1. Organizing Data and Information
Indr Information Systems
Organizing Data and Information
Chapter 3

2. In this chapter, we will study:
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In this chapter, we will study:
Basic data management terminology
Storing data in traditional files and problems with this approach
The database approach to storing data
How data are organized to create a database
Comparison of database models
Components of a DBMS
How companies utilize their stored data

3. Organizing and Managing Data
A database helps an organization to
organize data and to generate information
access data more efficiently and use it more effectively
A database management system (DBMS)
consists of a group of programs that manipulate the database and other application programs

4. The Hierarchy of Data

5. Lowest Hierarchy of Data
Bit: A binary digit that represents a circuit that is either on or off.
Byte: Collection of 8 bits that represents a character.
Character: Letter, numeric digit or symbol
Upper case letters={A,B,...,Z}
Lower case letters={a,b,...,z}
Numeric digits={0,1,2,...,9}
Symbols={.,!,+,-,/,...}

6. Data Entities, Attributes, and Keys
Entity
People, places, or things for which data are collected, stored, and maintained
Attribute
Characteristic of an entity
Data item
Value of an attribute

7. Data Entities, Attributes, and Keys (continued)
Field or set of fields in a record that is used to identify the record
Primary key
Field or set of fields that uniquely identifies the record

8. Keys and Attributes
Primary key
Secondary keys

9. The Traditional Approach To Data Management

10. The Database Approach to Data Management

11. Advantages of the Database Approach
Improved strategic use of corporate data
Reduced data redundancy
Improved data integrity
Easier modification and updating
Data and program independence

12. Advantages of the Database Approach
Better access to data and information
Standardization of data
A framework for program development
Better overall protection of the data
Shared data and information resources

13. Disadvantages of the Database Approach

14. Database Development
When building a database, the following must be considered:
Content: What data should be collected and at what cost?
Access: What data should be provided to which users and when?
Logical structure: How should data be arranged?
Physical organization: Where should data be located?

15. The logical design of a database involves:
Data Modeling
The logical design of a database involves:
Grouping data items
Identifying relationships among different data items
The physical database design involves:
Implementing the logical design with possible modifications due to cost and performance considerations such as planned data redundancy

16. Data Modeling Data model: Data modeling involves
An abstract model of the logical relationships among data represented by a diagram of entities and their relationships
Data modeling involves
understanding a specific business problem and analyzing the data and information needed to deliver a solution
Enterprise data modeling
First, investigate needs at the strategic level
Second, examine more specific needs for various functional areas and departments

17. Data Modeling Entity-relationship diagrams Types of relationships:
Use basic graphical symbols to show the organization of data
Boxes indicate data items or entities
Connecting lines show relationships
Types of relationships:
One-to-one: a student has one schedule; a schedule belongs to one student
One-to-many: a professor has one or more courses, a course has one professor
Many-to-many: a student has one or more courses; a course has one or more students
Many-to-one:

18. Entity-Relationship Diagram for a Customer Ordering Database

19. Database Models Hierarchical (tree)
Organization Structure
Top-down; supports one-to-many relations
Pros and Cons
gives best processing speeds, but poor query flexibility

20. Hierarchical Database Model

21. Database Models Network Supports many-to-many relations Pros and Cons
Organization Structure
Supports many-to-many relations
Pros and Cons
gives pretty good processing speeds and pretty good query flexibility, but is very complex
difficult to modify

22. Network Database Model

23. Database Models Relational Models Organization Structure
Two-dimensional tables, called relations, are the logical equivalent of files
Tabular format: row=data entity; column=attributes
Domain: allowable values of each attribute
Pros and Cons
common in PC environment because it is simple to understand
provides high flexibility and ease of use
provides slower search and access times; a problem in high-volume business settings

24. Relational Database Model

25. Relational Models – Manipulating Data
Making inquiries and analyzing data
Selecting
Choosing rows according to certain criteria
Projecting
Choosing columns, can be used to create new tables
Joining
Combining two or more tables

26. Relational Models
Linking
Tables that share at least one common attribute can be linked to provide useful information and reports
Linking provides the flexibility associated with relational models

27. Linking Database Tables to Answer an Inquiry
Find hire date of the manager of Sales Manual project
Find Hire Date of the Manager of Sales Manual Project

28. Find the Name of the Manager of the Payroll Project
Example inquiries
Find the Name of the Manager of the Payroll Project
How long has the manager of the Accounting Department been working?
Find hire date of the manager of Sales Manual project

29. Building a Relational Database
Physically implement the data model in the database management system software
Create tables
Define fields and field properties
Establish primary keys
Define table relationships
Add actual data (records) to tables

30. Database Management Systems

31. Database Management Systems
A DBMS is responsible for access to a database and acts as a user interface
The DBMS can reference a schema to find where to access the requested data in relation to another piece of data

32. Overview of Database Types
Flat file
Simple database program whose records have no relationship to one another
Single user
Only one person can use the database at a time
Multiple user
Allows dozens or hundreds of people to access the same database system at the same time
Examples: Oracle, Sybase, and IBM

33. Providing a User View Schema - a description of the entire database
the logical and physical structure of the data
Subschema - a file that contains a description of a subset of the database and identifies which users can modify the data items in that subset
Subschemas are developed to create different user views
Examples?

34. The Use of Schemas and Subschemas

35. Creating and Modifying the Database
Data dictionary – detailed description of data in a database
Name of the data item, aliases, type of data, range, length in bytes, users who can access it, list of reports that use the data item

36. Creating and Modifying the Database
Schemas and subschemas are implemented via a data definition language
Data definition language (DDL) - a collection of instructions and commands used to define and describe data and data relationships in a specific database

37. Using a DDL to define a schema

38. Typical Uses of a Data Dictionary
Provide a standard definition of terms and data elements
Assist programmers in designing and writing programs
Simplify database modification
Reduce data redundancy
Increase data reliability
Speed program development
Ease modification of data and information

39. Data Dictionary Entry

40. Storing and Retrieving Data
One function of a DBMS
To be an interface between an application program and the database
Concurrency control
Method of dealing with a situation in which two or more people need to access the same record in a database at the same time

41. Storing and Retrieving Data
Physical and Logical Access Paths

42. Manipulating Data and Generating Reports
Query-by-example (QBE)
Visual approach to developing database queries or requests
Data manipulation language (DML)
Commands that manipulate the data in a database
Structured Query Language (SQL)
Standardized data manipulation language
Lets programmers learn one powerful query language and use it on systems ranging from PCs to the largest mainframe computers

43. Manipulating Data and Generating Reports
Data Manipulation Language (DML)
Allows users to access, modify data and make queries to generate reports
Example: SELECT * FROM EMPLOYEE WHERE JOB_CLASSIFICATION = “C2”
Structured Query Language (SQL)
developed in 70s and adopted by the American National Standards Institute as the standard query language for relational databases in 1986
Can be used on PCs as wells as mainframe computers

44. Examples of SQL Commands

45. Structured Query Language

46. Database Output

47. Exception, scheduled, and demand reports for urgent attention
Database Output
Exception, scheduled, and demand reports for urgent attention
Documents and reports
Form letters with address labels
Payroll checks and reports
Invoices
Orders for materials and supplies
Financial performance reports

48. Popular Database Management Systems
Relational DBMSs
Microsoft Access
Corel’s Paradox database
FileMaker Pro
Lotus Approach
Microsoft SQL
IBM DB2 and Informix
Oracle
Sybase
Open source DBMSs: My SQL, Berkeley DB, PostgreSQL, etc.

49. Popular Database Management Systems
Popular DBMSs for end users
Microsoft Access and FileMaker Pro
Emerging software
Database as a Service (DaaS) or Database 2.0
Database administration is provided by the service provider

50. Special-Purpose Database Systems
Morphbank
MorphBank is an open web repository of biological images documenting specimen-based research in comparative anatomy, morphological phylogenetics, etc.

51. Special-Purpose Database Systems
iTunes Store music and video catalog
The Internet Movie Database (IMDb)
an online database of information related to movies, actors, television shows, production crew personnel, video games, and most recently, fictional characters featured in visual entertainment media.
IMDb launched on October 17, 1990, and in 1998 was acquired by Amazon.com.

52. Selecting a Database Management System
Important characteristics of databases
Database size
Database cost
Concurrent users
Performance
Integration
Vendor

53. Selecting a Database Management System
Database size – e.g. 100 million bytes, terabytes
Number of concurrent users
Performance – speed, memory requirement
Integration – compatibility with OS
Features – security, documentation, etc.
Vendor – support
Cost – cost of ownership

54. Using Databases with Other Software
Database management systems are often used with other software packages or the Internet
Front-end application
Directly interacts with users
Back-end application
Interacts with applications

55. Open Database Connectivity (ODBC)
Integration
Open Database Connectivity (ODBC)
Standards for data transfer and access among different databases
Import, export data and link to other applications
provides a standard software API method for using database management systems (DBMS)
The designers of ODBC aimed to make it independent of programming languages, database systems, and operating systems

56. Advantages of ODBC

57. Linking Databases to the Internet
Semantic Web
Developing a seamless integration of traditional databases with the Internet
Allows people to access and manipulate a number of traditional databases at the same time through the Internet

58. Database Applications

59. Data Warehouses, Data Marts, and Data Mining
Data Warehouse - a database that collects business information from many sources in the enterprise, covering all aspects of the company’s processes, products, and customers.
Data Mart – a subset of a data warehouse.
Data Mining - an information analysis tool that involves the automated discovery of patterns and relationships in a data warehouse.

60. Elements of a Data Warehouse

61. Data Mining
An information extraction activity whose goal is to discover hidden facts contained in databases
Data mining finds patterns and subtle relationships in data and infers rules that allow the prediction of future results
It is extensively used in marketing to improve customer retension, cross-selling opportunities, campaign management, market, channel and pricing analysis, customer segmentation analysis, etc.

62. Types of information output
Classification
Infer to the defining characteristics
Ex. for classification: classify transactions as fraudulent or not, Classify a credit application as high or low risk (dependent variable is discrete)
2. Clustering
Identify groups sharing characteristics
Ex: Customer segmentation as A, B, C (class labels are not known.)

63. Types of information output
3. Association
Identify relationships at one time
Ex: age between (20-29) ^ income( TL/month) à buys(X, “PC”) [support = 2%, confidence = 60%]
4. Sequencing
Identify relationships recurring over time. Observes similarities in sequential patterns
5. Forecasting
Estimate future values based on patterns in data
Ex. for prediction: How much does a 60 yrs old woman, from Kansascity is likely to spend in the casino? (dependent variable is continuous)

64. Data mining What can we learn (examples)?
Market segments and customer characteristics
Customer buying patterns
Fraudulent behavior
Evaluation of retail promotions
Credit risk analysis
What is not data mining?
query processing.
Expert systems or small ML/statistical programs

65. Data Mining vs. Data Query
A list of all customers who use a credit card to buy a PC
A list of all students having a GPA of 3.5 or higher and has studied 4 or less semesters
Data Mining problems:
What is the likelihood of a customer purchasing PC with credit card
Given the characteristics of MIS students predict her SPA in the comming term
What are the characteristics of MIS undergrad students

66. Techniques
Case-based reasoning is the process of solving new problems based on the solutions of similar past problems.
Ex: An auto mechanic who fixes an engine by recalling another car that exhibited similar symptoms is using case-based reasoning. Neural computing (machine learning)
Intelligent agents
Association analysis
Express statistical relationships
Others
Decision trees, genetic algorithms

67. Business Intelligence
Gathering enough of the right information in a timely manner and usable form
Analyzing information to generate positive impact on business strategy, tactics or operations
Competitive intelligence -> about competitors
Counter-intelligence -> protect information
Knowledge management -> record and share knowledge

68. Online Analytical Processing (OLAP)
Multidimensional databases and their analytical information display systems
OLAP servers and desktop tools allow high-speed analysis of data involving complex relationships
Ex: combinations of products, regions, channels of distribution, reporting units, time periods

69. Comparison of OLAP and Data Mining

70. Locating Data in Databases
Centralized vs. Distributed
Choice will affect user accessibility, query response time, data entry, security, and cost

71. Locating Data in Databases (continued)

72. Locating Data in Databases
Centralized database
All the related files are in one physical location
Provides database administrators with the ability to work on a database as a whole at one location
Data consistency is improved and security is easier
Files are only accessible via the centralized host computer
Recovery from disasters is easier
Vulnerable to a single point of failure
Speed problem due to transmission delays

73. Locating Data in Databases (continued)
Distributed database
Complete copies of a database, or portions of a database, are in more than one location, close to the user
Type 1: Replicated database
Copies of database in many locations
Reduced single-point-of-failure problems
Increased user access responsiveness
Type 2: Partitioned databases
A portion of the database in each location
Each location responsible for its own data

74. Object-Relational Database Management System
Provides a set of relational database capabilities plus the ability for third parties to add new data types and operations to the database.
Stores the following types of data as objects:
audio
images
Unstructured text
text
spatial data
Operations are indexing, optimization and retrieval.

75. Spatial Technology

76. Summary Database – data organized in files
Entity - a generalized class of objects for which data is collected, stored, and maintained.
Attribute - a characteristic of an entity.
DBMS - a group of programs used as an interface between a database and application programs.
Data mining - the automated discovery of patterns and relationships in a data warehouse.