describing many real-world relationships.

This model was primarily used by IBM’s Information Management Systems in the 60s and 70s, but they are rarely seen today due to certain operational inefficiencies.

The network model builds on the hierarchical model by allowing many-to-many relationships between linked records, implying multiple parent records. Based on mathematical set theory, the model is constructed with sets of related records. Each set consists of one owner or parent record and one or more member or child records. A record can be a member or child in multiple sets, allowing this model to convey complex relationships.

It was most popular in the 70s after it was formally defined by the Conference on Data Systems Languages (CODASYL).

This model defines a database as a collection of objects, or reusable software elements, with associated features and methods. There are several kinds of object-oriented databases:

multimedia database

Hypertext database.

The object-oriented database model is the best known post-relational database model, since it incorporates tables, but isn’t limited to tables. Such models are also known as hybrid database models.

Object-relational model

This hybrid database model combines the simplicity of the relational model with some of the advanced functionality of the object-oriented database model. In essence, it allows designers to incorporate objects into the familiar table structure.

Languages and call interfaces include SQL3, vendor languages, ODBC, JDBC, and proprietary call interfaces that are extensions of the languages and interfaces used by the relational model.

This model captures the relationships between real-world entities much like the network model, but it isn’t as directly tied to the physical structure of the database. Instead, it’s often used for designing a database conceptually.

Here, the people, places, and things about which data points are stored are referred to as entities, each of which has certain attributes that together make up their domain. The cardinality, or relationships between entities, are mapped as well.

A common form of the ER diagram is the star schema, in which a central fact table connects to multiple dimensional tables.

Other database models

A variety of other database models have been or are still used today.

Inverted file model

A database built with the inverted file structure is designed to facilitate fast full text searches. In this model, data content is indexed as a series of keys in a lookup table, with the values pointing to the location of the associated files. This structure can provide nearly instantaneous reporting in big data and analytics, for instance.

This model has been used by the ADABAS database management system of Software AG since 1970, and it is still supported today.

The flat model is the earliest, simplest data model. It simply lists all the data in a single table, consisting of columns and rows. In order to access or manipulate the data, the computer has to read the entire flat file into memory, which makes this model inefficient for all but the smallest data sets.

Multidimensional model

This is a variation of the relational model designed to facilitate improved analytical processing. While the relational model is optimized for online transaction processing (OLTP), this model is designed for online analytical processing (OLAP).

Each cell in a dimensional database contains data about the dimensions tracked by the database. Visually, it’s like a collection of cubes, rather than two-dimensional tables.

Semistructured model

In this model, the structural data usually contained in the database schema is embedded with the data itself. Here the distinction between data and schema is vague at best. This model is useful for describing systems, such as certain Web-based data sources, which we treat as databases but cannot constrain with a schema. It’s also useful for describing interactions between databases that don’t adhere to the same schema.

Context model

This model can incorporate elements from other database models as needed. It cobbles together elements from object-oriented, semistructured, and network models.

Associative model

This model divides all the data points based on whether they describe an entity or an association. In this model, an entity is anything that exists independently, whereas an association is something that only exists in relation to something else.

The associative model structures the data into two sets:

• A set of items, each with a unique identifier, a name, and a type
• A set of links, each with a unique identifier and the unique identifiers of a source, verb, and target. The stored fact has to do with the source, and each of the three identifiers may refer either to a link or an item.

Other, less common database models include:

• Semantic model, which includes information about how the stored data relates to the real world
• XML database, which allows data to be specified and even stored in XML format
• Named graph
• Triplestore

NoSQL database models

In addition to the object database model, other non-SQL models have emerged in contrast to the relational model:

graph database model

Multivalue model, document model, databases on the web.

Most websites rely on some kind of database to organize and present data to users. Whenever someone uses the search functions on these sites, their search terms are converted into queries for a database server to process. Typically, middleware connects the web server with the database.

Helpful Resources

• Database Structure and Design Tutorial

When you’re ready to start modelling your database, try Lucidchart. It’s the perfect tool for sketching out database systems quickly and easily. You can even import and export from SQL. Start your free trial today!

Entity-Relationship Model

• A database can be modeled as:
• a collection of entities,
• relationship among entities.
• An entity is an object that exists and is distinguishable from other objects.
• Example: specific person, company, event, plant
• Entities have attributes
• Example: people have names and addresses
• An entity set is a set of entities of the same type that share the same properties.
• Example: set of all persons, companies, trees, holidays

Entity Sets instructor and student

instructor_ID instructor_name student-ID student_name

Relationship Sets

• A relationship is an association among several entities

Example:� 44553 (Peltier ) advisor 22222 ( Einstein) � student entity relationship set instructor entity

• A relationship set is a mathematical relation among n ≥ 2 entities, each taken from entity sets

{( e 1 , e 2 , … e n ) | e 1 ∈ E 1 , e 2 ∈ E 2 , …, e n ∈ E n }��where ( e 1 , e 2 , …, e n ) is a relationship

(44553,22222) ∈ advisor

Relationship Set advisor

• An attribute can also be property of a relationship set.
• For instance, the advisor relationship set between entity sets instructor and student may have the attribute date which tracks when the student started being associated with the advisor

Degree of a Relationship Set

• binary relationship
• involve two entity sets (or degree two).
• most relationship sets in a database system are binary.
• Relationships between more than two entity sets are rare. Most relationships are binary.
• Example: students work on research projects under the guidance of an instructor .
• relationship proj_guide is a ternary relationship between instructor, student, and project
• An entity is represented by a set of attributes, that is descriptive properties possessed by all members of an entity set.

instructor = ( ID, name, street, city, salary ) � course= ( course_id, title, credits )

• Domain – the set of permitted values for each attribute
• Attribute types:
• Simple and composite attributes.
• Single-valued and multivalued attributes
• Example: multivalued attribute: phone_numbers
• Derived attributes
• Can be computed from other attributes
• Example: age, given date_of_birth

Composite Attributes

Mapping Cardinality Constraints

• Express the number of entities to which another entity can be associated via a relationship set.
• Most useful in describing binary relationship sets.
• For a binary relationship set the mapping cardinality must be one of the following types:

One to many

Many to one

Many to many

Mapping Cardinalities

Note: Some elements in A and B may not be mapped to any

elements in the other set

• A super key of an entity set is a set of one or more attributes whose values uniquely determine each entity.
• A candidate key of an entity set is a minimal super key
• ID is candidate key of instructor
• course_id is candidate key of course
• Although several candidate keys may exist, one of the candidate keys is selected to be the primary key .

Keys for Relationship Sets

• The combination of primary keys of the participating entity sets forms a super key of a relationship set.
• ( s_id, i_id ) is the super key of advisor
• NOTE: this means a pair of entity sets can have at most one relationship in a particular relationship set.
• Example: if we wish to track multiple meeting dates between a student and her advisor, we cannot assume a relationship for each meeting. We can use a multivalued attribute though
• Must consider the mapping cardinality of the relationship set when deciding what are the candidate keys
• Need to consider semantics of relationship set in selecting the primary key in case of more than one candidate key

Redundant Attributes

• Suppose we have entity sets
• instructor , with attributes including dept_name

and a relationship

• inst_dept relating instructor and department
• Attribute dept_name in entity instructor is redundant since there is an explicit relationship inst_dept which relates instructors to departments
• The attribute replicates information present in the relationship, and should be removed from instructor
• BUT: when converting back to tables, in some cases the attribute gets reintroduced, as we will see.

E-R Diagrams

• Rectangles represent entity sets.
• Diamonds represent relationship sets.
• Attributes listed inside entity rectangle
• Underline indicates primary key attributes

Entity With Composite, Multivalued, and Derived Attributes

Relationship Sets with Attributes

Cardinality Constraints

• We express cardinality constraints by drawing either a directed line (→), signifying “one,” or an undirected line (—), signifying “many,” between the relationship set and the entity set.
• One-to-one relationship:
• A student is associated with at most one instructor via the relationship advisor
• A student is associated with at most one department via stud_dept

One-to-One Relationship

• one-to-one relationship between an instructor and a student
• an instructor is associated with at most one student via advisor
• and a student is associated with at most one instructor via advisor

One-to-Many Relationship

• one-to-many relationship between an instructor and a student
• an instructor is associated with several (including 0) students via advisor
• a student is associated with at most one instructor via advisor,

Many-to-One Relationships

• In a many-to-one relationship between an instructor and a student,
• an instructor is associated with at most one student via advisor ,
• and a student is associated with several (including 0) instructors via advisor

Many-to-Many Relationship

• An instructor is associated with several (possibly 0) students via advisor
• A student is associated with several (possibly 0) instructors via advisor

Participation of an Entity Set in a Relationship Set

• Total participation (indicated by double line): every entity in the entity set participates in at least one relationship in the relationship set
• E.g., participation of section in sec_course is total
• every section must have an associated course
• Partial participation: some entities may not participate in any relationship in the relationship set
• Example: participation of instructor in advisor is partial

E-R Diagram with a Ternary Relationship

Weak Entity Sets

• An entity set that does not have a primary key is referred to as a weak entity set .
• The existence of a weak entity set depends on the existence of a identifying entity set
• It must relate to the identifying entity set via a total, one-to-many relationship set from the identifying to the weak entity set
• Identifying relationship depicted using a double diamond
• The discriminator ( or partial key) of a weak entity set is the set of attributes that distinguishes among all the entities of a weak entity set.
• The primary key of a weak entity set is formed by the primary key of the strong entity set on which the weak entity set is existence dependent, plus the weak entity set’s discriminator.
• We underline the discriminator of a weak entity set with a dashed line.
• We put the identifying relationship of a weak entity in a double diamond.
• Primary key for section – ( course_id, sec_id, semester, year )
• Note: the primary key of the strong entity set is not explicitly stored with the weak entity set, since it is implicit in the identifying relationship.
• If course_id were explicitly stored, section could be made a strong entity, but then the relationship between section and course would be duplicated by an implicit relationship defined by the attribute course_id common to course and section

E-R Diagram for a University Enterprise

Reduction to Relational Schemas

Reduction to Relation Schemas

• Entity sets and relationship sets can be expressed uniformly as relation schemas that represent the contents of the database.
• A database which conforms to an E-R diagram can be represented by a collection of schemas.
• For each entity set and relationship set there is a unique schema that is assigned the name of the corresponding entity set or relationship set.
• Each schema has a number of columns (generally corresponding to attributes), which have unique names.

Representing Entity Sets With Simple Attributes

• A strong entity set reduces to a schema with the same attributes� student( ID , name, tot_cred)
• A weak entity set becomes a table that includes a column for the primary key of the identifying strong entity set � section ( course_id, sec_id, sem, year )

Representing Relationship Sets

• A many-to-many relationship set is represented as a schema with attributes for the primary keys of the two participating entity sets, and any descriptive attributes of the relationship set.
• Example: schema for relationship set advisor

advisor = ( s_id, i_id )

Redundancy of Schemas

• Many-to-one and one-to-many relationship sets that are total on the many-side can be represented by adding an extra attribute to the “many” side, containing the primary key of the “one” side
• Example: Instead of creating a schema for relationship set inst_dept , add an attribute dept_name to the schema arising from entity set instructor
• For one-to-one relationship sets, either side can be chosen to act as the “many” side
• That is, extra attribute can be added to either of the tables corresponding to the two entity sets
• If participation is partial on the “many” side, replacing a schema by an extra attribute in the schema corresponding to the “many” side could result in null values
• The schema corresponding to a relationship set linking a weak entity set to its identifying strong entity set is redundant.
• Example: The section schema already contains the attributes that would appear in the sec_course schema

Composite and Multivalued Attributes

• Composite attributes are flattened out by creating a separate attribute for each component attribute
• Example: given entity set instructor with composite attribute name with component attributes first_name and last_name the schema corresponding to the entity set has two attributes name_first_name and name_last_name
• Prefix omitted if there is no ambiguity
• Ignoring multivalued attributes, extended instructor schema is
• instructor(ID, � first_name, middle_initial, last_name,� street_number, street_name, � apt_number, city, state, zip_code, � date_of_birth)
• A multivalued attribute M of an entity E is represented by a separate schema EM
• Schema EM has attributes corresponding to the primary key of E and an attribute corresponding to multivalued attribute M
• Example: Multivalued attribute phone_number of instructor is represented by a schema:� inst_phone= ( ID , phone_number )
• Each value of the multivalued attribute maps to a separate tuple of the relation on schema EM
• For example, an instructor entity with primary key 22222 and phone numbers 456-7890 and 123-4567 maps to two tuples: � (22222, 456-7890) and (22222, 123-4567)

Multivalued Attributes

• Special case:entity time_slot has only one attribute other than the primary-key attribute, and that attribute is multivalued
• Optimization: Don’t create the relation corresponding to the entity, just create the one corresponding to the multivalued attribute
• time_slot ( time_slot_id, day, start_time , end_time )
• Caveat: time_slot attribute of section ( from sec_time_slot ) cannot be a foreign key due to this optimization

Design Issues

• Use of entity sets vs. attributes � � ���
• Use of phone as an entity allows extra information about phone numbers (plus multiple phone numbers)
• Use of entity sets vs. relationship sets � Possible guideline is to designate a relationship set to describe an action that occurs between entities
• Binary versus n-ary relationship sets � Although it is possible to replace any nonbinary ( n -ary, for n > 2) relationship set by a number of distinct binary relationship sets, a n -ary relationship set shows more clearly that several entities participate in a single relationship.
• Placement of relationship attributes

e.g., attribute date as attribute of advisor or as attribute of student

Data Model Diagram

What do you think of this template.

Product details

Data modelling is the process of producing a diagram of relationships between various types of information that are to be stored in a database that helps us to think systematically about the key data points to be stored and retrieved, and how they should be grouped and interrelated.

A data model describes information in a systematic way that allows it to be stored and retrieved efficiently in a Relational Database System which can be thought of as a way of translating the logic of accurately describing things in the real world and the relationships between them into rules that can be followed and enforced by computer code. One of the goals of data modelling is to create the most efficient method of storing information while still providing for complete access and reporting.

Data models provide a blueprint for designing a new database or reengineering a legacy application. Overall, data modelling helps an organization use its data effectively to meet business needs for information.

Data modelling is a core data management discipline. By providing a visual representation of data sets and their business context, it helps pinpoint information needs for different business processes. It then specifies the characteristics of the data elements that will be included in applications and in the database or file system structures used to process, store and manage the data.

Data modelling can also help establish common data definitions and internal data standards, often in connection with data governance programs. In addition, it plays a big role in data architecture processes that document data assets, map how data moves through IT systems and create a conceptual data management framework. Data models are a key data architecture component, along with data flow diagrams, architectural blueprints, a unified data vocabulary and other artifacts.

Well-designed data models help an organization develop and implement a data strategy that takes full advantage of its data. Effective data modelling also helps ensure that individual databases and applications include the right data and are designed to meet business requirements on data processing and management.

This template will primarily be useful to IT companies in organizing the process of storing and processing data. You can use the slides in this template to present data types, storage methods, and processing in a simple and easy-to-understand manner.

Also, this template can be used by data scientists when preparing measures to improve secure storage and increase the speed of data processing. Programmers can use this template when preparing information for the client about the principle of storing data on cloud servers.

Data Modeling Diagram is a professional and modern template that contains four stylish and fully editable slides. If necessary, you can change all elements of the slide in accordance with your corporate requirements. This template will be useful for heads of IT companies, data scientists, business analysts, programmers. The Data Modeling Diagram template will seamlessly complement your presentations and will be a great addition to your collection of professional presentations.

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Database Model

Nov 11, 2019

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Database Model. does OpenEdge applications even need one?. Marian Edu, Acorn IT. About me. Working with Progress since 1998 4GL & OO, AppServer Architecture & Integration Java, .NET node.js, javascript, typescript angular, nativescript. Zamolxis – The ERD for OpenEdge. acorn.ro.

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Database Model does OpenEdge applications even need one? Marian Edu, Acorn IT

About me Working with Progress since 1998 4GL & OO, AppServer Architecture & Integration Java, .NET node.js, javascript, typescript angular, nativescript Zamolxis – The ERD for OpenEdge acorn.ro

Database Model A database model shows the logical structure of a database, including the relationships and constraints that determine how data can be stored and accessed. Zamolxis – The ERD for OpenEdge acorn.ro

Types • Hierarchical • Relational • Network • Object-oriented • Entity-relationship • Document • Star schema Zamolxis – The ERD for OpenEdge acorn.ro

Hierarchical Tree-like structure, each child record has only one parent, whereas each parent record can have one or more child records. Zamolxis – The ERD for OpenEdge acorn.ro

Network A flexible way of representing objects and their relationships. Its distinguishing feature is that the schema, viewed as a graph in which object types are nodes and relationship types are arcs, is not restricted to being a hierarchy. Zamolxis – The ERD for OpenEdge acorn.ro

Relational A structure consistent with first-order predicate logic, first described in 1969 by Edgar F. Codd, where all data is represented in terms of tuples, grouped into relations. Zamolxis – The ERD for OpenEdge acorn.ro

Object Oriented A collection of objects, or reusable software elements, with associated features and methods. Any object to link to any other object. It’s useful for organizing lots of disparate data. Zamolxis – The ERD for OpenEdge acorn.ro

Entity Relationship Captures the relationships between real-world entities much like the network model, but it isn’t as directly tied to the physical structure of the database. Instead, it’s often used for designing a database conceptually. Zamolxis – The ERD for OpenEdge acorn.ro

OpenEdge (R)DBMS • OLTP • Relational (well, almost) • Normalization (3NF) • No formal foreign-key support • Triggers Zamolxis – The ERD for OpenEdge acorn.ro

Normalization Edgar Codd proposed the theory of normalization with the introduction of First Normal Form, and he continued to extend theory with Second and Third Normal Form. Later he joined with Raymond F. Boyce to develop the theory of Boyce-Codd Normal Form. Zamolxis – The ERD for OpenEdge acorn.ro

1NF • Each table cell should contain a single value. • Each record needs to be unique. Zamolxis – The ERD for OpenEdge acorn.ro

What is a KEY? • A KEY is a value used to identify a record in a table uniquely. • A KEY could be a single column or combination of multiple columns. Zamolxis – The ERD for OpenEdge acorn.ro

Primary KEY • A primary key value cannot be NULL • A primary key value must be unique • The primary key values cannot be changed • The primary key must be given a value when a new record is inserted Zamolxis – The ERD for OpenEdge acorn.ro

2NF • Be in 1NF • Single Column Primary Key Zamolxis – The ERD for OpenEdge acorn.ro

Foreign KEY • Foreign Key references the primary key of another Table • It helps connect database tables (relations) • It ensures rows in one table have corresponding rows in another (parent) • Unlike the Primary key, they do not have to be unique. Most often they aren’t • Can be null even though primary keys can not Zamolxis – The ERD for OpenEdge acorn.ro

3NF • Be in 2NF • Has no transitive functional dependencies Zamolxis – The ERD for OpenEdge acorn.ro

What is a Data Model? Data Model is an abstract representation of an aspect of real world in the form of a list of data elements and their relationships. Data may be modeled using various techniques - Entity-Relationship Diagrams in relational modeling, UML Class Diagrams in object-oriented modelling, Data Dictionaries, Dimensional Modeling, to name a few. Zamolxis – The ERD for OpenEdge acorn.ro

Data Dictionary Data Dictionary is a list of data elements (entity/table and attribute/column) with their attributes and descriptions. It has a form of a set of tables. Zamolxis – The ERD for OpenEdge acorn.ro

Data Dictionary • Pros • May include many data attributes (e.g. default values, scale/precision, etc.) • Includes detailed descriptions of each element (table, column) • Easily searchable • Cons • Less visually appealing • More difficult to read Zamolxis – The ERD for OpenEdge acorn.ro

Entity Relationship Diagram ER Diagram is a graphical representation of a data model using entities, their attributes and relationships between those entities. It has a form of a diagram. Zamolxis – The ERD for OpenEdge acorn.ro

Entity Relationship Diagram • Pros • Easier to see the big picture • Easier to understand table relations • Possible to use visual cues to communicate information • Cons • Doesn't work with large data models due to space constraints and clutter • Supports limited amount of details • May contain very little descriptions (as notes on a diagram) • Requires careful layout and fitting into canvas • Hard to search Zamolxis – The ERD for OpenEdge acorn.ro

Why not best of both? Zamolxis – The ERD for OpenEdge acorn.ro

Zamolxis • Entity Relationship Diagram designer for OpenEdge • Eclipse based • OpenEdge Data Definition Editor (DSL) • Integrated with PSDOE • Import/export data definition/project database • Model base compare (diagram, definition file) • Incremental data definition Zamolxis – The ERD for OpenEdge acorn.ro

Database Objects • Database(s) • Areas • Sequences • Tables • Fields • Triggers • Relations Zamolxis – The ERD for OpenEdge acorn.ro

Properties • Supports all OpenEdge properties (area, table, sequence, field, index) • Even extended properties (schema holder, misc) • Grouped by category (or not) • Lists (data type, code page, collation) • References (areas) • Complex (triggers, sequences, index fields) Zamolxis – The ERD for OpenEdge acorn.ro

Properties Zamolxis – The ERD for OpenEdge acorn.ro

Can you see the forest? Zamolxis – The ERD for OpenEdge acorn.ro

Partitions • Split large data models • Create ‘Views’ as needed • Physical or Logical • Can be nested • Import ‘Shortcuts’ • Related tables (parent, child) • Even from different model Zamolxis – The ERD for OpenEdge acorn.ro

Partitions Zamolxis – The ERD for OpenEdge acorn.ro

Data Dictionary Views View data in more details, select properties of interest, sort order. Zamolxis – The ERD for OpenEdge acorn.ro

Wizards Wizards for quickly adding/editing database elements. Zamolxis – The ERD for OpenEdge acorn.ro

Model Compare Visualize model differences – diagram, data definition, project database, versioning. Synchronize or create incremental definition file. Zamolxis – The ERD for OpenEdge acorn.ro

PSDOE Integration • Import from project’s connected database(s) • Sync with project’s connected database(s) • Create and load incremental data definition Zamolxis – The ERD for OpenEdge acorn.ro

Model Validation Basic validation rules to help with most common design flows. Zamolxis – The ERD for OpenEdge acorn.ro

Templates Define reusables ‘template’ tables with common fields. Zamolxis – The ERD for OpenEdge acorn.ro

Zamolxis http://zamolxis.acorn.ro/update/

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• Preferences

Hierarchical Database Model - PowerPoint PPT Presentation

Hierarchical Database Model

Hierarchical database model. logically represented by an upside down tree ... each child has only one parent. figure 1.8. hierarchical database model. advantages ... – powerpoint ppt presentation.

• Logically represented by an upside down tree
• Each parent can have many children
• Each child has only one parent
• Conceptual simplicity
• Database security and integrity
• Data independence
• Disadvantages
• Complex implementation
• Difficult to manage and lack of standards
• Lacks structural independence
• Applications programming and use complexity
• Implementation limitations
• Each record can have multiple parents
• Composed of sets
• Each set has owner record and member record
• Member may have several owners
• Handles more relationship types
• Data access flexibility
• Promotes database integrity
• Conformance to standards
• System complexity
• Lack of structural independence

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Modules Overview

After completing this lesson, you will be able to describe SAP Integrated Business Planning for Supply Chain modules .

SAP IBP is a world class cloud-solution based on SAP HANA technology. The figure shows the different modules within the SAP IBP Landscape and the processes they support. The individual functionalities come together to support the Sales and Operations Planning processes. The top most layer is the visualization layer to get a high-level view of the health and status of the Supply Chain of an organization.

SAP IBP Modules' Functionality

SAP IBP's Modules cover the core Supply Chain Planning functionality in one integrated platform.

Let’s invest some time to carefully read through the highlights of each SAP IBP module and how it contributes to the Sales and Operations Planning process.

SAP IBP is a modern and flexible planning tool to enable businesses to cope with market volatility and complexity.

SAP IBP is a completely scalable solution with a unified data model. It offers simplified data integration to and from SAP IBP with an easy implemented concept via Microsoft Excel and the Web-based user interfaces. It offers easy collaboration within and between internal groups such as those created by Demand and Supply Planners, Finance team members, and Sales relevant employees. It provides what-if scenario capabilities and real-time Analytics to show the status of the key performance indicators of a Supply Chain at any given time.

SAP IBP Architecture

SAP IBP is a cloud-based solution running on SAP HANA. A user can access this solution via Excel, Web, and Mobile.

Harmonized Data Model

One harmonized data model facilitates your organization's planning.

All Time Series modules are run on the same data model with no need for data transformations. This is a key enabler for more frequent and integrated Planning cycles on all levels and across all areas.

A harmonized data model requires an unified user experience. Therefore, SAP IBP has user interfaces as Web-based and Excel-based. In addition, SAP IBP Solution can be accessed via Mobile. These user interfaces constitutes an intuitive and an unified user experience for all users, including collaboration capabilities.

Log in to track your progress & complete quizzes

Sherry @ CMU

Special topic: human-centered nlp.

https://canvas.cmu.edu/courses/32856

on Canvas, Zoom Cloud Recording

2023 Spring (05-499/899)

Sherry Tongshuang Wu

Monday / Wednesday 9:30-10:50am

“HCI people design useful things that NLP people cannot build; NLP people make things that nobody uses.” (Yang et al., 2019) This course aims to help students develop the mindsets and skills necessary to build useful NLP systems, by exploring the intersection between HCI and NLP. The course will discuss the strengths and weaknesses of the status quo NLP techniques in interactive scenarios, as well as ways to integrate humans into designing, developing, and evaluating NLP resources, models, and systems. Importantly, it will highlight topics shared between HCI and NLP (data curation, model interpretability, etc.) and reflect on how the two communities approach similar topics differently.

The primary goal of the course is offer an overview of HCI+NLP, and to help students get access to, and understand, both HCI and NLP research papers and methods. The course will be half lecture and half seminar style – every 1-2 weeks, students will sign up to lead the discussion of certain given papers.

Coursework includes lectures, paper readings, class presentations, and group projects; It will not contain exams.

Schedule and Readings

This schedule is tentative and subject to changes.

Additional course information available on Canvas .

Course Goals

The learning goals of the course are as follows:

• To introduce basic concepts of NLP and HCI, to the extent that students can read research papers in the relevant field.
• To introduce a variety of emerging topics related to human-centered NLP.
• To introduce practical tools for building & analyzing, NLP models, and building interactive systems.

Notice that this new course is mostly designed to be a graduate-level, semi-seminar-style course for students interested in HCI+NLP research. This means:

• The course is largely project-oriented, and does not involve exames.
• A large proportion of your grade will depend on research paper reading and digestion.
• This is a special topic course (not well-established) and may only be offered once, so please expect some glitches in the schedule or assignments :)
• I cannot guarantee it will count as a part of a technical requirement.

Prerequisites

There is no explicit prerequisite; However, students are expected to (1) be proficient in Python (for completing assignments), and (2) know basic ML concept — To the extent that you understand concepts like train/dev/test set, model fitting, feature, supervised learning, etc. (We will not cover these in this course!)

If you are familiar with NLP and relevant programming libraries (e.g. HuggingFace), you might find certain parts of the course introducing NLP concepts significantly easier (or, unnecessary :D).

Course Materials and Communications

• Slides and readings will be on this page.
• Assignments and discussions will be on Canvas when their time comes. The course webpage will have links that direct you to them.
• If you have questions related to course materials or logistics, please post on Canvas Discussion page, so either the instructor or other students can help answer that (I will check Discussion regularly).
• If you have special requests, please email the instructor at [email protected] .
• This is an in-person class by default, but if you cannot come, there’s a zoom option on Canvas.

Major Research Work

The tentative breakdown for grading is below. As a reminder, here is the university policy on academic integrity . See major course work

• 30% two assignments
• 6% two peer grading
• 5% Form group + short project description
• 10% Midterm presentation
• 15% Final presentation
• 10% Project report
• 14% Paper presentation
• 10% Participation and Discussions (Through discussions on Canvas)

Late Day Policy : Attending class in person and submitting course deliverables on time is critical. However, we realize that things happen, and that you might sometimes not be able to turn in your assignments. To accommodate this, you will each receive 4 free late days. Beyond those days, you receive a 5% penalty for each day late. You are welcome to budget late days as you like for the two assignments, two grading deadlines, and the final report.

Project presentation, paper presentation, and paper discussions cannot use late days as they are time sensitive; Final project report cannot use more than two late day as it affects grade submission.

Assignments

Model Evaluation ( link to opensourced repo ). We will evaluate existing Huggingface models with a model testing framework called CheckList. Hopefully the assignment will help you get familiar with the basic programming environment setup for playing with NLP models locally, and the concept of model evaluation.

Prompting via Crowdsourcing Strategies . We will apply decades of research findings in Crowdsourcing instructions to LLM prompting. we will select one paper in Crowdsourcing, replicate the idea by writing prompts to instruct different LLM modules as if they are crowdworkers, see whether the crowdsourcing task design strategies can transfer to LLM prompting. Hopefully the assignment will help you get familiar with the concept of LLM prompting, the OpenAI interface, and some crowdsourcing techniques.

Each assignment will have 100 points, and will be peer-graded:

• A base grade up to 80. I will try my best to make this easy to get, as long as you follow the instructions. If you find the assignment difficult, there will also be some ways to earn partial credits.
• A peer-graded point, up to 20. Three students will rate your assignment on some pre-set Likert Scale (e.g. 1 being poorly constructed 5 being very impressive), and your score depends on the relative ranking of averaged your test score usefulness. you will get {20, 15, 10, 5} scores if your averaged score is ranked top {25%, 50%, 75%, 100%} percent respectively.

We will provide more instructions, grading details, and the starter code repo through GitHub Classroom. More details will be posted on Canvas once the assignments are released.

Assignment Peer Grading

You will be assigned to grade other students’ assignments. This is a way for you to see how others think about a given task. You will get full grades for peer gradings unless:

• You do not complete them or submit them late.
• Your score deviates from other people’s rating drastically without justified reasoning (e.g. everyone gave 5 but you gave 1 because you wanted higher ranking).

This is how we will see annotator disagreement in real time :)

Presentations and Discussions

A large proportion of your grade will depend on reading and digesting papers from top HCI or NLP venues. The grading will split into two parts:

Paper presentation . The reading lectures will be led by students. 2-3 students will sign up to lead the same session, prepare the slides deck and discuss multiple aspects of the paper. To achieve deep paper digestion, you can take inspirations from the role playing model of Jacobson and Raffel. No need to pick explicit roles, just cover relevant discussion points.

Earn participation scores through discussions . Before each reading lecture, we will open corresponding discussion threads on Canvas. Students not leading the session are expected to participate submit comments on those required readings on Canvas. This is how you earn participation scores! Good comments typically exhibit one or more of the following:

• Critiques of arguments made in the papers
• Analysis of implications or future directions for work discussed in lecture or readings
• Clarification of some point or detail presented in the class
• Insightful questions about the readings or answers to other people’s questions
• Links to web resources or examples that pertain to a lecture or reading

Final Project

The most substantial portion of your coursework is a team-based project (2-4 people). You will self-propose a project broadly relevant to HCI+NLP, with four milestones (they will be posted on Canvas when the time comes):

Form research group + topic selection . You will fill in a short Google Form that documents your group members, and a general description of your project. This will act as a forcing function for you to start think about the project. In the form, you will mostly address these questions: 1 (what are you trying to do), 2 (how is it done today), 3 (what’s new), 4 (who cares), 5 (your proposed method), and 6 (metrics of success). If you are looking for project partners, please post to Canvas!

Midterm presentation + peer feedback . Shortly after the Spring Break, Each group will do a 7-8 minute in-class presentation on the project progress, so the instructor and other students can provide feedback.

Final presentation . Each group will do a 7-8 minute in-class presentation on the final project result. This will be similar to the midterm presentation.

Final report . Each group will also submit a 4-8 page final report (not counting references) written in the form of a conference paper submission. The paper might include content that is typical of papers that appear at ACL or CHI.

Special Thanks!

• Thanks to Diyi Yang who also created a Human-Centered NLP course at Stanford during a similar time. She taught me the importance of interchanging lectures with reading assignments, helped me revise the course syllabus, and pointed me to awesome resources for various topics.
• Thanks to Haiyi Zhu for inspiring me on the course assignment design!
• Thanks to Jay Alammar and Huggingface tutorial whose interactive visualization on Transformers, interpretability, etc. helped ground the modeling part of the course.
• Lectures on many topics were built upon amazing conference tutorials. Thanks to the organizers for their thorough reflection on specific topics, and for allowing me to borrow the materials. Most noteably: EMNLP 2020: Interpreting Predictions of NLP Models ; EMNLP 2021: Crowdsourcing Beyond Annotation: Case Studies in Benchmark Data Collection ; NAACL 2022: Human-centered Evaluations of Explanations .
• Thanks to the guest speakers for giving expert-covers on several topics. At one point, I’m gonna do a all-guest-speaker course – I can very easily think of someone more knowledgeable than me on every topic!
• I also borrowed specific materials here and there from various other courses and online blog posts. I tried my best to credit everyone in slides. Thank you!

Other Information

Respect for diversity.

It is our intent that students from all diverse backgrounds and perspectives be well served by this course, that students’ learning needs be addressed both in and out of class, and that the diversity that students bring to this class be viewed as a resource, strength and benefit. It is our intent to present materials and activities that are respectful of diversity: gender, sexuality, disability, age, socioeconomic status, ethnicity, race, and culture. Your suggestions are encouraged and appreciated. Please let us know ways to improve the effectiveness of the course for you personally or for other students or student groups. In addition, if any of our class meetings conflict with your religious events, please let us know so that we can make arrangements for you.

Accommodations for Students with Disabilities

If you have a disability and are registered with the Office of Disability Resources, we encourage you to use their online system to notify us of your accommodations and discuss your needs with us as early in the semester as possible. We will work with you to ensure that accommodations are provided as appropriate. If you suspect that you may have a disability and would benefit from accommodations but are not yet registered with the Office of Disability Resources, we encourage you to contact them at [email protected] .

Health and Well-being

If you are experiencing COVID-like symptoms or have a recent COVID exposure, do not attend class if we are meeting in-person. Please email the instructors for accomodations.

If you or anyone you know experiences any academic stress, difficult life events, or feelings like anxiety or depression, we strongly encourage you to seek support. Counseling and Psychological Services (CaPS) is here to help; call 412-268-2922 and visit their website at www.cmu.edu/counseling/ . Consider reaching out to a friend, faculty or family member you trust for help getting connected to the support that can help. If you or someone you know is feeling suicidal or in danger of self-harm, call someone immediately, day or night:

• CaPS: 412-268-2922
• Re:solve Crisis Network: 888-796-8226

If the situation is life threatening, call the police. On campus call CMU Police: 412-268-2323 . Off campus: 911 .

If you have questions about this, please let the instructors know. Thank you, and have a great semester.

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Price match only available for newest-generation products in the same comparable geographic region only. Please note that not all services are included and there might be minor pricing variations.  Pricing adjustments in response to price drops by AWS might cause slight delays. [2] Customers may see savings estimated to be between 11 percent and 65 percent. The 65 percent savings is based on one M64dsv2 Azure VM for CentOS or Ubuntu Linux in the East US region running for 36 months at a pay-as-you-go rate of ~$4,868.37/month vs. a reduced rate for a 3-year savings plan of ~$1,703.44/month. Based on Azure pricing as of October 2022. Prices subject to change. Actual savings may vary based on location, instance type, or usage. [3] Savings based on sample of VMs across variety of regions including Dv4 in West Europe. Savings are calculated from full price (license included) for SQL Server Enterprise edition VM against reduced rate (applying Azure Hybrid Benefit for SQL Server and Windows Server), which excludes Software Assurance costs for SQL Server and Windows Server. Actual savings may vary based on region, instance size, compute family, and EA agreement. Prices as of January 2023, subject to change.

ESG Sentiment Analysis Using AI

Miguel Nunez

Aug 13, 2024, 8:14 PM

AllianceBernstein – Leveraging AI for ESG Sentiment Analysis

In collaboration with Vanderbilt Data Science, AllianceBernstein undertook a project aimed at analyzing the impact of Environmental, Social, and Governance (ESG) sentiment on stock prices. The project sought to determine whether negative sentiments surrounding ESG topics could influence the financial performance of companies. By utilizing advanced Natural Language Processing (NLP) models, the project provided deep insights that could help guide sustainable investment strategies at AllianceBernstein.

ESG Sentiment Analysis Presentation

The project outcomes were presented by a team of data science students who demonstrated the sophisticated methods used to extract and analyze ESG sentiment from a vast dataset of over 120,000 news articles. This detailed analysis offered AllianceBernstein valuable insights into how ESG factors influence market perceptions and, subsequently, stock performance.

Project Highlights:

Several stages of data collection, model development, and analysis were integral to the project’s completed state. The students focused on multiple aspects of ESG sentiment analysis, including:

• Purpose: To assess how negative ESG sentiment in news articles correlates with potential declines in stock returns, providing actionable insights for making informed investment decisions.
• Data Collection: The team collected and processed a dataset of 120,000 news articles, selecting 5,400 articles for manual annotation and using advanced models to label the rest based on ESG relevance and sentiment.
• Techniques: Various NLP models, including FinBERT and Llama 2, were employed to classify ESG sentiment. The team also explored cutting-edge models like GPT-4 for enhanced performance.
• Applications: The findings led to the development of a predictive model that helps AllianceBernstein identify investment risks and opportunities based on ESG sentiment.

Methodological Approach:

In order to ensure the reliability and accuracy of the results, the project team employed a rigorous methodological approach:

• Information Retrieval: Using models like BM25, Two Towers, and ColBERT, the team refined the process of retrieving relevant ESG news articles, achieving an impressive F1-score of 84.2% with the fine-tuned ColBERT model.
• Sentiment Classification: The sentiment of ESG-related news articles was classified using state-of-the-art models. Fine-tuning models like FinBERT significantly improved their performance, enabling precise sentiment analysis.
• Causal Analysis: Rigorous experiments, including regression analysis and Monte Carlo simulations, were conducted to establish a causal relationship between negative ESG sentiment and stock returns.
• Strategy Development: The team developed a long-short investment strategy based on sentiment scores, which was thoroughly tested using quantitative analysis and visualization techniques.

Session Insights:

The analysis provided several critical insights for AllianceBernstein’s investment strategies:

• Negative ESG sentiment demonstrated as a strong indicator of potential declines in stock returns, particularly over short-term periods.
• The sentiment-based trading strategy developed from the analysis showed promising results, especially in exploiting extremes in sentiment scores.
• The findings underscore the importance of integrating ESG factors into investment decisions to mitigate risks and capitalize on opportunities.

Advanced Model Performance:

The project explored and compared the performance of several advanced NLP models in analyzing ESG sentiment:

• FinBERT: Fine-tuning improved both precision and recall, making FinBERT a reliable model for ESG sentiment classification.
• GPT-4 vs. GPT-3.5: While GPT-4 offered the highest performance, GPT-3.5 provided a more cost-effective solution with negligible impact on accuracy.
• ColBERT: The fine-tuned ColBERT model outperformed other models in information retrieval tasks, making it the preferred choice for identifying ESG-relevant news articles.

Project Data:

• Data Volume: The project processed and analyzed over 120,000 news articles.
• Key Metrics: Achieved an F1-score of 84.2% in information retrieval tasks, with sentiment classification models showing high precision and recall.
• Visualization Techniques: Multiple visualizations were used to depict the relationship between ESG sentiment and stock performance, providing clear insights for decision-making.

AI-driven sentiment analysis, developed through the work of AllianceBernstein and Vanderbilt Data Science, is refining investment strategies through focused assessment of ESG factors. The project provided actionable insights and highlighted the growing importance of sustainable and conscious investing in today’s financial markets.

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Introduction to Database Management System

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Introduction to Databases

1 Minggu 1, Pertemuan 1 Introduction to Database Matakuliah: T0206-Sistem Basisdata Tahun: 2005 Versi: 1.0/0.0.

Data Management I DBMS Relational Systems. Overview u Introduction u DBMS –components –types u Relational Model –characteristics –implementation u Physical.

©Silberschatz, Korth and Sudarshan1.1Database System Concepts Chapter 1: Introduction Purpose of Database Systems View of Data Data Models Data Definition.

Introduction to Databases Transparencies

Chapter 1 INTRODUCTION TO DATABASE.

Dr. Kalpakis CMSC 461, Database Management Systems Introduction.

Chapter 1 Introduction to Databases

Database Design and Introduction to SQL

Introduction to DBMS Purpose of Database Systems View of Data

Introduction to Database

DBMS By Narinder Singh Computer Sc. Deptt. Topics What is DBMS What is DBMS File System Approach: its limitations File System Approach: its limitations.

CS370 Spring 2007 CS 370 Database Systems Lecture 2 Overview of Database Systems.

Sistem Basis Data (DATABASE) Siauw Yohanes Darmawan

Module Title? DBMS Introduction to Database Management System.

Chapter 1 Introduction to Databases Pearson Education ©

Chapter 1 : Introduction §Purpose of Database Systems §View of Data §Data Models §Data Definition Language §Data Manipulation Language §Transaction Management.

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Highlights: Harris to hold rally in North Carolina, highlights from Trump’s NJ news conference

With consumer goods placed on tables near him, Republican presidential nominee former President Donald Trump speaks at a news conference at Trump National Golf Club, Thursday, Aug. 15, 2024, in Bedminster, N.J. (AP Photo/Julia Nikhinson)

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Today’s live coverage has ended. See what you missed below and find the latest on the 2024 presidential election at apnews.com.

Donald Trump held a press conference Thursday in Bedminster , New Jersey in which he said he thinks he’s “ entitled to personal attacks ” on his Democratic rival Vice President Kamala Harris, saying he’s “very angry” at her because of the criminal charges he faces.

Earlier, Harris joined President Joe Biden to announce a deal to lower drug prices , their first joint speaking appearance since she replaced him at the top of the Democratic ticket, as they both struggle to convince voters that costs will trend down after years of above-normal inflation.

What to know:

• Harris is set to unveil her economic agenda. Her upcoming speech in North Carolina will preview policies around price gouging on groceries and cutting other costs, as her campaign zeroes in on food and housing prices.
• Trump addressed inflation and the economy Thursday. The Republican presidential nominee blamed inflation directly on government spending, sidestepping some of the more complex realities of global supply chain interruptions during the pandemic and global oil price spikes after Russia invaded Ukraine.
• Vance and Walz plan to debate later this year. Minnesota Gov. Tim Walz and Ohio Sen. JD Vance have agreed to debate each other on Oct. 1, setting up a matchup of potential vice presidents as early voting in some states gets underway for the general election .

Trump warns that anti-Israel protestors will descend on next week’s Democratic National Convention in Chicago.

Trump said that “hundreds of thousands of people that are opposed to Israel” are planning to protest around the events. “There will be no jihad coming to America under Trump,” he said, to applause from the crowd.

Trump called Democrats “a radical group of people,” again criticizing Harris for not attending Netanyahu’s recent speech before a joint session of Congress, traveling to Indiana instead .

Ohio Sen. JD Vance, Trump’s running mate, also did not attend the speech, stumping in Ohio and Virginia.

Harris, however, met with the prime minister privately.

Of Trump, Adelson said there’s just one president in her lifetime that fully shares the values she’s been talking about. “That president is Donald Trump,” she said, to cheers from the gathered crowd, adding that it was a “promised kept” when Trump moved the U.S. embassy in Israel to Jerusalem, as well as recognizing the Golan Heights and withdrawing from the Iran nuclear deal.

Trump is being introduced by GOP mega-donor Miriam Adelson, an Israeli-born, naturalized U.S. citizen who earned a medical degree from Tel Aviv University and founded drug abuse treatment and research centers in Las Vegas and Tel Aviv.

In her remarks, Adelson talked about the impact of the Israel-Hamas war, saying that “Israel is being demonized,” adding that, “in too many parts of the world, it is open season against the Jews, and antisemitism becomes acceptable.” Miriam is the wife of the late Sheldon Adelson, a Las Vegas casino magnate considered one of the nation’s most powerful Republican donors, who died in 2021 .

The Adelsons donated $30 million to Trump’s campaign in the final months of the 2016 race, and Miriam Adelson is behind some major spending aimed at boosting Trump’s current campaign. Preserve America super PAC, a pro-Trump group backed by her, has reserved $45 million worth of advertising through the end of August.

As president, Trump awarded Miriam Adelson with the Presidential Medal of Freedom - the highest honor the nation reserves for a civilian — in 2018.

Trump repeats false claims that Minnesota law requires tampons in boys’ bathrooms

CLAIM: Democratic vice presidential nominee and Minnesota Gov. Tim Walz “signed a bill that all boys bathrooms in Minnesota will have tampons.”

FACTS: Walz signed a bill in 2023 that requires Minnesota public schools to make tampons available to “all menstruating students.” The law does not mention gender. The relevant section of the law reads: “A school district or charter school must provide students with access to menstrual products at no charge.

The products must be available to all menstruating students in restrooms regularly used by students in grades 4 to 12 according to a plan developed by the school district. For purposes of this section, ‘menstrual products’ means pads, tampons, or other similar products used in connection with the menstrual cycle.”

That means a school system could theoretically opt to place dispensers in all bathrooms. But there is no legal requirement that tampons be distributed in boys’ bathrooms — because a system could satisfy the law with dispensers in girls’ restrooms.

Former President Donald Trump falsely claimed he won Pennsylvania in the 2020 election. He did not.

CLAIM: “I won Pennsylvania and I did much better the second time. I won it in 2016, did much better the second time. I know Pennsylvania very well.”

THE FACTS: Trump did win the state in 2016, when he beat Democrat Hillary Clinton to win the presidency. But he lost the state in 2020 to President Joe Biden, a Pennsylvania native. According to the official certified results Biden and Vice President Kamala Harris received 3.46 million votes, compared to Trump and Vice President Mike Pence with 3.38 million votes.

Trump is arguing that his presidency brought more national unity than he gets credit for and that he is not actually as brash or bombastic as he often seems on the campaign trail.

“I think I’m doing a very calm campaign,” he said after being asked about some criticism from Republicans who want him to focus less on personal attacks. “Some of you will say, ‘he ranted and raved,’” Trump said, referring to journalists.

“I’m a very calm person, believe it or not,” he said.

He pointed to a criminal justice overhaul enacted when he was president. He did it with Democratic support."When you do that, that’s a big step,” he said, describing a negotiation process involving unusual allies. “Our country was coming together. Success will bring our country together again.”

Trump says he’d welcome Nikki Haley on the campaign trail if she wanted to stump for him.

Asked if he would want Haley to rally support for him, Trump said, “I’d love to have her go around and campaign.”

“I fought Nikki very hard,” Trump said. “I beat her in her own state by legendary numbers. I get along with her fine.” The former South Carolina governor, who was the last remaining GOP opponent against Trump in the primary campaign and served two years as his administration’s ambassador to the United Nations, shuttered her campaign after Trump’s big wins on Super Tuesday.

She didn’t endorse Trump immediately but has since and spoke at last month’s RNC in Milwaukee in support of his candidacy. Otherwise, she has not campaigned for him.

Trump says he feels that he’s earned the right to take on his political opponents in a personal way, citing what he termed the way the justice system has been “weaponized” against him.

It’s a theme Trump often hits in his rally speeches, citing the legal cases against him and characterizing them as politically motivated attacks by Democratic prosecutors and the Biden Justice Department.

On Thursday, asked if he felt he needed to run a more disciplined campaign in the closing months ahead of Nov. 5 and pivot away from making personal attacks on Harris, Trump said, “I think I’m entitled to personal attacks, I don’t have a lot of respect for her, I don’t have a lot of respect for her intelligence, and I think she’ll be a terrible president.”

Trump went on to say of Harris that “people don’t know who she is,” explaining that he doesn’t typically use his opponent’s last name because, “You can ask the man on the street, what’s the last name of Kamala?’ Nobody knew.”

Trump’s been encouraging supporters to vote for him early, but he again called for “one-day voting” in elections.

“We should have one-day voting, paper ballots,” Trump said, in part of a response to a question about credit card debt.

Saying that large numbers of early voting is “ridiculous,” Trump also called for “voter ID, and we should have proof of citizenship” for voting in U.S. elections.

Trump claimed the U.S. could suffer an economic collapse akin to the 1929 crash that preceded the Great Depression if Harris wins the election.

CLAIM: “We’re going to have a crash like the 1929 crash if she gets in.”

THE FACTS: Countering Trump’s gloomy predictions, the economy has shown recent signs of strength and stability. On Thursday, the S&P 500 jumped 1.6%, its sixth gain in a row. The Dow Jones Industrial Average also increased, as did the Nasdaq composite.

Recent economic reports show that shoppers increased their retail spending last month and fewer workers sought unemployment benefits.

Fears that the economy was slowing emerged last month following a sharp drop in hiring and higher unemployment rates. But those worries were assuaged earlier this month when a better-than-expected jobless numbers led to Wall Street’s best rally since 2022.

Trump was asked if he had reflected on his assassination attempt and why he felt his life had been saved.

Calling it “a miracle” that he wasn’t more seriously harmed, Trump said that “God has something to do with it. ... And maybe it’s, we want to save the world.”

At the Republican National Convention, Trump spoke in depth about the shooting and said that he wouldn’t make a habit of talking about it, although he has done so frequently at campaign events since.

Trump made it more than 45 minutes into his remarks before bringing up his grievances over the 2020 election. He again implied — falsely — that the election was rigged as he bragged about getting “millions more votes” than he got in 2016 plus “other votes that we can talk about at a different day.”

Indeed, turnout in 2020 was considerably higher than in 2016. Trump’s national vote total jumped from just shy of 63 million to 74.2 million. But Joe Biden got almost 81.3 million votes, up from Hillary Clinton’s 65.9 million votes four years earlier.

Much of Trump’s remarks have been aimed to focus on the economy, and — more than 40 minutes into his event — he made reference to the food items and household goods set up in a display behind him.

“I haven’t seen Cheerios in a long time,” Trump said, noticing the cereal box. “I’m going to take them back to my cottage.”

At about 45 minutes in, Trump started taking questions.

But there have been some stem-winding moments, reminiscent of his rallies, where remarks can run an hour and a half.

Trump is promising to eliminate all incentives and mandates that push the U.S. auto market toward electric vehicles. But he’s insistent that he does not oppose EVs — just that he wants market forces and consumer choices to guide the economy.

Trump is going after remarks that Harris made in a Senate confirmation hearing in 2018. He said that Harris had compared U.S. Immigration and Customs Enforcement agents to the Ku Klux Klan.

In 2018, Harris, then a senator, aggressively questioned acting Director Ronald D. Vitiello of ICE about a comment he had made on social media that compared the Democratic Party to the KKK.

After a heated exchange during the hearing, Harris asked Vitiello, “Are you aware that there’s a perception that ICE is administering its power in a way that is causing fear and intimidation — particularly among immigrants and specifically among immigrants coming from Mexico and Central America?”

Donald Trump is 30 minutes into what his campaign billed as a news conference, but he has yet to take questions. He stuck largely to prepared remarks on inflation but has stretched his remarks with various broadsides and anecdotes.

Trump misstated Harris’ role in overseeing border security during his press conference Thursday in Bedminster, N.J.

CLAIM: “She was the border czar but she didn’t do anything. She’s the worst border czar in history ... She was the person responsible for the border and she never went there.”

THE FACTS: Biden tapped Harris in 2021 to work with Central American countries to address the root causes of migration and the challenges it creates. Illegal crossings are one aspect of those challenges, but Harris was never assigned to the border or put in charge of the Department of Homeland Security, which oversees law enforcement at the border .

Trump is hammering Harris as a politician who wants to “defund the police.” His characterization of her previous statements is not accurate.

After the murder of George Floyd in 2020, Harris expressed praise for the “defund the police” movement and questioned whether money was being effectively spent on public safety. But she has not advocated abolishing police forces, the insinuation behind Trump’s line of attack.

Harris did an interview in June 2020 with the “Ebro in the Morning” radio show in New York noting, “This whole movement is about rightly saying, we need to take a look at these budgets and figure out whether it reflects the right priorities.” Her comments were part of a wider critique that police departments have to confront more social problems because other public programs — education, social services, mental health care — are often not funded well enough.

The Biden administration tried to overhaul policing, but the legislation stalled on Capitol Hill, and Biden ultimately settled for issuing an executive order . The administration also pumped more money into local departments.

The pivot to criticizing the Biden-Harris administration on immigration has been a theme of many of Trump’s speeches and events during this campaign cycle, and it’s one the GOP nominee is again renewing during his Bedminster remarks.

After listing off programs he said Harris would threaten as president — saying “she will destroy Medicare, she will destroy Social Security” — Trump then turned back to immigration concerns, overstating the number of people who have come across the U.S.-Mexico border during Biden’s time in office.

It’s a regular feature of modern U.S. elections for Republican nominees to cast Democratic nominees for various offices as “too liberal” or even “socialist.”

Trump has elevated that line of attack on Harris. “She wants to change a free-enterprise-type country into a communist-type country,” he insisted.

Trump’s argument depends heavily on certain positions Harris took as a U.S. senator from California and candidate for the 2020 Democratic presidential nomination. Harris once co-sponsored Sen. Bernie Sanders’ “Medicare for All” Senate proposal, but she has backed away from that since the 2020 campaign.

The Biden-Harris campaign in 2020 advocated for a “public option” plan to be added to existing private health insurance exchanges set up under the 2010 Affordable Care Act. That policy would not, as Trump claims, end private health insurance or private health care.

Trump is warning that his Democratic opponent would upend not only the U.S. economy but also American relationships abroad.

“If Kamala wins the election, the worst is yet to come,” Trump said, claiming that Harris is “far more radical” than Vermont independent Sen. Bernie Sanders and “wants to change a free enterprise type country into a communist type country.”

On foreign relations, Trump warned that “you’ll have a real problem in the future because people don’t respect her in the world.”

Turning to the grocery items that are surrounding him, Trump quoted various inflation statistics for various staples of U.S. household diets.

But he blasted Harris’ talk of taking on price gouging, including on food, calling it “communist price controls” and “the Maduro plan,” a nod to the authoritarian socialist leader of Venezuela. “She wants price controls,” he said. “They don’t work. ... They lead to food shortages.” He’s adding references to higher car insurance premiums and fuel costs.

So far, Trump has not introduced any of his proposals to confront the situation. He’s opting instead for blaming Harris, because she is Biden’s top lieutenant, and recycling his sweeping predictions that a President Harris would tank the economy.

Trump is opening his latest news conference with renewed attacks that Harris “broke the border and broke the world.”

He is highlighting her role as vice president casting the tie-breaking Senate vote on major spending initiatives for the Biden administration.

As the economy takes center stage in the presidential contest, Trump is blaming inflation directly on government spending — sidestepping some of the more complex realities of global supply chain interruptions during the pandemic and global oil price spikes after Russia invaded Ukraine.

Harris, in turn, has focused the blame on corporate greed, glossing over the effects of considerable stimulus spending by the U.S. and other governments around the world.

Trump has started a news conference outside at his Bedminster resort in New Jersey, where he is speaking surrounded by groceries set on tables.

The former president and Republican nominee is trying to step up his attacks on Vice President Harris, shifting blame for rising prices from Biden to her as she becomes her party standard-bearer.

Trump delivered what was billed as a major address on the economy a day earlier in North Carolina but he spent most of the 75-minute speech on his usual grievances. The news conference marks another opportunity for him to focus his argument on what many political observers believe can be a weak spot for Harris.

For her part, Harris is planning an address on the economy Friday in North Carolina.

At a rally yesterday in North Carolina that his campaign billed as a big economic address, Trump made little effort to stay on message. He mixed pledges to slash energy prices and “unleash economic abundance” with familiar off-script tangents.

He aired his frustration over the Democrats swapping the vice president in place of Biden at the top of their presidential ticket. He repeatedly denigrated San Francisco, where Harris was once the district attorney, as “unlivable” and went after his rival in deeply personal terms, questioning her intelligence, saying she has “the laugh of a crazy person” and musing that Democrats were being “politically correct” in trying to elevate the first Black woman and person of South Asian descent to serve as vice president.

The former president will meet members of the press at his New Jersey golf club at 4:30 p.m. ET for his second news conference in as many weeks as he adjusts to a newly energized Democratic ticket.

He’ll be flanked by popular grocery store items as he seeks to highlight the rising cost of food.

The event comes one day after the Labor Department announced year-over-year inflation reached its lowest level in more than three years in July — the latest sign that the worst price spike in four decades is fading. But consumers are still feeling the impact of higher prices and the Trump campaign is hoping to capitalize on those frustrations.

▶ Watch Trump’s New Jersey news conference live

Vice President Kamala Harris’ campaign has released a video of her chatting with her running mate, Minnesota Gov. Tim Walz, as the pair discuss their upbringings and tastes.

Running just under 10 minutes and filmed last week when the pair made a joint campaign appearance in Detroit, the video recalls a similar one then-candidate Joe Biden had with former President Barack Obama during the 2020 campaign.

Walz likens the election to a football game, saying it’s “halftime in America” and he feels like the campaign is trailing by a touchdown, adding, “I kind of like the idea of being a little bit behind.”

Harris, who’s played up her running mate’s former career coaching high school football responds, “I’m looking at Coach Walz right now.”

The vice president asks her running mate about liking what he describes as “white guy tacos,” which he says are ground beef and cheese but no sauce — prompting Harris to note that she’s grown chili peppers at the vice president’s residence.

He tells her about his love of Bruce Springsteen and Harris talks about growing up with the music of Aretha Franklin, Stevie Wonder, Miles Davis and John Coltrane and her love of Prince. She says she’s “more of a hip hop girl” while her husband, Doug Emhoff, is more partial to Depeche Mode.

Harris talks about supporting policies that don’t “let people fall through the cracks” and trying to unify rather than divide the country.

In the weeks since launching her presidential campaign, Harris hasn’t sat for a major media interview — making her chat with Walz one of the closest approximations.

Former President Donald Trump will speak at his golf club in Bedminster, New Jersey, on Thursday flanked by popular grocery store items as he seeks to highlight the rising cost of food.

Staff were seen laying out a selection of items, including tubs of instant coffee, sugary breakfast cereals, pastries and fruit, on tables behind the lectern where he’ll speak at a news conference Thursday afternoon.

The event comes one day after the Labor Department announced year-over-year inflation reached its lowest level in more than three years in July — the latest sign that the worst price spike in four decades is fading.

But consumers are still feeling the effect of higher prices — something Trump’s campaign is banking on to motivate voters this fall.

President Joe Biden and Vice President Kamala Harris have appeared together for the first time since she replaced him as the Democratic presidential nominee.

Biden and Harris spoke at an event at a Maryland community college to talk about the administration’s work to lower the cost of prescription drugs.

They also praised each other.

Harris, who spoke first, said it’s her “eternal and great honor” to serve with Biden, whom she called an “extraordinary human being and American and leader.”

Biden said of Harris, “She’s going to make one hell of a president.”

President Joe Biden and Vice President Kamala Harris arrive to speak about their administration’s efforts to lower prescription drug costs during an event at Prince George’s Community College in Largo, Md., Thursday, Aug. 15, 2024. (AP Photo/Stephanie Scarbrough)

Chicago Police Superintendent Larry Snelling is touting extensive officer training as a critical part of the city’s preparations for the Democratic National Convention next week.

Roughly 50,000 people are expected in Chicago for the convention, including thousands of anti-war activists who plan to demonstrate near the United Center where Vice President Kamala Harris will officially accept the party’s nomination.

Snelling maintained that the Chicago Police Department — working alongside federal law enforcement agencies — is prepared to deal with large crowds and any security concerns.

“There’s a possibility that things could take a turn. Something could happen that we don’t expect,” Snelling told The Associated Press on Wednesday. “We know that our officers can respond in a professional manner with training behind them. They’ll be more effective in decision making. And then the response becomes greater and better.”

FILE - Independent presidential candidate Robert F. Kennedy Jr. speaks during a campaign event, Tuesday, Nov. 14, 2023, in Columbia, S.C. Independent presidential candidate Robert F. Kennedy Jr. filed paperwork Thursday, June 20, 2024 to get on the ballot for the November election in swing-state Pennsylvania, the state’s election office said. (AP Photo/Meg Kinnard, File)

Robert F. Kennedy Jr.’s independent presidential campaign suffered a blow this week when a judge in New York invalidated his petition to put his name on the state ballot, a ruling that could potentially create problems for the candidate as he faces challenges elsewhere.

Kennedy’s attorneys filed an appeal Wednesday to a ruling this week from Justice Christina Ryba, who said the residence listed on his nominating petitions was a “sham” address he used to maintain his voter registration and to further his political aspirations. The judge ruled in favor of the challengers, who argued Kennedy’s actual residence was the home in Los Angeles he shares with his wife, the “Curb Your Enthusiasm” actor Cheryl Hines.

New York is just one of more than a half-dozen states where challenges have been made to Kennedy’s petitions from Democrats and their allies. Some of the challenges allege he falsely listed the same New York address that was the subject of litigation in that state, or that there were problems with petition signatures.

Donald Trump is asking the judge in his New York hush money criminal case to delay his sentencing until after the November presidential election.

In a letter made public Thursday, a lawyer for the former president and current Republican nominee suggested that sentencing Trump as scheduled on Sept. 18 — about seven weeks before Election Day — would amount to election interference.

Trump lawyer Todd Blanche wrote that a delay would also allow Trump time to weigh next steps after the trial judge, Juan M. Merchan, is expected to rule Sept. 16 on the defense’s request to overturn the verdict and dismiss the case because of the U.S. Supreme Court’s July presidential immunity ruling .

“There is no basis for continuing to rush,” Blanche wrote.

Republican vice presidential nominee Sen. JD Vance, R-Ohio speaks at a campaign event, Wednesday, Aug. 14, 2024, in Byron Center, Mich. (AP Photo/Carlos Osorio)

To attendees gathered at a VFW post in New Kensington on Thursday in western Pennsylvania, Vance lauded Trump for putting “in place real policies that really benefit the veterans who served in our military.”

As he opened his remarks, Vance asked attendees to hold a brief moment of silence to commemorate the deaths of 13 service members killed in a suicide bombing at Kabul’s airport as the U.S. withdrew from Afghanistan three years ago, a move by Biden he said occurred “in the most disgraceful way,” also laying blame on Harris.

Vance was introduced by several veterans now serving in Congress, including Florida Reps. Brian Mast and Mike Waltz. Pat Harrigan, a Republican running for the U.S. House in North Carolina, levied harsh criticism on Tim Walz’s record, calling the Minnesota governor “a coward who betrayed his men at their greatest time of need.”

Since Harris named Walz - a 24-year veteran of the Army National Guard - as her running mate, Vance has repeatedly questioned his rival’s military credentials, accusing him of retiring to avoid deploying with his unit and saying Walz misled voters about serving in a combat zone, when he was deployed to Italy in 2003 in a supportive, not combat, role.

As he ramped up for a congressional bid in 2005, Walz’s campaign in March issued a statement saying he still planned to run despite a possible mobilization of Minnesota National Guard soldiers to Iraq. According to the Guard, Walz retired from service in May of that year. In August 2005, the Department of the Army issued a mobilization order for Walz’s unit. The unit mobilized in October of that year before it deployed to Iraq in March 2006 .

“Kamala Harris, showing terrible judgment, has decided that her way out of this political problem is to claim that she and Tim Walz are the victims,” Vance said, referencing comments by the campaign pushing back on Republicans’ criticism. “I think the victims are the veterans who are having their service disparaged because the guy who wants to be their vice president is lying instead of telling the truth.”

Vance said he became a member of the VFW Post at which he spoke, saying he’d be back in the future to “have some pancakes, maybe a cigarette, whatever.”

Vice President Kamala Harris has a perceived advantage over former President Donald Trump on several leadership qualities such as honesty, a poll from The Associated Press-NORC Center for Public Affairs Research finds, although Americans are slightly more likely to trust Trump on the economy and immigration.

Nearly half of Americans say that “committed to democracy” and “disciplined” are attributes that better describe Harris. About 3 in 10 say these qualities better describe Trump.

About 4 in 10 say Harris is someone who “cares about people like you” while about 3 in 10 say that about Trump. About 4 in 10 say “honest” better describes Harris and 24% say that quality better describes Trump.

▶ Read more about the AP-NORC poll

Vice President Kamala Harris plans to hold a rally in Milwaukee on Tuesday in the same venue where Republicans gathered for their national convention last month.

Harris will hold the event at the Fiserv Forum while the Democratic National Convention is underway about 100 miles to the south in Chicago, the Harris campaign said Thursday.

The stop will mark the third time since Harris become the Democratic presidential nominee that she’s come to swing state Wisconsin, a nod to the importance of the state. She held her first rally of the presidential campaign outside Milwaukee and last week stopped in Eau Claire, in western Wisconsin, with her newly selected running mate, Minnesota Gov. Tim Walz.

The campaign didn’t say if Walz would be with Harris on Tuesday. The rally is scheduled during the second day of the Democratic National Convention.

Ohio Sen. JD Vance, running mate to Republican Donald Trump, planned to be in Milwaukee on Friday to speak at the Milwaukee Police Association. That would be his second visit to Wisconsin as the vice presidential nominee. He was in Eau Claire last week at the same time as Harris and Walz.

Minnesota Gov. Tim Walz and Ohio Sen. JD Vance have agreed to debate each other on Oct. 1, setting up a matchup of potential vice presidents as early voting in some states gets underway for the general election.

CBS News on Wednesday posted on its X feed that the network had invited both Vance and Walz to debate in New York City, presenting four possible dates — Sept. 17, Sept. 24, Oct. 1 and Oct. 8 — as options.

Walz reposted that message from his own campaign account, “See you on October 1, JD.” The Harris-Walz campaign followed up with a message of its own, saying Walz “looks forward to debating JD Vance — if he shows up.”

Vance posted on X that he would accept the Oct. 1 invitation. He also challenged Walz to meet on Sept. 18.

And she’s zeroing in on corporate price gouging.

Her campaign says Harris plans to push for a federal ban on price gouging on food and groceries. She’s putting particular emphasis on rising meat prices, which she says account for a large part of rising grocery bills.

Year-over-year inflation has reached its lowest level in more than three years. But many Americans are still struggling with food prices, which remain 21% above where they were three years ago. Republican Donald Trump has been pointing to inflation as a key failing of the Biden-Trump administration and its energy policies.

Former President Donald Trump invited reporters to his New Jersey golf club Thursday for his second news conference in as many weeks as he adjusts to a newly energized Democratic ticket ahead of next week’s Democratic National Convention.

Trump will meet the press as he steps up his criticism of Vice President Kamala Harris for not holding a news conference or sitting down for interviews since President Joe Biden ended his reelection campaign and endorsed her to replace him.

▶ Read more about Trump’s scheduled news conference

The Biden administration is taking a victory lap after federal officials inked deals with drug companies to lower the price for 10 of Medicare’s most popular and costliest drugs, but shared few immediate details about the new price older Americans will pay when they fill those prescriptions.

White House officials said Wednesday night they expect U.S. taxpayers to save $6 billion on the new prices, while older Americans could save roughly $1.5 billion on their medications. Those projections, however, were based on dated estimates and the administration shared no details as to how they arrived at the figures.

Nonetheless, the newly negotiated prices — still elusive to the public as of early Thursday morning — will affect the price of drugs used by millions of older Americans to help manage diabetes, blood cancers and prevent heart failure or blood clots.

The drugs include the blood thinners Xarelto and Eliquis and diabetes drugs Jardiance and Januvia. Medicare spent $50 billion covering the drugs last year.

In his first solo appearance as the Democratic vice presidential nominee, Minnesota Gov. Tim Walz warned cheering union members Tuesday that Donald Trump would wage war on working people and threaten Medicare and Social Security as he kicked off a five-state fundraising swing.

He spoke of a grim future for unions if Trump and Ohio Sen. JD Vance are elected, describing a nation where bargaining rights, overtime pay and other protections would be scuttled. He said Trump and Vance have “waged war on working people.”

However, Trump also has courted union support. When he accepted the Republican nomination, he said that he would rescue the auto industry from what he called “complete obliteration.”

Read more about Walz’s 5-state fundraising blitz

Whatever possessed Vice President Kamala Harris to pick Minnesota Gov. Tim Walz as her running mate, it probably wasn’t a desire to inflame arguments about apostrophes. But it doesn’t take much to get grammar nerds fired up .

“The lower the stakes, the bigger the fight,” said Ron Woloshun, a creative director and digital marketer in California who jumped into the fray on social media less than an hour after Harris selected Walz last week to offer his take on possessive proper nouns.

The Associated Press Stylebook says “use only an apostrophe” for singular proper names ending in S: Dickens’ novels, Hercules’ labors, Jesus’ life. But not everyone agrees.

While there is widespread agreement that Walz’s is correct, confusion persists about Harris’ vs. Harris’s. Dreyer’s verdict? Add the ’s.

▶ Read more about the political world’s latest grammar debate

Vice President Kamala Harris talks to the media, Friday, June 25, 2021, after her tour of the U.S. Customs and Border Protection Central Processing Center in El Paso, Texas. Harris visited the U.S. southern border as part of her role leading the Biden administration’s response to a steep increase in migration. (AP Photo/Jacquelyn Martin)

Vice President Kamala Harris was never the “border czar,” as her critics claim.

Biden administration officials say she was assigned to tackle the “root causes” of migration from the Central American nations of El Salvador, Guatemala and Honduras that were responsible for a large chunk of border crossers.

A review of Harris’ work on immigration reveals a record that is more nuanced than the one presented by her critics or allies. It also provides insights into how Harris — who took over as the Democratic standard-bearer when Biden dropped out of the presidential race last month — might tackle one of the nation’s most vexing concerns.

▶ Read more about Harris’ work on immigration

The Democratic National Committee will offer a schedule of trainings, panels and other programming it’s calling “DemPalooza” during the party’s convention in Chicago next week. The name is a play on the Lollapalooza music festival Chicago plays hosts to every year.

“DemPalooza” events will range from trainings on how to use organizing tools to polling briefings and skills workshops. The DNC says these programs are part of its and the Harris campaign’s efforts to organize and reach voters in an evolving media environment and provide opportunities for Democrats to take what they’ve learned back to the communities that will decide the November presidential election.

President Joe Biden leaves after speaking about prescription drug costs at the National Institutes of Health in Bethesda, Md., Dec. 14, 2023. (AP Photo/Andrew Harnik, File)

WASHINGTON (AP) — The Biden administration is taking a victory lap after federal officials inked deals with drug companies to lower the price for 10 of Medicare’s most popular and costliest drugs, but shared few immediate details about the new price older Americans will pay when they fill those prescriptions.

▶ Read the full story here .

Former President Donald Trump leaves Trump Tower in New York on Thursday, Feb. 15, 2024. Trump is expected in court for an important hearing in his New York hush-money criminal case. (AP Photo/Bryan Woolston)

Donald Trump has lost his latest bid for a new judge in his New York hush money criminal case as it heads toward a key ruling and potential sentencing next month.

In a decision posted Wednesday , Judge Juan M. Merchan declined to step aside and said Trump’s demand was a rehash “rife with inaccuracies and unsubstantiated claims” about his ability to remain impartial.

It’s the third time Merchan has rejected such a request from lawyers for the former president and current Republican nominee. They contend the judge has a conflict of interest because his daughter works as a political consultant for prominent Democrats, including Kamala Harris when she sought the Democrats’ 2020 presidential nomination. Harris is now the party’s nominee against Trump.

The judge’s daughter, Loren Merchan, met Harris occasionally in 2019 but never “developed an individual relationship” with her, consulting firm founder Mike Nellis told the chairman of the U.S. House Judiciary Committee, Rep. Jim Jordan, R-Ohio, in a letter Tuesday. The firm, Authentic Campaigns Inc., has not worked for Harris’ campaign, President Joe Biden’s now-ended reelection bid or the Democratic National Committee in the 2024 election cycle, Nellis said.