LEARNING OBJECTIVES
After studying this chapter, you should be able to:
1. Explain the importance and advantages of databases, as well as the difference between database systems and file-based legacy systems.
2. Explain database systems, including logical and physical views, schemas,
the data dictionary, and DBMS languages.
3. Describe what a relational database is, how it organizes data, and how to
create a set of well-structured relational database tables.
Relational databases underlie most modern integrated AISs. This chapter and Chapters 17
through 19 explain how to participate in the design and implementation of a database. This
chapter defines a database, with the emphasis on understanding the relational database structure. Chapter 17 introduces two tools used to design databases—entity-relationship diagramming and REA data modeling—and demonstrates how to use them to b uild a data model.
To appreciate the power of databases, it is important to understand how data are stored in
computer systems. Figure 4-1 shows a data hierarchy. Information about the attributes of a
customer, such as name and address, are stored in fields. All the fields containing data about
one entity (e.g., one customer) form a record. A set of related records, such as all customer
records, forms a file (e.g., the customer file). A set of interrelated, centrally coordinated data
files that are stored with as little data redundancy as possible forms a database. A database
consolidates records previously stored in separate files into a common pool and serves a variety of users and data processing applications.
Databases were developed to address the proliferation of master files. For many years, companies created new files and programs each time a need for information arose. Bank of America
once had 36 million customer accounts in 23 separate systems. This proliferation created problems such as storing the same data in two or more master files, as shown in Figure 4-2. This made
it difficult to integrate and update data and to obtain an organization-wide view of data. It also created problems because the data in the different files were inconsistent. For example, a customer’s
address may have been correctly updated in the shipping master file but not the billing master file.
Figure 4-2 illustrates the differences between file-oriented systems and database systems.
In the database approach, data is an organizational resource that is used by and managed for
the entire organization, not just the originating department. A database management system
(DBMS) is the program that manages and controls the data and the interfaces between the
data and the application programs that use the data stored in the database. The database, the
DBMS, and the application programs that access the database through the DBMS are referred
to as the database system. The database administrator (DBA) is responsible for coordinating, controlling, and managing the database.
Database development life cycle unit 2 part 1Ram Paliwal
The document discusses the database development life cycle (DDLC), which is a six-phase process for designing, implementing, and maintaining a database system to meet an organization's information needs. The six phases are: 1) database initial study, 2) database design, 3) implementation and loading, 4) testing and evaluation, 5) operation, and 6) maintenance and evolution. Each phase is described in detail, from analyzing requirements in the initial study to ongoing maintenance activities once the database is operational.
Michael Joseph is giving a presentation on database normalization. He begins by explaining the importance of properly structuring data across database tables and the problems that can arise from poor database design, such as redundancy, inaccuracy, and consistency issues. He then describes database normalization as a process that organizes data to minimize redundancy by decomposing relations and isolating data in separate, well-defined tables connected through relationships. Different levels of normalization are discussed, with third normal form being sufficient for most applications. Examples are provided to illustrate how normalization progresses from first to third normal form. Potential issues with highly normalized databases are also outlined.
The document discusses different types of data marts:
- Dependent data marts draw data directly from a centralized data warehouse, allowing for unified data access but with a focus on a specific group's needs.
- Independent data marts are standalone systems built from direct access to operational or external data sources without using a centralized warehouse. They are suitable for smaller groups.
- Hybrid data marts can integrate data from both a centralized warehouse and other sources, providing flexibility for ad hoc integration needs.
Este documento describe los conceptos básicos de normalización de bases de datos, incluyendo tablas, tuplas, campos, claves primarias y foráneas. Explica las tres formas normales y sus validaciones. La primera forma normal requiere que los atributos tengan un solo valor. La segunda elimina dependencias parciales de las llaves compuestas. La tercera elimina dependencias transitivas de atributos no primarios. En general, la normalización estructura la base de datos para mantener los datos organizados y facilitar cambios sin efectos secundarios
Data Warehousing (Need,Application,Architecture,Benefits), Data Mart, Schema,...Medicaps University
Data warehousing Components –Building a Data warehouse,
Need for data warehousing,
Basic elements of data warehousing,
Data Mart,
Data Extraction, Clean-up, and Transformation Tools –Metadata,
Star, Snow flake and Galaxy Schemas for Multidimensional databases,
Fact and dimension data,
Partitioning Strategy-Horizontal and Vertical Partitioning.
This document provides an overview and introduction to a lecture on database management systems (DBMS). It discusses how companies are increasingly data-driven and how this class will teach the basics of using and managing data. The lecture will cover the motivation for studying DBMS, an overview of the subject, and course logistics. The goal is for students to understand fundamental database concepts and be able to design, query, and build applications with databases.
This document discusses data warehousing and decision support systems. It defines a data warehouse as a subject-oriented, integrated, time-variant, and non-volatile collection of data used to support management decision making. It describes key features of a data warehouse including being subject-oriented, integrated, time-variant, and non-volatile. The document also discusses the need for decision support systems in business and different architectural styles for data warehousing like OLTP and OLAP.
This document provides an overview of the 3-tier data warehouse architecture. It discusses the three tiers: the bottom tier contains the data warehouse server which fetches relevant data from various data sources and loads it into the data warehouse using backend tools for extraction, cleaning, transformation and loading. The bottom tier also contains the data marts and metadata repository. The middle tier contains the OLAP server which presents multidimensional data to users from the data warehouse and data marts. The top tier contains the front-end tools like query, reporting and analysis tools that allow users to access and analyze the data.
Database development life cycle unit 2 part 1Ram Paliwal
The document discusses the database development life cycle (DDLC), which is a six-phase process for designing, implementing, and maintaining a database system to meet an organization's information needs. The six phases are: 1) database initial study, 2) database design, 3) implementation and loading, 4) testing and evaluation, 5) operation, and 6) maintenance and evolution. Each phase is described in detail, from analyzing requirements in the initial study to ongoing maintenance activities once the database is operational.
Michael Joseph is giving a presentation on database normalization. He begins by explaining the importance of properly structuring data across database tables and the problems that can arise from poor database design, such as redundancy, inaccuracy, and consistency issues. He then describes database normalization as a process that organizes data to minimize redundancy by decomposing relations and isolating data in separate, well-defined tables connected through relationships. Different levels of normalization are discussed, with third normal form being sufficient for most applications. Examples are provided to illustrate how normalization progresses from first to third normal form. Potential issues with highly normalized databases are also outlined.
The document discusses different types of data marts:
- Dependent data marts draw data directly from a centralized data warehouse, allowing for unified data access but with a focus on a specific group's needs.
- Independent data marts are standalone systems built from direct access to operational or external data sources without using a centralized warehouse. They are suitable for smaller groups.
- Hybrid data marts can integrate data from both a centralized warehouse and other sources, providing flexibility for ad hoc integration needs.
Este documento describe los conceptos básicos de normalización de bases de datos, incluyendo tablas, tuplas, campos, claves primarias y foráneas. Explica las tres formas normales y sus validaciones. La primera forma normal requiere que los atributos tengan un solo valor. La segunda elimina dependencias parciales de las llaves compuestas. La tercera elimina dependencias transitivas de atributos no primarios. En general, la normalización estructura la base de datos para mantener los datos organizados y facilitar cambios sin efectos secundarios
Data Warehousing (Need,Application,Architecture,Benefits), Data Mart, Schema,...Medicaps University
Data warehousing Components –Building a Data warehouse,
Need for data warehousing,
Basic elements of data warehousing,
Data Mart,
Data Extraction, Clean-up, and Transformation Tools –Metadata,
Star, Snow flake and Galaxy Schemas for Multidimensional databases,
Fact and dimension data,
Partitioning Strategy-Horizontal and Vertical Partitioning.
This document provides an overview and introduction to a lecture on database management systems (DBMS). It discusses how companies are increasingly data-driven and how this class will teach the basics of using and managing data. The lecture will cover the motivation for studying DBMS, an overview of the subject, and course logistics. The goal is for students to understand fundamental database concepts and be able to design, query, and build applications with databases.
This document discusses data warehousing and decision support systems. It defines a data warehouse as a subject-oriented, integrated, time-variant, and non-volatile collection of data used to support management decision making. It describes key features of a data warehouse including being subject-oriented, integrated, time-variant, and non-volatile. The document also discusses the need for decision support systems in business and different architectural styles for data warehousing like OLTP and OLAP.
This document provides an overview of the 3-tier data warehouse architecture. It discusses the three tiers: the bottom tier contains the data warehouse server which fetches relevant data from various data sources and loads it into the data warehouse using backend tools for extraction, cleaning, transformation and loading. The bottom tier also contains the data marts and metadata repository. The middle tier contains the OLAP server which presents multidimensional data to users from the data warehouse and data marts. The top tier contains the front-end tools like query, reporting and analysis tools that allow users to access and analyze the data.
This document discusses data warehousing, including its definition, importance, components, strategies, ETL processes, and considerations for success and pitfalls. A data warehouse is a collection of integrated, subject-oriented, non-volatile data used for analysis. It allows more effective decision making through consolidated historical data from multiple sources. Key components include summarized and current detailed data, as well as transformation programs. Common strategies are enterprise-wide and data mart approaches. ETL processes extract, transform and load the data. Clean data and proper implementation, training and maintenance are important for success.
Online analytical processing (OLAP) allows users to easily extract and analyze data from different perspectives. It originated in the 1970s and was formalized in 1993, with OLAP cubes organizing numeric facts by dimensions to enable fast analysis. OLAP provides operations like roll-up, drill-down, slice, and dice to analyze aggregated data across multiple systems. It offers advantages over relational databases for consistent reporting and analysis.
FellowBuddy.com is an innovative platform that brings students together to share notes, exam papers, study guides, project reports and presentation for upcoming exams.
We connect Students who have an understanding of course material with Students who need help.
Benefits:-
# Students can catch up on notes they missed because of an absence.
# Underachievers can find peer developed notes that break down lecture and study material in a way that they can understand
# Students can earn better grades, save time and study effectively
Our Vision & Mission – Simplifying Students Life
Our Belief – “The great breakthrough in your life comes when you realize it, that you can learn anything you need to learn; to accomplish any goal that you have set for yourself. This means there are no limits on what you can be, have or do.”
Like Us - https://www.facebook.com/FellowBuddycom
This document defines a data warehouse as a collection of corporate information derived from operational systems and external sources to support business decisions rather than operations. It discusses the purpose of data warehousing to realize the value of data and make better decisions. Key components like staging areas, data marts, and operational data stores are described. The document also outlines evolution of data warehouse architectures and best practices for implementation.
The document provides an introduction to database management systems (DBMS) presented by Mrs. Surkhab Shelly. It defines a database and DBMS, lists some examples of DBMS software, and discusses the advantages of using a DBMS including reducing data redundancy, sharing data, ensuring data integrity and security, and automating backup and recovery. It also outlines the components of a DBMS including software, hardware, procedures, data, and different types of users.
This document outlines the design and implementation of a data warehouse for KostLess, a multinational retail company. It includes details on the business case, dimensional model, data definition language to create the schema, ETL processes, sample reports, and project management considerations. The dimensional model includes facts about sales and dimensions for customers, products, time and currency. The schema uses star schema design with dimension and fact tables linked by primary and foreign keys. Sample SQL is provided to define the tables, constraints, and indexes.
The document discusses databases and database management systems. It describes how databases solve problems with traditional file-based data storage, such as data redundancy and inconsistency. It explains how a database centralizes data into a collection of related files and controls access through a database management system. The document also covers relational databases, object-oriented databases, and capabilities provided by database management systems.
The document discusses dimensional modeling concepts used in data warehouse design. Dimensional modeling organizes data into facts and dimensions. Facts are measures that are analyzed, while dimensions provide context for the facts. The dimensional model uses star and snowflake schemas to store data in denormalized tables optimized for querying. Key aspects covered include fact and dimension tables, slowly changing dimensions, and handling many-to-many and recursive relationships.
The document discusses key concepts from Chapter 2 on database environments, including:
1) It describes the ANSI-SPARC three-level architecture for database systems, which separates data into external, conceptual, and internal levels.
2) It explains the roles of various users in a database environment like data administrators, database administrators, and end users.
3) It provides an overview of database languages, data models, and the functions of a database management system.
A data warehouse is a database used for reporting and analysis that integrates data from multiple sources. It provides strategic information through analysis that cannot be done by operational systems. A data warehouse contains integrated, subject-oriented data that is periodically updated and stored over time for decision making. It supports analytical tools and access for management rather than daily transactions.
This document provides an overview of key concepts related to data warehousing including what a data warehouse is, common data warehouse architectures, types of data warehouses, and dimensional modeling techniques. It defines key terms like facts, dimensions, star schemas, and snowflake schemas and provides examples of each. It also discusses business intelligence tools that can analyze and extract insights from data warehouses.
The document provides an overview of data warehousing and decision support systems. It discusses how data warehouses evolved from databases used for transaction processing to integrated databases designed for analysis and decision making. Key points include:
- Data warehouses store historical data from multiple sources to support analysis and decision making.
- They address limitations of transactional databases that are optimized for real-time queries rather than complex analysis.
- Effective data warehousing requires resolving data conflicts, documenting assumptions, and learning from mistakes in the implementation process.
A database management system (DBMS) is software that allows for the creation, management, and use of databases. A DBMS provides users and administrators with various tools and applications to store, organize, and access data. It allows for data to be easily retrieved, filtered, sorted, and updated efficiently. Some key components of a DBMS include the database users, the data itself, software and procedures, hardware, and database access languages. DBMSs are widely used in applications such as banking, universities, e-commerce, and more.
This document discusses database normalization and different normal forms including 1NF, 2NF, 3NF, and BCNF. It defines anomalies like insertion, update, and deletion anomalies that can occur when data is not normalized. Examples are provided to illustrate the different normal forms and how denormalizing data can lead to anomalies. The key aspects of each normal form like removing repeating groups (1NF), removing functional dependencies on non-prime attributes (2NF), and removing transitive dependencies (3NF, BCNF) are explained.
James A. O'Brien, and George Marakas. Management Information Systems with MISource 2007, 8th ed. Boston, MA: McGraw-Hill, Inc., 2007. ISBN: 13 9780073323091
The document discusses the FP-Growth algorithm for frequent pattern mining. It improves upon the Apriori algorithm by not requiring candidate generation and only requiring two scans of the database. FP-Growth works by first building a compact FP-tree structure using two passes over the data, then extracting frequent itemsets directly from the FP-tree. An example is provided where an FP-tree is constructed from a sample transaction database and frequent patterns are generated from the tree. Advantages of FP-Growth include only needing two scans of data and faster runtime than Apriori, while a disadvantage is the FP-tree may not fit in memory.
Data resource management involves applying information systems technology to manage data resources. It includes activities like creating, storing, organizing, and retrieving data using database management systems. There are different types of databases like operational, distributed, data warehouses, data marts, and end user databases. Data warehouses store historical data from various operational databases to help identify trends. Data mining techniques are used to better understand data through analysis, sorting, extracting patterns and relationships to gain insights. Common applications of data mining include banking, customer relationship management, targeted marketing, fraud detection, and scientific data analysis.
This document provides an introduction to databases and database management systems (DBMS). It discusses key concepts such as the main components and users of a database including end users, database administrators, and designers. It also summarizes the main characteristics of the database approach like data abstraction, multiple views, and transaction processing. Some advantages of using a DBMS are controlling redundancy, restricting access, and enforcing integrity constraints. The document also outlines scenarios where a DBMS may not be needed.
Data warehouse implementation design for a Retail businessArsalan Qadri
The document contains an end to end data warehouse design - from SKU procurement to SKU Sale. Additionally, a BI dashboard has been created in Tableau, to mine the warehouse, with SKU as the grain. The data can be aggregated at levels of Supplier/Store/Location/Inventory/Sale Date/Time in Warehouse etc.
In this PPT, you will learn:
• The difference between data and information
• What a database is, the various types of databases, and why they are valuable assets for
decision making
• The importance of database design
• How modern databases evolved from file systems
• About flaws in file system data management
• The main components of the database system
• The main functions of a database management system (DBMS)
The document discusses key concepts related to databases including:
1) It defines data as representations of facts, concepts or instructions that are suitable for communication, interpretation or processing.
2) A database is defined as a structured set of non-redundant information organized based on a data model, consisting of files, records and fields.
3) A database management system (DBMS) provides an interface between users and the database, allowing for data definition, manipulation and control.
This document discusses data warehousing, including its definition, importance, components, strategies, ETL processes, and considerations for success and pitfalls. A data warehouse is a collection of integrated, subject-oriented, non-volatile data used for analysis. It allows more effective decision making through consolidated historical data from multiple sources. Key components include summarized and current detailed data, as well as transformation programs. Common strategies are enterprise-wide and data mart approaches. ETL processes extract, transform and load the data. Clean data and proper implementation, training and maintenance are important for success.
Online analytical processing (OLAP) allows users to easily extract and analyze data from different perspectives. It originated in the 1970s and was formalized in 1993, with OLAP cubes organizing numeric facts by dimensions to enable fast analysis. OLAP provides operations like roll-up, drill-down, slice, and dice to analyze aggregated data across multiple systems. It offers advantages over relational databases for consistent reporting and analysis.
FellowBuddy.com is an innovative platform that brings students together to share notes, exam papers, study guides, project reports and presentation for upcoming exams.
We connect Students who have an understanding of course material with Students who need help.
Benefits:-
# Students can catch up on notes they missed because of an absence.
# Underachievers can find peer developed notes that break down lecture and study material in a way that they can understand
# Students can earn better grades, save time and study effectively
Our Vision & Mission – Simplifying Students Life
Our Belief – “The great breakthrough in your life comes when you realize it, that you can learn anything you need to learn; to accomplish any goal that you have set for yourself. This means there are no limits on what you can be, have or do.”
Like Us - https://www.facebook.com/FellowBuddycom
This document defines a data warehouse as a collection of corporate information derived from operational systems and external sources to support business decisions rather than operations. It discusses the purpose of data warehousing to realize the value of data and make better decisions. Key components like staging areas, data marts, and operational data stores are described. The document also outlines evolution of data warehouse architectures and best practices for implementation.
The document provides an introduction to database management systems (DBMS) presented by Mrs. Surkhab Shelly. It defines a database and DBMS, lists some examples of DBMS software, and discusses the advantages of using a DBMS including reducing data redundancy, sharing data, ensuring data integrity and security, and automating backup and recovery. It also outlines the components of a DBMS including software, hardware, procedures, data, and different types of users.
This document outlines the design and implementation of a data warehouse for KostLess, a multinational retail company. It includes details on the business case, dimensional model, data definition language to create the schema, ETL processes, sample reports, and project management considerations. The dimensional model includes facts about sales and dimensions for customers, products, time and currency. The schema uses star schema design with dimension and fact tables linked by primary and foreign keys. Sample SQL is provided to define the tables, constraints, and indexes.
The document discusses databases and database management systems. It describes how databases solve problems with traditional file-based data storage, such as data redundancy and inconsistency. It explains how a database centralizes data into a collection of related files and controls access through a database management system. The document also covers relational databases, object-oriented databases, and capabilities provided by database management systems.
The document discusses dimensional modeling concepts used in data warehouse design. Dimensional modeling organizes data into facts and dimensions. Facts are measures that are analyzed, while dimensions provide context for the facts. The dimensional model uses star and snowflake schemas to store data in denormalized tables optimized for querying. Key aspects covered include fact and dimension tables, slowly changing dimensions, and handling many-to-many and recursive relationships.
The document discusses key concepts from Chapter 2 on database environments, including:
1) It describes the ANSI-SPARC three-level architecture for database systems, which separates data into external, conceptual, and internal levels.
2) It explains the roles of various users in a database environment like data administrators, database administrators, and end users.
3) It provides an overview of database languages, data models, and the functions of a database management system.
A data warehouse is a database used for reporting and analysis that integrates data from multiple sources. It provides strategic information through analysis that cannot be done by operational systems. A data warehouse contains integrated, subject-oriented data that is periodically updated and stored over time for decision making. It supports analytical tools and access for management rather than daily transactions.
This document provides an overview of key concepts related to data warehousing including what a data warehouse is, common data warehouse architectures, types of data warehouses, and dimensional modeling techniques. It defines key terms like facts, dimensions, star schemas, and snowflake schemas and provides examples of each. It also discusses business intelligence tools that can analyze and extract insights from data warehouses.
The document provides an overview of data warehousing and decision support systems. It discusses how data warehouses evolved from databases used for transaction processing to integrated databases designed for analysis and decision making. Key points include:
- Data warehouses store historical data from multiple sources to support analysis and decision making.
- They address limitations of transactional databases that are optimized for real-time queries rather than complex analysis.
- Effective data warehousing requires resolving data conflicts, documenting assumptions, and learning from mistakes in the implementation process.
A database management system (DBMS) is software that allows for the creation, management, and use of databases. A DBMS provides users and administrators with various tools and applications to store, organize, and access data. It allows for data to be easily retrieved, filtered, sorted, and updated efficiently. Some key components of a DBMS include the database users, the data itself, software and procedures, hardware, and database access languages. DBMSs are widely used in applications such as banking, universities, e-commerce, and more.
This document discusses database normalization and different normal forms including 1NF, 2NF, 3NF, and BCNF. It defines anomalies like insertion, update, and deletion anomalies that can occur when data is not normalized. Examples are provided to illustrate the different normal forms and how denormalizing data can lead to anomalies. The key aspects of each normal form like removing repeating groups (1NF), removing functional dependencies on non-prime attributes (2NF), and removing transitive dependencies (3NF, BCNF) are explained.
James A. O'Brien, and George Marakas. Management Information Systems with MISource 2007, 8th ed. Boston, MA: McGraw-Hill, Inc., 2007. ISBN: 13 9780073323091
The document discusses the FP-Growth algorithm for frequent pattern mining. It improves upon the Apriori algorithm by not requiring candidate generation and only requiring two scans of the database. FP-Growth works by first building a compact FP-tree structure using two passes over the data, then extracting frequent itemsets directly from the FP-tree. An example is provided where an FP-tree is constructed from a sample transaction database and frequent patterns are generated from the tree. Advantages of FP-Growth include only needing two scans of data and faster runtime than Apriori, while a disadvantage is the FP-tree may not fit in memory.
Data resource management involves applying information systems technology to manage data resources. It includes activities like creating, storing, organizing, and retrieving data using database management systems. There are different types of databases like operational, distributed, data warehouses, data marts, and end user databases. Data warehouses store historical data from various operational databases to help identify trends. Data mining techniques are used to better understand data through analysis, sorting, extracting patterns and relationships to gain insights. Common applications of data mining include banking, customer relationship management, targeted marketing, fraud detection, and scientific data analysis.
This document provides an introduction to databases and database management systems (DBMS). It discusses key concepts such as the main components and users of a database including end users, database administrators, and designers. It also summarizes the main characteristics of the database approach like data abstraction, multiple views, and transaction processing. Some advantages of using a DBMS are controlling redundancy, restricting access, and enforcing integrity constraints. The document also outlines scenarios where a DBMS may not be needed.
Data warehouse implementation design for a Retail businessArsalan Qadri
The document contains an end to end data warehouse design - from SKU procurement to SKU Sale. Additionally, a BI dashboard has been created in Tableau, to mine the warehouse, with SKU as the grain. The data can be aggregated at levels of Supplier/Store/Location/Inventory/Sale Date/Time in Warehouse etc.
In this PPT, you will learn:
• The difference between data and information
• What a database is, the various types of databases, and why they are valuable assets for
decision making
• The importance of database design
• How modern databases evolved from file systems
• About flaws in file system data management
• The main components of the database system
• The main functions of a database management system (DBMS)
The document discusses key concepts related to databases including:
1) It defines data as representations of facts, concepts or instructions that are suitable for communication, interpretation or processing.
2) A database is defined as a structured set of non-redundant information organized based on a data model, consisting of files, records and fields.
3) A database management system (DBMS) provides an interface between users and the database, allowing for data definition, manipulation and control.
The document provides an overview of database management systems, including their history and importance in organizations. It discusses the evolution of databases from file management systems to hierarchical and network databases to modern relational database systems. The key advantages of relational database management systems are consistent data access, flexibility, standardized products, use of the SQL query language, and easier management of data security.
The document provides an overview of relational databases and their advantages over traditional file-based systems. It discusses key concepts such as entities, attributes, records, files and databases. The document also describes database management systems (DBMS), schemas, data dictionaries, and relational database structures including tables, rows, columns, primary keys and foreign keys. Relational databases organize data into logically related tables to facilitate data integration, sharing, flexibility and consistency.
The document provides an overview of relational databases and their advantages over traditional file-based systems. It discusses key concepts such as entities, attributes, records, files and databases. The document also describes database management systems (DBMS), schemas, data dictionaries, and relational database structures including tables, rows, columns, primary keys and foreign keys. Relational databases organize data into logically related tables to facilitate data integration, sharing, flexibility and consistency.
The document provides an overview of relational databases and their advantages over traditional file-based systems. It discusses key concepts such as entities, attributes, records, files and databases. The document also describes database management systems (DBMS), schemas, data dictionaries, and relational database structures including tables, rows, columns, primary keys and foreign keys. Relational databases organize data into logically related tables to facilitate data integration, sharing, flexibility and consistency.
The document discusses key concepts related to databases including:
- A database is an organized collection of data stored electronically and accessed via a DBMS.
- Data is logically organized into records, tables, and databases for meaningful representation to users.
- Databases offer advantages like reduced data redundancy, improved data integrity, and easier data sharing.
- Database subsystems include the database engine, data definition language, and data administration.
The document then covers database types, uses, issues, and security concepts.
This document outlines the syllabus for a Database Management Systems course. It includes 5 units that cover database concepts, the relational model, SQL, normalization, transaction management, recovery, and query processing. The objectives are to understand basic database concepts, master SQL, understand relational design principles, and become familiar with transaction processing, storage structures, and query optimization techniques. Key topics include the entity-relationship model, relational algebra, normalization, concurrency control, crash recovery, and query processing and optimization.
DATABASE MANAGEMENT SYSTEMS university course materials useful for students ...SakkaravarthiS1
This document provides information about a database management systems (DBMS) course syllabus. It includes the course objectives, which are to understand basic database concepts, master SQL, understand relational database design principles, and become familiar with transaction processing and concurrency control. The syllabus outlines 5 units that will be covered: data models and languages, the relational model and SQL, normalization, transaction management and recovery, and query processing. Required textbooks and references are also listed.
● Why Databases?
● Why Database Design is Important?
● The Database System Environment and Functions.
● Managing the Database System: A Shift in Focus.
The document provides an overview of fundamentals of database design including definitions of key concepts like data, information, and databases. It discusses the purpose of databases and database management systems. It also covers topics like selecting a database system, database development best practices, and data entry considerations.
The document provides an introduction to database management systems (DBMS). It can be summarized as follows:
1. A DBMS allows for the storage and retrieval of large amounts of related data in an organized manner. It removes data redundancy and allows for fast retrieval of data.
2. Key components of a DBMS include the database engine, data definition subsystem, data manipulation subsystem, application generation subsystem, and data administration subsystem.
3. A DBMS uses a data model to represent the organization of data in a database. Common data models include the entity-relationship model, object-oriented model, and relational model.
The document provides an introduction to database management systems. It discusses the key components of a DBMS including data models like the hierarchical, network, relational, and entity-relationship models. It also summarizes some of the advantages of using a DBMS like data independence, efficient data access, data integrity and security, data administration, concurrent access and crash recovery, and reduced application development time. Textbooks and references on the topic are also listed.
A database management system (DBMS) is a software system that is used to create and manage databases. It allows users to define, create, maintain and control access to the database. There are four main types of DBMS: hierarchical, network, relational and object-oriented. A DBMS provides advantages like improved data sharing, security and integration. It also enables better access to data and decision making. However, DBMS also have disadvantages such as increased costs, management complexity and the need to constantly maintain and upgrade the system.
The document provides an introduction to database management systems (DBMS). It defines what a database and DBMS are, and explains that a DBMS allows users to define, create, and manipulate databases for applications. It also discusses some key components of a DBMS environment, including software, hardware, data, procedures, and database access languages like SQL. The document compares traditional file-based data storage with DBMS approaches and outlines some benefits DBMS provide like reduced redundancy, improved data integrity and sharing, and increased accessibility.
helps the DBA in day to day activities
2
Security Administrator: responsible for security policies and implementation
3
Performance Tuner: responsible for tuning the database for better performance
4
Backup and Recovery Administrator: responsible for backup and recovery plans
2. Database Designer
Responsible for conceptual, logical and physical design of the database
Determines the data model, structure and constraints
Defines the schema and metadata
Works closely with the DBA and users
3. Database Programmer
Responsible for implementing the design into
The document provides an overview of key concepts in database systems including:
1) It defines data, databases, DBMS and typical database system components.
2) It describes different data management approaches including manual, file-based and database approaches.
3) It outlines the functions of a DBMS including data storage, security, and integrity management.
This document provides an introduction to database systems. It discusses what a database is and the functions of a database management system (DBMS). It outlines three approaches to data management - manual, file-based, and database-based. The database approach centralizes data storage and provides tools to ensure data integrity and security. A DBMS performs functions like data storage management, security management, and backup/recovery to maintain the database. The document compares the advantages of database systems like data sharing and improved accessibility over file-based systems.
Organizing Data in a Traditional File Environment
File organization Term and Concepts
Computer system organizes data in a hierarchy
Bit: Smallest unit of data; binary digit (0,1)
Byte: Group of bits that represents a single character
Field: Group of characters as word(s) or number
Record: Group of related fields
File: Group of records of same type
IS 3003Chapter 61The Globe and MailIt is the.docxpriestmanmable
IS 3003
Chapter 6
1
The Globe and Mail
It is the largest newspaper in Canada; is considered the NYT and USA Today all in one; It wants to enjoy the subscribership of every household in Canada, but it has a problem with trying to house the information needed on all the homes in Canada
Its problem with storing and retrieving information started with the practice of storing everything on a mainframe that was hard to use for retrieving and analyzing; this led to large amounts of data relating to specific areas being downloaded to smaller computers which created pockets of data processing interests that became unwieldy; data was not integrated and difficult to analyze without the relationships to other data being available
2
There were inconsistent database systems in use, such as MS Access, SQL Server, Foxbase Pro, and even Excel; updating information was difficult because the latest information was back on the mainframe, and had to be downloaded again to get to new info
Getting to potential new subscribers was almost impossible; even information on existing subscribers was a problem security-wise, because it was stored in many places thus causing potential security breaches with inconsistent controls
3
In 2002, Globe and Mail acquired SAP NetWeaver BW, a data warehousing platform (platform versus program: platform is a capability to build several programs, whereas a program is just that); the general definition of NetWeaver is that it is an application builder from SAP for integrating business processes and databases from a number of sources while exploiting the leading Web services technologies;
All information is now aggregated within the SAP NetWeaver system in which applications for analyzing and using database items can be built and changed quickly; additionally, the database is available quickly for warehousing and mining purposes
4
The investment paid for itself in one year
This example points out the extreme importance of data management; management decision making was also enhanced by more effective use of available info
Boost in efficiency was caused by:
Making the globe and mail data easier to locate and assemble
The SAP NetWeaver system integrated info from all sources available to the paper
Duplications were eliminated, and synchronization of data sources was achieved
5
A database is – based on computer files, records and items; contains data on people, places, and things; the archetypical database is the phone book which is a record of people who use phones that are listed (accessible to the public)
Most non-computer databases, namely paper records, rolodex files, folders, notes, etc. are sequential and can only be reviewed in a certain order
6
Data and file structures
Databases are usually organized as relational databases
These are 2-dimensional tables where each subject, e.g., customers, is organized as a set on entities (records), which contain things like names of each customer, address, ph ...
Semelhante a CHAPTER-4_RELATIONAL-DATABASE.pptx (20)
Walmart Business+ and Spark Good for Nonprofits.pdfTechSoup
"Learn about all the ways Walmart supports nonprofit organizations.
You will hear from Liz Willett, the Head of Nonprofits, and hear about what Walmart is doing to help nonprofits, including Walmart Business and Spark Good. Walmart Business+ is a new offer for nonprofits that offers discounts and also streamlines nonprofits order and expense tracking, saving time and money.
The webinar may also give some examples on how nonprofits can best leverage Walmart Business+.
The event will cover the following::
Walmart Business + (https://business.walmart.com/plus) is a new shopping experience for nonprofits, schools, and local business customers that connects an exclusive online shopping experience to stores. Benefits include free delivery and shipping, a 'Spend Analytics” feature, special discounts, deals and tax-exempt shopping.
Special TechSoup offer for a free 180 days membership, and up to $150 in discounts on eligible orders.
Spark Good (walmart.com/sparkgood) is a charitable platform that enables nonprofits to receive donations directly from customers and associates.
Answers about how you can do more with Walmart!"
Strategies for Effective Upskilling is a presentation by Chinwendu Peace in a Your Skill Boost Masterclass organisation by the Excellence Foundation for South Sudan on 08th and 09th June 2024 from 1 PM to 3 PM on each day.
How to Fix the Import Error in the Odoo 17Celine George
An import error occurs when a program fails to import a module or library, disrupting its execution. In languages like Python, this issue arises when the specified module cannot be found or accessed, hindering the program's functionality. Resolving import errors is crucial for maintaining smooth software operation and uninterrupted development processes.
This document provides an overview of wound healing, its functions, stages, mechanisms, factors affecting it, and complications.
A wound is a break in the integrity of the skin or tissues, which may be associated with disruption of the structure and function.
Healing is the body’s response to injury in an attempt to restore normal structure and functions.
Healing can occur in two ways: Regeneration and Repair
There are 4 phases of wound healing: hemostasis, inflammation, proliferation, and remodeling. This document also describes the mechanism of wound healing. Factors that affect healing include infection, uncontrolled diabetes, poor nutrition, age, anemia, the presence of foreign bodies, etc.
Complications of wound healing like infection, hyperpigmentation of scar, contractures, and keloid formation.
A workshop hosted by the South African Journal of Science aimed at postgraduate students and early career researchers with little or no experience in writing and publishing journal articles.
Beyond Degrees - Empowering the Workforce in the Context of Skills-First.pptxEduSkills OECD
Iván Bornacelly, Policy Analyst at the OECD Centre for Skills, OECD, presents at the webinar 'Tackling job market gaps with a skills-first approach' on 12 June 2024
ISO/IEC 27001, ISO/IEC 42001, and GDPR: Best Practices for Implementation and...PECB
Denis is a dynamic and results-driven Chief Information Officer (CIO) with a distinguished career spanning information systems analysis and technical project management. With a proven track record of spearheading the design and delivery of cutting-edge Information Management solutions, he has consistently elevated business operations, streamlined reporting functions, and maximized process efficiency.
Certified as an ISO/IEC 27001: Information Security Management Systems (ISMS) Lead Implementer, Data Protection Officer, and Cyber Risks Analyst, Denis brings a heightened focus on data security, privacy, and cyber resilience to every endeavor.
His expertise extends across a diverse spectrum of reporting, database, and web development applications, underpinned by an exceptional grasp of data storage and virtualization technologies. His proficiency in application testing, database administration, and data cleansing ensures seamless execution of complex projects.
What sets Denis apart is his comprehensive understanding of Business and Systems Analysis technologies, honed through involvement in all phases of the Software Development Lifecycle (SDLC). From meticulous requirements gathering to precise analysis, innovative design, rigorous development, thorough testing, and successful implementation, he has consistently delivered exceptional results.
Throughout his career, he has taken on multifaceted roles, from leading technical project management teams to owning solutions that drive operational excellence. His conscientious and proactive approach is unwavering, whether he is working independently or collaboratively within a team. His ability to connect with colleagues on a personal level underscores his commitment to fostering a harmonious and productive workplace environment.
Date: May 29, 2024
Tags: Information Security, ISO/IEC 27001, ISO/IEC 42001, Artificial Intelligence, GDPR
-------------------------------------------------------------------------------
Find out more about ISO training and certification services
Training: ISO/IEC 27001 Information Security Management System - EN | PECB
ISO/IEC 42001 Artificial Intelligence Management System - EN | PECB
General Data Protection Regulation (GDPR) - Training Courses - EN | PECB
Webinars: https://pecb.com/webinars
Article: https://pecb.com/article
-------------------------------------------------------------------------------
For more information about PECB:
Website: https://pecb.com/
LinkedIn: https://www.linkedin.com/company/pecb/
Facebook: https://www.facebook.com/PECBInternational/
Slideshare: http://www.slideshare.net/PECBCERTIFICATION
2. Learning Objectives
After studying this chapter, you should be able to:
1. Explain the importance and advantages of databases, as well as the
difference between database systems and file-based legacy systems.
2. Explain database systems, including logical and physical views,
schemas, the data dictionary, and DBMS languages.
3. Describe what a relational database is, how it organizes data, and
how to create a set of well-structured relational database tables
4. Integrative Case
S&S is very successful and operates five stores and a popular website.
Ashton Fleming believes that it is time to upgrade S&S’s accounting
information system (AIS) so that Susan and Scott can easily access the
information they need to run their business. Most new AISs are based on
a relational database. Since Ashton knows that Scott and Susan are likely
to have questions, he prepared a brief report that explains why S&S’s new
AIS should be a relational database system. His report addresses the
following questions:
5. 1. What is a database system, and how does it differ from
file-oriented systems?
2. What is a relational database system?
3. How do you design a well-structured set of tables in a
relational database?
4. How do you query a relational database system?
Integrative Case
7. Introduction
• Relational databases underlie most modern integrated AISs.
• This chapter explain how to participate in the design and
implementation of a database.
• This chapter defines a database, with the emphasis on
understanding the relational database structure.
9. Databases and Files
• It is important to understand how data are stored in computer systems.
• Information about the attributes of a customer, such as name and address,
are stored in fields.
• All the fields containing data about one entity (e.g., one customer) form a
record.
• A set of related records, such as all customer records, forms a file (e.g.,
the customer file).
11. Databases and Files
• A set of interrelated, centrally coordinated data tables stored
electronically with as little data redundancy as possible.
• A database consolidates records previously stored in separate files
into a common pool and serves a variety of users and data processing
applications.
• Databases were developed to address the proliferation of master files.
Database
12. Databases and Files
• For many years, companies created new files and programs each time
a need for information arose. This proliferation created problems:
Storing the same data in two or more master files
Difficult to integrate and update data and to obtain an
organization-wide view of data
The data in the different files were inconsistent
Files
14. Databases and Files
The program that manages and controls the data and the interfaces
between the data and the application programs that use the data stored in
the database.
Database Management System (DBMS)
Database System
The data-base, the DBMS, and the application programs that access the
database through the DBMS.
Database Administrator (DBA)
The person responsible for coordinating, controlling, and managing the
database.
15. Databases and Files
Data Warehouse
• Very large databases containing detailed and summarized data for a
number of years used for analysis rather than transaction processing.
• Measured in petabytes (1,000 terabytes or 1 million gigabytes).
• Data warehouses do not replace transaction processing databases; they
complement them by providing support for strategic decision making.
• They are usually updated periodically rather than in real time.
• Data warehouses are purposely redundant to maximize query efficiency
16. Databases and Files
• Analyzing large amounts of data for strategic decision making. There
are two main techniques used in business intelligence:
1. Online analytical processing (OLAP) - Using queries to investigate
hypothesized relationships among data.
2. Data mining - Using sophisticated statistical analysis to “discover”
unhypothesized relationships in the data.
Business Intelligence
17. Databases and Files
Data validation controls are needed to ensure that data warehouse input is
accurate. Verifying the accuracy, called scrubbing the data, is often one of
the most time-consuming and expensive steps in creating a data
warehouse. It is also important to control access to the data warehouse as
well as to encrypt the stored data. Finally, it is important to regularly backup
the data warehouse and store the backups securely.
Proper Controls
19. Data integration
Advantages of Database Systems
Master files are combined
into large “pools” of data that
many application programs
access.
Data sharing
Integrated data are more easily shared
with authorized users. Databases are
easily browsed to research a problem
or obtain detailed information
underlying a report.
20. Minimal data redundancy
and data inconsistencies
Because data items are usually
stored only once, data
redundancy and data
inconsistencies are minimized.
Data independence
Because data and the programs that use
them are independent of each other,
each can be changed without changing
the other. This facilitates programming
and simplifies data management
Advantages of Database Systems
21. Advantages of Database Systems
Cross-functional analysis
In a database system, relationships, such as the
association between selling costs and promotional
campaigns, can be explicitly defined and used in the
preparation of management reports.
23. Importance of Good Data
Incorrect database data can lead to bad decisions, embarrassment, and
angry users. For example:
A company sent half its mail-order
catalogs to incorrect addresses. A
manager finally investigated the
large volume of returns and
customer complaints. Correcting
customer addresses in the
database saved the company $12
million a year.
Valparaiso, Indiana, used the county
database to develop its tax rates. After
the tax notices were mailed, a huge error
was discovered: A $121,900 home was
valued at $400 million and caused a $3.1
million property tax revenue shortfall. As
a result, the city, the school district, and
governmental agencies had to make
severe budget cuts.
24. Importance of Good Data
• The Data Warehousing Institute estimates that bad data cost businesses over
$600 billion a year in unnecessary postage, marketing costs, and lost customer
credibility.
• It is estimated that over 25% of business data is inaccurate or incomplete.
• In a recent survey, 53% of 750 information technology (IT) professionals said
their companies experienced problems due to poor-quality data.
• To avoid outdated, incomplete, or erroneous data, management needs policies
and procedures that ensure clean, or scrubbed, data.
• The Sarbanes-Oxley Act (SOX) states that top executives face prosecution and
jail time if a company’s financial data are not in order.
27. Logical and Physical Views of Data
Record layout is a document that shows the items stored in a file, including the order
and length of the data fields and the type of data stored in an A/R file.
Accounts Receivable File Record Layout
28. Logical and Physical Views of Data
Logical view
The database approach provides two separate views of the data:
how people conceptually organize, view, and understand the relationships
among data items
Physical view
the way data are physically arranged and stored in the computer system
29. Function of the DBMS:
To Support Multiple Logical Views of Data
31. Schemas
There are three levels of schemas:
A description of the data elements in a database, the
relationships among them, and the logical model used to
organize and describe the data.
1. External-level schema
2. Conceptual-level schema
3. Internal-level schema
32. Schemas
An individual user’s view of portions of a database; also called a
subschema (a subset of the schema).
The organization-wide view of the entire database that lists all data
elements and the relationships between them.
A low-level view of the entire database describing how the data are
actually stored and accessed.
Conceptual-level schema
External-level schema
Internal-level schema
34. Schemas
Inputs include new or deleted data elements and changes in data
element names, descriptions, or uses.
Outputs include reports for programmers, designers, and users, such as
(1) programs or reports using a data item, (2) synonyms for the data
elements in a file, and (3) data elements used by a user.
39. DBMS LANGUAGES
DBMS language that builds
the data dictionary, creates
the database, describes
logical views, and specifies
record or field security
constraints.
High-level, English-like, DBMS
language that contains powerful,
easy-to-use commands that
enable users to retrieve, sort,
order and display data
DBMS language that changes
database content, including data
element creations, updates,
insertions, and deletions.
DBMS language that simplifies
report creation
Data query language (DQL)
Data definition language
(DDL)
Data manipulation language
(DML)
Report writer
42. Relational Databases
abstract representation of database contents like the conceptual view
represents conceptual- and external-level schemas as if data are stored in
two-dimensional tables
A relational database is a collection of two-dimensional tables with each
table representing an object about which we wish to collect and store
information. Each row in a table, called a tuple, contains data about a
specific occurrence of an entity. Each column contains data about an
attribute of that entity.
Relational data model
Data model
Relational Database
46. Types of Attributes
the database attribute, or combination of attributes, that uniquely
identifies a specific row in a table
an attribute in one table that is also a primary key in another table and is
used to link the two tables.
area where all other attributes reside.
Foreign key
Primary key
Non-key attributes
50. Disadvantages
• It stores lots of redundant data
• Problems occur when invoice data are
stored in these types of tables
Update anomaly - Improper
database organization where a non-primary key
item is stored multiple times; updating the item in
one location and not the others causes data
inconsistencies.
Insert anomaly - Improper database
organization that results in the inability to add
records to a database.
Delete anomaly - Improper
organization of a database that results in the loss
of all information about an entity when a row
is deleted.
1: Store All Data in
One Table with Each
Data Element
Represented as a
Column.
Designing a Relational Database for S&S, Inc.
55. Basic Requirements of a Relational Database
1. Every column in a row must be single valued.
2. Primary keys cannot be null.
• entity integrity rule - A non_x0002_null primary key ensures that every row in a
table represents something and that it can be identified.
3. Foreign keys, if not null, must have values that correspond to
the value of a primary key in another table.
• referential integrity rule - For_x0002_eign keys which link rows in one table to rows in
another table must have values that corre_x0002_spond to the value of a primary
key in another table.
4. All non-key attributes in a table must describe a characteristic
of the object identified by the primary key.
59. Following relational
database creation rules
to design a relational
database that is free from
delete, insert, and update
anomalies.
Normalization
Two Approaches of Database Design
60. Using knowledge of
business processes and
information needs to create
a diagram that shows what
to include in a fully
normalized database (in
3NF).
Semantic data
modeling
Two Approaches of Database Design
61. Facilitates the
efficient design of
transaction
processing
databases
Advantages of Semantic Data Modeling
Helps ensure that
the new system
meets users’ actual
needs
64. QUERY 1
• Query 1 answers two questions:
What are the invoice numbers of
all sales made to D. Ainge, and
who was the salesperson for
each sale?
• The Sales and Customer tables
contain the three items needed
to answer this query: Sales
Invoice #, Salesperson, and
Customer Name.
• Click the “Query Design”
button and select the Sales and
Customer tables
65. • A line between the two tables
connects the Customer #
fields (the Customer table
primary key and the Sales
table foreign key).
• Click on Close to make the
Show Table window
disappear.
• To populate the bottom half of
the screen, double-click on
Sales Invoice #,
Salesperson, and Customer
Name or drag and drop them
into the Field row.
QUERY 1
66. • Access automatically checks
the box in the Show line
• Since we only want sales to D.
Ainge, enter that in the criteria
line of the Customer Name
column.
• Access will automatically put
question marks around the
criteria.
• Run the query by clicking on
the red ! (exclamation) mark
on the Query Tools Design
ribbon.
QUERY 1
67. • The query answer does not
automatically have the title
“Ainge Sales.”
• To assign the query a name,
save it by selecting File from
the Access menu, then Save
Object As, and then enter
“Ainge Sales” in the first line
of the Save As window,
making sure the Object select
box is set to “Query,” and
then clicking OK.
QUERY 1
68. • Query 2 answers this question:
How many televisions were
sold in October?
• The Sales, Inventory, and
Sales-Inventory tables contain
the three items needed to
answer this query: Date,
Inventory Description, and
Quantity.
QUERY 2
69. • Click on the “Query Design”
button in the Create ribbon and
select the three tables and the
three fields
• Since we want the quantity of
televisions sold in October, we
add the criteria “Between
#10/1/2018# And #10/31/2018#”
to the Date field and
“Television” to the Description
field
• To specify criteria, Access uses
operators such as “And,” “Or,”
and “Between.”
QUERY 2
70. • An “And” operator returns the
data that meets all the criteria
linked by “And” operators.
• The “Between” operator
selects all the data in October of
2018; that is, between and
including the first and last days
of the month.
• The “Or” operator returns data
that meets at least one of the
criteria linked by the “Or”
operators.
• The “#” symbol tells Access to
look for a date rather than some
other type of text.
QUERY 2
71. • Since we are only looking for total
television sales in October, uncheck
the “Show” box in the Date and
Description columns.
• To generate total sales, click the
“Totals” button in the Show/Hide
portion of the Query Tools Design
ribbon. A new row, labeled Total,
appears.
• Click on the Totals line in the
Quantity column, click on the down-
arrow symbol, and select Sum
from the drop- down menu that
appears. The remaining two fields in
the Total line will stay as Group By.
QUERY 2
72. • Query 3 answers this question:
What are the names and
addresses of customers buying
televisions in October?
• This query needs these fields:
Date, Description, and
Customer Name, Street, City,
and State.
QUERY 3
73. • All four tables are used because
the Sales-Inventory table is used
to move between the Sales and
Inventory tables.
• The query uses the same criteria
as Query 2.
• The Date and Description data do
not need to be displayed, so the
boxes in the Show line are
unchecked.
QUERY 3
74. • Query 4 answers this question:
What are the sales invoice
numbers, dates, and invoice totals
for October sales, arranged in
descending order by sale amount?
• Since the database does not
contain an Invoice Total column, it
is calculated by multiplying the unit
price by the quantity for each sale.
• Query 4 requires the Sales table,
Sales-Inventory table, and the
Inventory table
QUERY 4
75. • For example, we would calculate the
total sales price of each item sold by
multiplying the Quantity field in the
Sales-Inventory table by the Unit Price
field in the Inventory table. The Sales-
Inventory table in Table 4-5 shows that
three items were sold on Sales Invoice
102. For item 20, we multiply the
quantity (3) by the Unit Price (699),
producing 2,097. The same calculation
is made for items 10 and 30. Finally,
we sum the three item totals to get an
invoice total
QUERY 4
76. • However, some fields will not appear
in columns on the Select Query
window.
• Three columns are displayed: Sales
Invoice #, Date, and Invoice Total,
which we will calculate. The other
fields, Quantity and Unit Price, are
used in the Invoice Total calculations.
• To calculate Invoice Total, type
“Invoice Total:” in the first blank Field
cell, right-click in the cell, and select
Build from the pop-up menu that
appears.
QUERY 4
77. • An Expression Builder window
appears, where the formula to
calculate the Invoice Total is
entered by typing “Sum( )”.
Between the parentheses, click on
the + sign in front of the S&S In-
Chapter Database folder in the
Expressions Elements box.
QUERY 4
78. • Then clicking on the + sign in the
Tables folder causes the four
database tables to appear.
• Click on the Sales-Inventory table,
and the fields in the Sales-
Inventory table appear.
• Double-click on Quantity to put
this field in the expression.
• Note in Table 4-12 that the
expression shows the table name
and the field name, separated by
an exclamation point.
QUERY 4
79. • To multiply Quantity by Unit Price,
type * (the multiplication symbol)
and select the Inventory table and
the Unit Price field.
• The formula is now complete, and
the screen will appear as shown.
• To enter the expression into the
Select Query window, click on OK
QUERY 4
80. • To complete Query 4, click the
“Totals” button in the Query
Tools Design ribbon.
• Click on the down arrow in the
Total row of the Invoice Totals
column, and select Expression
from the pop-up menu.
• This tells Access to calculate
the indicated expression for all
items with the same sales
invoice number and date.
QUERY 4
81. • In the same column, click on the
down arrow in the Sort row, and
select Descending so that the
answer is shown in descending
Invoice Total order.
• In the criteria section of the Date
column, use the “Between” operator
to specify the month of October.
• Running Query 4 produces the
answer shown in Table 4-11.
QUERY 4
82. • Query 5 will answer this
question: What are total sales
by salesperson?
• This query is similar to Query
4, except that we total invoices
by salesperson rather than by
invoice number.
QUERY 5
84. Database Systems and Future of Accounting
Database systems expand accounting’s ability
to produce real-time dynamic reports of all
aspects of the accounting equation. Databases
are capturing increasing amounts of transaction
data beyond what was captured through
accounting journals and ledgers in double entry
accounting.
85. SUMMARY AND CASE CONCLUSION
● Database management system (DBMS), the software that makes a database system work, is
based on a logical data model that shows how users perceive the way the data is stored.
● Many DBMSs are based on the relational data model that represents data as being stored in
tables.
● Every row in a relational table has only one data value in each column. Neither row nor column
position is significant. These properties support the use of simple, yet powerful, query languages
for interacting with the database.
● The DBMS functions as an intermediary between the user and the database, thereby hiding the
complex addressing schemes actually used to retrieve and update the information stored in the
database.
● After reading Ashton’s report, Scott and Susan agreed that it was time to upgrade S&S’s AIS
and to hire a consulting firm to help select and install the new system.
86. CREDITS: This presentation template was created by Slidesgo,
including icons by Flaticon, and infographics & images by Freepik
Do you have any questions?
Thanks!