311: Management Information Systems Database Systems Chapter 3

© Jakob Iversen, 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

© Jakob Iversen, The Data Hierarchy

© Jakob Iversen, Traditional File Environment

© Jakob Iversen, 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

© Jakob Iversen, Database : The Modern Approach One database – several programs Data changes only in one place Examples: MS Access, Oracle, DB2

© Jakob Iversen, 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

© Jakob Iversen, Disadvantages of Database Approach

© Jakob Iversen, 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

© Jakob Iversen, 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

© Jakob Iversen, Use of Schemas and Subschemas

© Jakob Iversen, 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

© Jakob Iversen, 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)

© Jakob Iversen, 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

© Jakob Iversen, Hierarchical Model Fast access, large installed base Best with one-to-many relationship btwn data Cumbersome, redundant data

© Jakob Iversen, 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?

© Jakob Iversen, 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

© Jakob Iversen, The Entity Relationship Model Relationship Entity Attribute Primary Key

© Jakob Iversen, 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

© Jakob Iversen, 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

© Jakob Iversen, A Relational Database Model Identify for each table: –Records –Fields –Field values –Primary keys –Foreign keys

© Jakob Iversen, Queries can combine data

© Jakob Iversen, Problems with redundant data

© Jakob Iversen, Comparing Data Models ModelAdvantagesDisadvantages Hierarchi- cal Speed and efficiency in searchAccess 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.

© Jakob Iversen, Worldwide Dabase Market Share, 2002 Total revenue: $6.6 billion Source: Gartner Dataquest

© Jakob Iversen, Selecting a DBMS Database Size Number of concurrent users Performance Integration Features The Vendor Cost

© Jakob Iversen, 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)

© Jakob Iversen, Data Warehouse Elements

© Jakob Iversen, 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

© Jakob Iversen, OLAP and Data Mining

© Jakob Iversen, 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