1. Data Resource Management Data Concepts Database Management Types of Databases Chapter 5 McGraw-Hill/Irwin Copyright © 2007 by The McGraw-Hill Companies, Inc. All rights reserved.

2. Examples of logical data elements in information systems

3. Fundamental Data Concepts Character: single alphabetic, numeric or other symbol Field or data item: a grouping of related characters – Represents an attribute (a characteristic or quality) of some entity (object, person, place or event) – Example: salary Record: grouping of all the fields used to describe the attributes of an entity – Example: payroll record with name, SSN and rate of pay

4. Fundamental Data Concepts File or table: a group of related records Database: an integrated collection of logically related data elements

5. Electric Utility Database Figure 5.3 outlines some of the entities and relationships in a database for an electric utility. Also shown are some of the business applications (billing, payment processing) that depend on access to the data elements in the database.

6. Database Structures Hierarchical Network Relational Object-oriented

7. Hierarchical Structure Early DBMS structure Records arranged in tree-like structure Relationships are one-to-many, since each data element is related to only one element above it.

8. Hierarchical Structure

9. Network Structure Used in some mainframe DBMS packages Many-to-many relationships

10. Network Structure

11. Relational Structure Most widely used structure Data elements are viewed as being stored in tables Row represents record Column represents field Can relate data in one file with data in another file if both files share a common data element

12. Relational Structure

13. Relational Operations Three basic operations on relational databases: Select: – Create a subset of records that meet a stated criterion – Example, select employees who make more than $30,000 Join – Combine two or more tables temporarily – Looks like one big table Project – Create a subset of columns in a table

14. Multidimensional Structure Variation of relational model Data elements are viewed as being in cubes

15. Multidimensional Model

16. Object-oriented Structure Encapsulate data and their operations in one objects. Object consists of – Data values describing the attributes of an entity – Operations that can be performed on the data Encapsulation: – Combine data and operations Inheritance: – New objects can be created by replicated some or all of the characteristics of parent objects Supports complex data types – Examples, graphic images, video clips, web pages

17. Object-oriented Structure Source: Adapted from Ivar Jacobsen, Maria Ericsson, and Ageneta Jacobsen, The Object Advantage: Business Process Reengineering with Object Technology (New York: ACM Press, 1995), p. 65. Copyright @ 1995, Association for Computing Machinery. By permission.

18. Evaluation of Database Structures Hierarchical – Worked for structured routine transaction processing – Can’t handle many-to-many relationships Network – More flexible than hierarchical – Unable to handle ad hoc requests Relational – Easily respond to ad hoc requests – Easier to work with and maintain – Not as efficient or quick as hierarchical or network

19. Data Planning and Modeling Enterprise Model (Data Planning) – Defines basic business process of the enterprise – Defined by DBAs and designers with end users Data Modeling – A process of identifying and defining the relationships between data elements by Developing ERD – The Entity Relationship Diagram (ERD) is simply graphical model of the various files and their relationships, contained within a database system. – There are three modeling levels: conceptual, logical, physical

20. Data Planning and Modeling

21. Entity Relationship Diagram

22. Conceptual model Conceptual model is a summary- level data model identifies the highest-level relationships between the different entities. 5-22

23. Logical model – Logical model is a fully- attributed data model describes the details of our data without regard to how they will actually implemented in the database – The overall logical model view of relationships is called schema. – The logical model view to support specific application programs is called subschema. 5-23

24. Physical model - Physical model is a fully- attributed data model describes how to implement our data model in the database - Or How data are to be stored and accessed on storage devices 5-24

25. Section II: Types of databases

26. Operational Databases Store detailed data to support business processes and operations Examples, customer database, inventory database and human resource database

27. Distributed Databases Copies or parts of databases on servers at a variety of locations. Advantages: – to improve database performance at end-user worksites. – protection of valuable data Challenges: – any data change in one location must be made in all other locations – extra computing power and bandwidth necessary to access multiple databases in multiple locations. Replication: – Look at each distributed database and find changes – Apply changes to each distributed database – Very complex, so it can consume a lot of time and computer resources Duplication – One database is master – Duplicate that database after hours in all locations – Easier and much less complicated

28. External Databases Databases available for a fee from commercial online services or For free from World Wide Web Examples, statistical databanks, bibliographic and full text databases

29. Hypermedia Database External website database Consists of hyperlinked pages of multimedia (text, graphics, video clips, audio segments)

30. Data Warehouse Stores data that has been extracted from the operational, external and other databases Data has been cleaned, transformed and cataloged unlike a typical database in which changes can occur constantly, data in a data warehouse are static. Used by managers and professionals for – Data mining, – Online analytical processing, – Business analysis, – Market research, – Decision support Data mart is subset of warehouse for specific use of department

31. Data Mining Data in data warehouse are analyzed to reveal hidden patterns and trends Data mining can discover new correlations, patterns, and trends in vast amounts of business stored in data warehouses. Examples: – Perform market-basket analysis to identify new business processes – Find root causes to quality problems – Cross sell to existing customers – Profile customers with more accuracy

32. Traditional File Processing Data stored in independent files Problems: – Data redundancy: duplicated data in several files, caused problems when data had to be updated. – Lack of Data Integration: it difficult to provide end users with information for ad hoc requests that required accessing data stored in several different files.

33. Database Management Approach Consolidate data into databases that can be accessed by different programs the main software tool of the database management approach is a database management system (DBMS). DBMS serves as interface between users and databases. This helps users easily access the data in a database. Example on DBMS that allow you to set up and manage databases on microcomputers : Microsoft Access, Lotus Approach, or Corel Paradox Examples of popular mainframe and server versions of DBMS software are Oracle 10g by Oracle Corp., MySQL, a popular open-source DBMS, and IBM’s DB2 Universal Database,.

34. DBMS

35. DBMS Major Functions The three major functions of a database management system are – Database Interrogation. – Database Maintainence. – Database Development and Database application development.

36. Database Interrogation – Database Interrogation:to access the data in a database to selectively retrieve and display information and produce reports, forms, and other documents. is the primary use of a database by end users

37. Database Interrogation End users use a DBMS by asking for information via a query or a report generator Query language – immediate responses to ad hoc data requests – SQL (Structured Query Language) an international standard query language. SQL language is a combination of DDL, DML and the SELECT statement – Graphical Queries -- Point-and-click methods – Natural Queries – similar to conversational English Graphical and Natural Queries are translated by the software into SQL commands. Report generator – quickly specify a report format for information you want to present as a report

38. Natural Language versus SQL

39. Graphical Query Source: Courtesy of Microsoft Corp.

40. Database Maintenance Database maintenance involves using transaction processing systems with support of DBMS to add, delete, update, and correct the data in a database. Updating database to reflect new business transactions such as a new sale.

41. Database Development Database development: involves defining and organizing the content, relationships, and structure of the data needed to build a database. Metadata: data definitions and specifications of the data contents, relationships and structure – The metada are stored in data dictionary and maintained by DBA – Data dictionary: Data base catalog containing metadata Data Definition Language (DDL): – Develop and specify the data contents, relationships and structure and modify the database specifications when necessary Database Administrator (DBA) – In charge of database development and maintaining the metadata using the DDL

42. Database application Development Database application development involves using a DBMS software development tools to develop prototypes of queries, data entry screens, forms, reports, and Web pages for a proposed custom business application that accesses a company database to find and update the data it needs. DBMS can include features such as data manipulation language (DML) to perform necessary data-handling activities. Data Manipulation Language (DML): modify stored data but not the schema or database objects. Manipulation of persistent database objects, e.g., tables or stored procedures, via the SQL schema statements, rather than the data stored within them, is considered to be part of a separate data definition language(DDL)