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Data Resource Management

Data Resource Management. Lecture 9. Database Structures. Hierarchical Structure Network Structure Relational Structure Multidimensional Structure Object-Oriented Structure. 3 – Relational Structure.

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Data Resource Management

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  1. Data Resource Management Lecture 9

  2. Database Structures • Hierarchical Structure • Network Structure • Relational Structure • Multidimensional Structure • Object-Oriented Structure

  3. 3 – Relational Structure • All data elements within the database are viewed as being stored in the form of simple tables • Represents data as two-dimensional tables called relations • Relates data across tables based on common data element • Relation is described as a table

  4. Relational Structure • Table  Relation • Row  Tuple • Column  Attribute

  5. Three basic operations in Relational Database • Select Creates subset of rows that meet specific criteria • Join Combines relational tables to provide users with information • Project Enables users to create new tables containing only relevant information

  6. Relational Database

  7. Relational Database

  8. Three basic operations in RDBMS

  9. 4 – Multidimensional Data Model • Multidimensional databases combine data from a multitude of data sources. • Multi-dimensional databases are especially useful in sales and marketing applications that involve time series. • Large volumes of sales and inventory data can be stored to ultimately be used for logistics and executive planning. • For example, data can be more readily segregated by sales region, product, or time period.

  10. Multidimensional Data Model • The data cube is a conceptual representation of database which can be implemented in a variety of ways, including top-down, bottom-up, and arrays.

  11. 5 – Object-Oriented Databases • Object-oriented DBMS: Stores data and procedures as objects that can be retrieved and shared automatically • Object-relational DBMS: Provides capabilities of both object-oriented and relational DBMS

  12. Object-Oriented Databases • Can accommodate more complex data types including graphics, pictures, voice and text • Encapsulation – data values and operations that can be performed on them are stored as a unit • Inheritance – automatically creating new objects by replicating some or all of the characteristics of one or more existing objects

  13. Object-Relational DBMS • An object-relational database (ORD) or object-relational database management system (ORDBMS) is a database management system (DBMS) similar to a relational database, but with an object-oriented database model: objects, classes and inheritance • In addition, it supports extension of the data model with custom data-types and methods.

  14. Evaluation of Database Structures • Hierarchical data structure is best for structured, routine types of transaction processing. • Network data structure is best when many-to-many relationships are needed. • Relational data structure is best when ad hoc reporting is required.

  15. Types of Databases • Conceptual categories of databases • Operational Databases • Distributed Databases • External Databases • Hypermedia Databases

  16. 1 – Operational Databases • Store detailed data needed to support the business processes and operations of a company • Also called Subject Area Databases (SADB), Transaction Databases, Production Databases

  17. 2 – Distributed Databases • Many organizations replicate and distribute copies or parts of databases to network servers at variety of sites • Distributed databases can reside on network servers on world wide web, corporate intranets or extranets, or on any other company network • Distributed databases may be copies of operational or any other type of databases. • Replication and distribution is done to improve database performance at end user worksites

  18. Distributed Databases • Centralized Database • Used by single central processor or multiple processors in client/server network • Distributed Database • Stored in more than one physical location • Partitioned database • Duplicated database

  19. Distributed Databases

  20. 3 – External Databases • Contain a wealth of information available from commercial online services and from many sources on the World Wide Web

  21. 4 – Hypermedia Databases • Hypermedia database consists of hyperlinked pages of multimedia (text, graphic, and images, video clips etc.)

  22. Data Warehouse Definition: • Large database that stores data that has been extracted from the various operational, external, and other databases of an organization • Central source of data that has been • Cleaned • Transformed • Cataloged

  23. Data Warehouse • Stored data is used for • Data Mining • Online Analytical Processing • Market Research • Decision Support • Data Warehouses are subdivided into: • Data Marts Databases that hold subsets of data from a data warehouse that focus on specific aspects of a company, such as a department or a business process

  24. Data Warehouse • Supports reporting and query tools • Stores current and historical data • Consolidates data for management analysis and decision making • Improved and easy accessibility to information • Ability to model and remodel the data

  25. Data Warehouse & Data Marts

  26. Data Warehouse & Data Marts

  27. Retrieving Information from Data Warehouse

  28. Database Management Software (DBMS) Definition: • Software that controls the creation, maintenance, and use of databases Use of DBMS

  29. Database Maintenance • Updating a database continually to reflect new business transactions and other events • Updating a database to correct data and ensure accuracy of the data

  30. Database Interrogation Definition: • Capability of a DBMS to report information from the database in response to end users’ requests • Query Language – allows easy, immediate access to ad hoc data requests • Report Generator - allows quick, easy specification of a report format for information users have requested

  31. Database Query vs. Report

  32. References • Management Information Systems, 7th Edition, James A. O’Brien, George M. Marakas. Chapter 5

  33. Data Mining Definition: • Analyzing the data in a data warehouse to reveal hidden patterns and trends in historical business activity

  34. Data Mining Uses • Perform “market-basket analysis” to identify new product bundles. • Find root causes to quality or manufacturing problems. • Prevent customer attrition and acquire new customers. • Cross-sell to existing customers. • Profile customers with more accuracy. Back

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