1. Database Systems and Applications
Chapter 3
Database Systems and Applications

2. Principles and Learning Objectives: Data Management and Modeling
Data management and modeling are key aspects of organizing data and information
Define general data management concepts and terms, highlighting the advantages of the database approach to data management
Describe logical and physical database design considerations, the function of data centers, and the relational database model

3. Principles and Learning Objectives: Database Support in Decision Making
A well-designed and well-managed database is an extremely valuable tool in supporting decision making
Identify the common functions performed by all database management systems, and identify popular database management systems

4. Principles and Learning Objectives: Evolving Database Applications
The number and types of database applications will continue to evolve and yield real business benefits
Identify and briefly discuss business intelligence, data mining, and other database applications

5. Why Learn About Database Systems and Applications?
A huge amount of data is captured for processing by computers every day
Learning about database systems and applications can help you make the most effective use of information
Databases and applications to extract and analyze valuable information can help you succeed in your career

6. Introduction Database: an organized collection of data
A database management system (DBMS) is a group of programs that:
Manipulate the database
Provide an interface between the database and its users and other application programs

7. Data Management Without data and the ability to process it:
An organization could not successfully complete most business activities
Data consists of raw facts
Data must be organized in a meaningful way to transform it into useful information

8. The Hierarchy of Data
A bit (binary digit) represents a circuit that is either on or off
A byte is made up of eight bits
Each byte represents a character
Field: a name, number, or combination of characters that describes an aspect of a business object or activity

9. The Hierarchy of Data (cont’d.)
Record: a collection of related data fields
File: a collection of related records
Database: a collection of integrated and related files
Hierarchy of data: bits, characters, fields, records, files, and databases

10. An Example of Hierarchy of Data
FIGURE 3.1 Hierarchy of data
Together, bits, characters, fields, records, files, and databases form the hierarchy of data.

11. Data Entities, Attributes, and Keys
Entity: a person, place, or thing for which data is collected, stored, and maintained
Attribute: a characteristic of an entity
Data item: the specific value of an attribute
Primary key: a field or set of fields that uniquely identifies the record

12. Keys and Attributes FIGURE 3.2 Keys and attributes
The key field is the employee number. The attributes include last name, first name, hire date, and department number.

13. The Database Approach Traditional approach to data management
Each distinct operational system used data files dedicated to that system
Database approach to data management
Information systems share a pool of related data

14. Database Approach to Data Management
FIGURE 3.4 Database approach to data management
In a database approach to data management, multiple information systems share a pool of related data.

15. Data Centers, Data Modeling and Database Characteristics
Considerations when building a database
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 so that it makes sense?
Physical organization: where should data be physically located?

16. Data Modeling
Data model: a diagram of data entities and their relationships
Enterprise data modeling: data modeling done at the level of the entire enterprise
Entity-relationship (ER) diagrams: data models that use basic graphical symbols to show the organization of and relationships between data

17. Entity-Relationship (ER) Diagram for a Customer Order Database
FIGURE 3.5 Entity-relationship (ER) diagram for a customer order database
Development of ER diagrams helps ensure that the logical structure of application programs is consistent with the data relationships in the database.

18. Relational Database Model
Relational model: a simple but highly useful way to organize data into collections of two-dimensional tables called relations
Relational model databases include:
Oracle, IBM DB2, Microsoft SQL Server, Microsoft Access, MySQL, and Sybase
Domain: range of allowable values for a data attribute

19. Relational Database Model (cont’d.)
FIGURE 3.6 Relational database model
In the relational model, data is placed in two-dimensional tables, or relations. As long as they share at least one common element, these relations can be linked to provide output useful information.

20. Manipulating Data
Selecting: eliminating rows according to certain criteria
Projecting: eliminating columns in a table
Joining: combining two or more tables
Linking: combining two or more tables through common data attributes to form a new table with only the unique data attributes

21. Simplified ER Diagram FIGURE 3.7 Simplified ER diagram
This diagram shows the relationship among the Manager, Department, and Project tables.

22. Linking Data Tables to Answer an Inquiry
FIGURE 3.8 Linking data tables to answer an inquiry
To find the name and hire date of the manager working on the sales manual project, the president needs three tables: Project, Department, and Manager. The project description (Sales manual) leads to the department number (598) in the Project table, which leads to the manager’s SSN ( ) in the Department table, which leads to the manager’s name (Fiske) and hire date ( ) in the Manager table.

23. Data Cleansing Also called data cleaning or data scrubbing
The process of detecting and then correcting or deleting incomplete, incorrect, inaccurate, irrelevant records that reside in a database
The cost of performing data cleansing can be quite high

24. Database Management Systems
Creating and implementing the right database system ensures that the database will support both business activities and goals
Capabilities and types of database systems vary considerably

25. Overview of Database Types
Single-user DBMS
Installed on a personal computer and meant for a single user
Examples: Microsoft Access and InfoPath, Lotus Approach, and Personal Oracle
Multiple-user DBMS
Allows dozens or hundreds of people to access the same system at the same time
Vendors: Oracle, Microsoft, Sybase, and IBM

26. Overview of Database Types (cont’d.)
Flat file
Simplest database program
The records have no relationship to one another
Store and manipulate a single table or file
Examples: OneNote and Evernote

27. SQL Databases
SQL: a special-purpose programming language for accessing and manipulating data stored in a relational database
SQL databases conform to ACID properties:
Atomicity, consistency, isolation, and durability
1986: SQL was adopted by ANSI as the standard query language for relational databases

28. Table 3.1 Examples of SQL Commands
Description
SELECT ClientName, Debt FROM Client WHERE Debt > 1000
This query displays all clients (ClientName) and the amount they owe the company (Debt) from a database table called Client for clients who owe the company more than $1,000 (WHERE Debt > 1000).
SELECT ClientName, ClientNum, OrderNum FROM Client, Order WHERE
Client.ClientNum=Order.ClientNum
This command is an example of a join command that combines data from two tables: the Client table and the Order table (FROM Client, Order). The command creates a new table with the client name, client number, and order number (SELECT ClientName, ClientNum, OrderNum). Both tables include the client number, which allows them to be joined. This ability is indicated in the WHERE clause, which states that the client number in the Client table is the same as (equal to) the client number in the Order table (WHERE Client.ClientNum=Order.ClientNum).
GRANT INSERT ON Client to Guthrie
This command is an example of a security command. It allows Bob Guthrie to insert new values or rows into the Client table.
TABLE 3.1 Examples of SQL commands

29. NoSQL Databases
A database designed to store and retrieve data in a manner that does not rigidly enforce the atomic conditions associated with the relational database model
Provides faster performance and greater scalability
Examples
Cassandra used by Facebook
DynamoDB used by Amazon

30. Visual, Audio, and Other Database Systems
Visual databases store images of charge slips, X-rays, and vital records
Images can be stored in some object-relational databases or special-purpose database systems
Spatial databases provide location-based services
Maps are embedded into a Web site’s Web applications and operational systems

31. Database Activities Providing a user view of the database
Adding and modifying data
Storing and retrieving data
Manipulating the data and generating reports

32. Providing a User View Schema: a description of the entire database
A schema can be part of the database or a separate schema file
The DBMS can reference a schema to find where to access the requested data in relation to another piece of data

33. Creating and Modifying the Database
Data definition language (DDL)
A collection of instructions and commands used to define and describe data and relationships in a specific database
Allows the database’s creator to describe data and relationships that are to be contained in the schema
Data dictionary: a detailed description of all the data used in the database

34. Data Definition Language
FIGURE 3.13 Data definition language
You use a data definition language to define a schema.

35. Data Dictionary Entry FIGURE 3.14 Data dictionary entry
A data dictionary provides a detailed description of all data used in the database.

36. Storing and Retrieving Data
When an application program needs data, it requests the data through the DBMS
Concurrency control deals with the situation in which two or more users or applications need to access the same record at the same time

37. Logical and Physical Access Paths
FIGURE 3.15 Logical and physical access paths
When an application requests data from the DBMS, it follows a logical access path to the data. When the DBMS retrieves the data, it follows a path to the physical access path to the data.

38. Manipulating Data and Generating Reports
Query by Example (QBE) is a visual approach to developing database queries or requests
Data manipulation language (DML): a specific language, provided with a DBMS
Allows users to access and modify the data, to make queries, and to generate reports

39. Manipulating Data and Generating Reports (cont’d.)
Once a database has been set up and loaded with data, it can produce reports, documents, and other outputs
A DBMS can produce a wide variety of documents, reports, and other output that can help organizations achieve their goals

40. Database Administration
DBA: a skilled and trained IS professional
Works with users to define their data needs
Applies database programming languages to craft a set of databases to meet those needs
Tests and evaluates databases
Implements changes to improve their databases’ performance
Assures that data is secure from unauthorized access

41. Database Administration (cont’d.)
Data administrator: a nontechnical position responsible for defining and implementing consistent principles for a variety of data issues
The data administrator can be a high-level position reporting to top-level managers

42. Table 3.2 Popular Database Management Systems
Open-Source Relational DBMS
Relational DBMS for Individuals and Workgroups
Relational DBMS for Workgroups and Enterprise
NoSQL DBMS
MySQL
Microsoft Access
Oracle
Mongo DB
PostgreSQL
IBM Lotus Approach
IBM DB2
Cassandra
MariaDB
Google Base
Sybase
Redis
SQL Lite
OpenOffice Base
Teradata
CouchDB
Microsoft SQL Server
Progress OpenEdge
TABLE 3.2 Popular database management systems

43. Popular Database Management Systems
Database as a Service (DaaS)
The database is stored on a service provider’s servers
The database is accessed by the client over a network, typically the Internet
Database administration is handled by the service provider
Example of DaaS: Amazon Relational Database Service (Amazon RDS)

44. Using Databases with Other Software
DBMSs can act as front-end or back-end applications
Front-end applications interact directly with people
Back-end applications interact with other programs or applications
Spin-off database applications include:
Big data, data warehouses and data marts, and business intelligence

45. Big Data Extremely large and complex data collections
Traditional data management software, hardware, and analysis processes are incapable of dealing with them
Three characteristics of big data
Volume
Velocity
Variety

46. Table 3.3 Big Data Generators
Source
Magnitude of Data Generated
Large Hadron particle accelerator at CERN
40 terabytes of data per second
Commercial aircraft engines
More than 1 petabyte per day of sensor data
Cell phones
More than 5 billion people worldwide are making cell phone calls, exchanging text messages, and accessing Web sites
YouTube
48 hours of video uploaded per minute
Facebook
100 terabytes uploaded per day
Twitter
500 million tweets per day
RFID tags
1,000 times the volume of data generated by bar codes
TABLE 3.3 Big data generators

47. Challenges of Big Data How to choose what subset of the data to store
Where and how to store the data
How to find the nuggets of data that are relevant to the decision making at hand
How to derive value from the relevant data

48. In-Memory Databases
A database management system that stores the entire database in random access memory (RAM)
Enable the analysis of big data and other challenging data-processing applications
Perform best on multiple multicore CPUs

49. Data Warehouses and Data Marts
Data warehouse: a large database that collects business information from many sources in the enterprise in support of management decision making
ETL process
Extract
Transform
Load

50. Elements of a Data Warehouse
FIGURE 3.21 Elements of a data warehouse
A data warehouse can help managers and executives relate information in innovative ways and make better decisions.

51. Data Warehouses and Data Marts (cont’d.)
Data mart: a subset of a data warehouse that is used by small- and medium-sized businesses and departments within large companies to support decision making
A specific area in the data mart might contain greater detailed data than the data warehouse

52. Business Intelligence
A broad range of technologies and applications
Enabling an organization to transform mostly structured data obtained from information systems to perform analysis, generate information, and improve the decision making of the organization

53. Business Intelligence (cont’d.)
Technologies include:
Data mining
Online analytical processing
Predictive analytics
Data visualization
Competitive intelligence

54. Data Mining
An information-analysis tool that involves the automated discovery of patterns and relationships in a data warehouse
Provides bottom-up, discovery-driven analysis

55. Online Analytical Processing (OLAP)
A form of analysis that allows users to explore data from a number of perspectives, enabling a style of analysis known as “slicing and dicing”
Provides top-down, query-driven data analysis

56. Table 3.6 Comparison of OLAP and Data Mining
Characteristic
OLAP
Data Mining
Purpose
Supports data analysis and decision making
Type of analysis supported
Top-down, query-driven data analysis
Bottom-up, discovery-driven data analysis
Skills required of user
Must be very knowledgeable of the data and its business context
Must trust in data-mining tools to uncover valid and worthwhile hypotheses
TABLE 3.6 Comparison of OLAP and data mining

57. Predictive Analysis Also called predictive analytics
A form of data mining that combines historical data with assumptions about future conditions to predict outcomes of events, e.g., future product sales or the probability that a customer will default on a loan

58. Data Visualization
In analyzing data, charts and graphs make it easier to:
See trends and patterns
Identify opportunities for further analysis
Software examples
Excel and SAS Visual Analytics

59. Data Visualization: Social Graph Analysis
A data visualization technique in which data is represented as networks
Vertices are the individual data points (social network users)
Edges are the connections among the vertices

60. Data Visualization: Key Performance Indicators (KPIs) and Dashboards
KPIs: quantifiable measurements that assess progress toward organizational goals and reflect the critical success factors of an organization
Dashboard: a data visualization tool that displays the current status of the key performance indicators (KPIs) for an organization

61. Competitive Intelligence
Competitive intelligence encompasses information about competitors and the ways that knowledge affects strategy, tactics, and operations
Counterintelligence describes the steps an organization takes to protect information sought by “hostile” intelligence gatherers

62. Summary – Principle 1
Data is one of the most valuable resources that a firm possesses
An entity is an object for which data is collected, stored, and maintained
Database considerations: content, access, logical structure, and physical organization
The relational model places data in two-dimensional tables

63. Summary – Principle 2
A DBMS is a group of programs used as an interface between a database and its users and other application programs
DBMS basic functions include:
Providing user views
Creating and modifying the database
Storing and retrieving data
Manipulating data and generating reports

64. Summary – Principle 2 (cont’d.)
After a DBMS has been installed, the database can be accessed, modified, and queried via a data manipulation language
A database administrator (DBA) plans, designs, creates, operates, secures, monitors, and maintains databases
Database as a Service (DaaS) is a new form of database service

65. Summary – Principle 3
“Big data” is the term used to describe enormous and complex data collections
Data warehouses are relational DBMSs specifically designed to support management decision making

66. Summary – Principle 3 (cont’d.)
Data mining allows the automated discovery of patterns and relationships in a data warehouse
Counterintelligence describes the steps an organization takes to protect information sought by “hostile” intelligence gatherers