710 Foundations of Information Systems Database Systems Chapter
710: Foundations of Information Systems Database Systems Chapter 3
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 2 © Jakob Iversen, 2002
The Data Hierarchy 3 © Jakob Iversen, 2002
Traditional File Environment 4 © Jakob Iversen, 2002
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 5 © Jakob Iversen, 2002
Database : The Modern Approach • One database – several programs • Data changes only in one place • Examples: MS Access, Oracle, DB 2 6 © Jakob Iversen, 2002
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 7 © Jakob Iversen, 2002
Disadvantages of Database Approach 8 © Jakob Iversen, 2002
DBMS Components • 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 © Jakob Iversen, 2002 9
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 10 © Jakob Iversen, 2002
Use of Schemas and Subschemas 11 © Jakob Iversen, 2002
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 12 © Jakob Iversen, 2002
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 singlepoint-of-failure and increased responsiveness • partitioned database - a portion of the entire database in each location • This is planned redundancy (p. 106) 13 © Jakob Iversen, 2002
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 14 © Jakob Iversen, 2002
Hierarchical Model • Fast access, large installed base • Best with one-to-many relationship btwn data • Cumbersome, redundant data 15 © Jakob Iversen, 2002
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, 2002 16
Relational Model • Data organized in tables that are related through stored values • 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 17 © Jakob Iversen, 2002
The Entity Relationship Model Relationship Entity Attribute © Jakob Iversen, 2002 Primary Key 18
Relational Database Example 19 © Jakob Iversen, 2002
A Relational Database Model • Identify for each table: – – – Records Field values Primary keys Foreign keys 20 © Jakob Iversen, 2002
Queries can combine data 21 © Jakob Iversen, 2002
Problems with redundant data 22 © Jakob Iversen, 2002
Comparing Data Models Model Advantages Disadvantages Hierarchi- Speed and efficiency in search cal 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. Relational Conceptual simplicity; no predefined relationships among data. High flexibility in ad hoc querying. New data and records can be added easily The most complicated model to design, implement, and maintain. More flexibility than hierarchical model, but less than relational model. Lower processing efficiency and speed. Data redundancy is common, requiring additional maintenance. 23 © Jakob Iversen, 2002
Worldwide Dabase Market Share, 2002 Total revenue: $6. 6 billion Source: Gartner Dataquest 24 © Jakob Iversen, 2002
Selecting a DBMS • Database Size • Number of concurrent users • Performance • Integration • Features • The Vendor • Cost 25 © Jakob Iversen, 2002
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) 26 © Jakob Iversen, 2002
Data Warehouse Elements 27 © Jakob Iversen, 2002
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 28 © Jakob Iversen, 2002
OLAP and Data Mining 29 © Jakob Iversen, 2002
Next Week • Lecture – Chapter 4: Telecommunication, Internet, Extranet • Presentations from Team 5 and 1 • Assignment 1 due for Team 3 and 4 30 © Jakob Iversen, 2002
- Slides: 30