311: Management Information Systems Database Systems Chapter 3

311: Management Information Systems

Database Systems

Chapter 3

© Jakob Iversen, 20022

Database

• Organized collection of related data– Databases: A list of phone numbers and

names. List of book titles and authors. List of customers and sales figures.

– Not databases: List of book titles and phone numbers. Pile of papers on a desk.

• Major Problems– Data are usually not collected in a very

organized fashion– Too much data – not enough information


The Data Hierarchy


Traditional File Environment


Problems with the File Approach

• data redundancy - the same piece of information could be duplicated in several places

• data inconsistency - the various copies of the data no longer agree

• data isolation - difficulty in accessing data from different applications

• data integrity - data values don’t adhere to integrity constraints


Database : The Modern Approach

• One database – several programs• Data changes only in one place• Examples: MS Access, Oracle, DB2


Advantages of Database Approach

• Reduced data redundancy

• Shared data and information resources

• Improved data integrity

• Easier modification and updating

• Data and program independence

• Better access to data and information

• Standardization of data access

• Framework for program development

• Better overall protection of the data

• Improved strategic use of corporate data


Disadvantages of Database Approach


• Data model– defines the way data are conceptually structured

• Data definition language (DDL)– defines what types of information are in the database and how they

will be structured– functions of the DDL

• provide a means for associating related data• indicate the unique identifiers (or keys) of the records• set up security access and change restrictions

• Data manipulation language (DML)– query the contents of the database, store or update information in

the database, and develop database applications– Structured query language (SQL) - most popular relational database

language, combining both DML and DDL features

• Data Dictionary (metadata)– stores definitions of data elements and data characteristics

DBMS Components


DBMS: Logical versus Physical View• Physical view

– Actual, physical arrangement and location of data

– Described in a schema (describes entire database)

• Logical view– represents data in a format that is

meaningful to a user and to the software programs that process that data

– Can be different for different users as described in subschemas

– Underlying structure may change but subschema (user view) remains the same


Use of Schemas and Subschemas


Database : Centralized database

• all related files in one location

• single mainframe computer

• Users can work on a database as a whole at one location

• files only accessible via the host computer

• disaster recovery can be more easily accomplished at a central location

• vulnerable to a single point of failure

• speed problem


Database : Distributed database

• complete copies (or portions) of a database, in more than one location

• replicated database - complete copies of entire database available at many locations: No single-point-of-failure and increased responsiveness

• partitioned database - a portion of the entire database in each location

• This is planned redundancy (p. 106)


Logical Data Models

• A manager’s ability to use a database is highly dependent on how the database is structured logically and physically.

• In logically structuring a database, businesses need to consider the characteristics of the data and how the data will be accessed.

• Three common data models: hierarchical, network, and relational


Hierarchical Model

• Fast access, large installed base• Best with one-to-many relationship btwn data• Cumbersome, redundant data


Network Model

• Related data ordered in sets• Member/owner of set• Can handle many-to-many relationships• How do we notify customers who ordered

a defective product?


Relational Model

• Tables– Files

• Tuples– Records

• Attributes– Fields

• Structured Query Language (SQL)– Query language

that simplifies access to data

– MS Access makes it even simpler!

SQL Example:SELECT (Customer_Name and Customer_Address) FROM Customer_Table WHERE Credit_Limit > 5000


The Entity Relationship ModelRelationship

Entity

Attribute Primary Key


Important Relational DB Concepts - 1

• Primary Key– Uniquely identifies records in a table– Examples: Employee ID, SSN, Account #– Can be more than one field– Must be defined in every table

• Foreign Key– Identifies a primary key in a different

table– This is how information is related


Important Relational DB Concepts - 2

• Redundant data– The same information exists in more

than one place in the database– Should be avoided– Only foreign keys should be

duplicated– Example


A Relational Database Model

• Identify for each table:– Records– Fields– Field values– Primary keys– Foreign keys


Queries can combine data


Problems with redundant data


Comparing Data Models

Model

Advantages Disadvantages

Hierarchi-cal

Speed and efficiency in search

Access to data is predefined by exclusively hierarchical relationships, predetermined by administrator. Limited search/ query flexibility. Not all data is naturally hierarchical.

Network Many more relationships between data elements can be defined. Greater speed and efficiency than relational database models.

The most complicated model to design, implement, and maintain. More flexibility than hierarchical model, but less than relational model.

Relational

Conceptual simplicity; no predefined relationships among data. High flexibility in ad hoc querying. New data and records can be added easily

Lower processing efficiency and speed. Data redundancy is common, requiring additional maintenance.


Worldwide Dabase Market Share, 2002

IBM36%

Oracle34%

Microsoft18%

NCR3%

Others9%

Total revenue: $6.6 billionSource: Gartner Dataquest


Selecting a DBMS

• Database Size• Number of

concurrent users• Performance• Integration

• Features• The Vendor• Cost


Data Warehousing

• Data extracted from production systems• Historical data for decision making• Concerns

– Data extraction (when, from where, what data)

– Data cleaning– Timeliness– Business mergers– Analysis: Data mining and Online Analytical

Processing (OLAP)


Data Warehouse Elements


Data Mart

• A data warehouse for single division or department

• Easier and cheaper to set up• More detailed data• But might create ’islands’ of

unlinked information


OLAP and Data Mining


Now what?

• Thursday– Lab, Access Tutorial 1– Lecture: Intro to lab and Access– Location: Halsey 101C– Due: IT Problem 1

• Next Tuesday– Catching up on Chapter 1 and 3

Documents

311: Management Information Systems Database Systems Chapter 3