1. Data Resource Management Lecture 8

2. Traditional File Processing Data are organized, stored, and processed in independent files of data records In traditional file processing a flat file is used to contain records, and in which each record is specified in a single line. Fields from each record may simply have a fixed width Delimited by whitespace, tabs, commas (CSV) or other characters. There are no structural relationships.

3. Traditional File Processing

4. Problems with Traditional File Processing Data redundancy Lack of data integration Program-Data dependence Lack of flexibility Poor security Lack of data-sharing and availability

5. Database Management System A software that permits an organization to centralize data, manage them efficiently, and provide access to the stored data by application programs Act as an interface between application programs and the physical data files DBMS relieves the end user from understanding where and how the data is actually stored DBMS separates logical and physical view of data

6. Database Management System Logical view presents data as it is perceived by the end user Physical view shows how data is actually organized and structured on the physical storage media DBMS enables organizations to  Reduce data redundancy  Reduce inconsistency  Uncouples program and data  Increase access and availability of information  Centrally manage data and security

8. Database Management System

9. Benefits of DBMS Improved strategic use of corporate data Reduced complexity of the organization’s information systems environment Reduced data redundancy and inconsistency Enhanced data integrity Application-data independence Improved security Reduced application development and maintenance costs Improved flexibility of information systems Increased access and availability of data and information Logical & Physical data independence Provides central control on the system through DBA.

10. Management Requirements for Database Systems

11. Foundation Data Concepts Fundamental concepts about how data are organized in information systems A conceptual framework of several levels of data, that differentiates between different groupings or elements, of data Data may be logically organized into  Characters  Fields  Records  Files  Databases

12. Logical data elements in IS Character  Basic logical data element  Character consists of a single alphabetic, numeric or other symbol  Physical storage elements – Bits and bytes Field  Field or data item  Field consists of grouping of related characters  Field represents an attribute of some entity  For example: Employee’s Salary

13. Entity: Person, place, thing, event about which information is maintained Attribute: Description of a particular entity Key field: Identifier field used to retrieve, update, sort a record

14. Logical data elements in IS Record  Related fields of data are grouped to form a record  Record represents a collection of attributes that describe an entity Fixed-length  Contain fixed number of fixed-length data fields Variable-length  Contain a variable number of fields and field lengths

15. Logical data elements in IS File  A group of related records is a data file, or table Database  A database is an integrated collection of logically related data elements  Data stored in database is independent of application programs using it  Database contain data elements describing entities and relationships among them

16. Logical data elements in IS

17. Entities and Relationships

18. Database Structures 1. Hierarchical Structure 2. Network Structure 3. Relational Structure 4. Multidimensional Structure 5. Object-Oriented Structure

20. 1 – Hierarchical DBMS Organizes data in a tree-like structure Structure allows repeating information using parent/child relationships: each parent can have many children but each child only has one parent. Supports one-to-many parent-child relationships All attributes of a specific record are listed under an entity type.

21. Hierarchical DBMS

23. 2 – Network DBMS The network model is a database model conceived as a flexible way of representing objects and their relationships. The network model allows each record to have multiple parent and child records, forming a net structure. This model allows more natural modeling of relationships between entities. Although the model was widely implemented and used, it failed to become dominant as it was eventually displaced by the relational model, which offered a higher-level, more declarative interface.

24. Network DBMS Depicts data logically as many-to-many relationships Data can be accessed by one of several paths because any data element or record can be related to any number of other data elements

26. Hierarchical & Network DBMS Disadvantages  Outdated  Less flexible compared to RDBMS  Lack support for ad-hoc and English language-like queries

27. 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

28. Relational Structure Table  Relation Row  Tuple Column  Attribute

29. Examples Customer(Customer ID, Tax ID, Name, Address, City, State, Zip, Phone) Order(Order No, Customer ID, Invoice No, Date Placed, Date Promised, Terms, Status) Order Line(Order No, Order Line No, Product Code, Qty) Invoice(Invoice No, Customer ID, Order No, Date, Status) Invoice Line(Invoice No, Line No, Product Code, Qty Shipped) Product(Product Code, Product Description)

30. Relational Data Model

31. 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

32. Three basic operations in Relational Database

33. 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. 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.

34. Multidimensional Data Model

36. 5 – Object-Oriented Databases In an object database (also object oriented database), information is represented in the form of objects as used in object-oriented programming. When database capabilities are combined with object programming language capabilities, the result is an object database management system (ODBMS). An ODBMS makes database objects appear as programming language objects in one or more object programming languages.

37. 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

38. 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

40. 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.

41. 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.

42. SQL Commands

43. Basic SQL Commands SELECT: Specifies columns FROM: Identifies tables or views WHERE: Specifies conditions

44. Select Statement

45. Conditional Selection

46. Projection & Join Statement

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