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Database Modeling and Design
rd Edition
Toby J. Teorey University of Michigan
Lecture Notes
Contents
I. Database Systems and the Life Cycle (Chapter 1)…………………… Introductory concepts; objectives of database management 2 Relational database life cycle 3 Characteristics of a good database design process 7
II. Requirements Analysis (Chapter 3)………………………………….
III. Entity-Relationship (ER) Modeling (Chapters 2-4)……………… 11 Basic ER modeling concepts 11 Schema integration methods 22 Entity-relationship 26 Transformations from ER diagrams to SQL Tables 29
IV. Normalization and normal forms (Chapter 5)……………………… First normal form (1NF) to third normal form (3NF) and BCNF 35 3NF synthesis algorithm (Bernstein) 42 Fourth normal form (4NF) 47
V. Access Methods (Chapter 6)…………………………..……………… Sequential access methods 50 Random access methods 52 Secondary Indexes 58 Denormalization 62 Join strategies 64
VI. Database Distribution Strategies (Chapter 8)………………………. Requirements of a generalized DDBMS: Date’s 12 Rules 68 Distributed database requirements 72 The non-redundant “ best fit” method 74 The redundant “all beneficial sites” method 77
VII. Data Warehousing, OLAP, and Data Mining (Chapter 9)……..... Data warehousing 79 On-line analytical processing (OLAP) 86 Data mining 93
Revised 11/18/98 – modify Section V Revised 11/21/98 – insertions into Section VII Revised 1/14/99 – modify Section VI Revised 2/11/99 – modify Section IV, 4NF (p.47 FD, MVD mix) Revised 6/13/00 – modify Section V (secondary indexes)
I. Database Systems and the Life Cycle
Introductory Concepts
data —a fact, something upon which an inference is based (information or knowledge has value, data has cost)
data item —smallest named unit of data that has meaning in the real world (examples: last name, address, ssn, political party)
data aggregate (or group ) -- a collection of related data items that form a whole concept; a simple group is a fixed collection, e.g. date (month, day, year); a repeating group is a variable length collection, e.g. a set of aliases.
record —group of related data items treated as a unit by an application program (examples: presidents, elections, congresses)
file —collection of records of a single type (examples: president, election)
database —computerized collection of interrelated stored data that serves the needs of multiple users within one or more organizations, i.e. interrelated collections of records of potentially many types. Motivation for databases over files: integration for easy access and update, non-redundancy, multi-access.
database management system (DBMS) -- a generalized software system for manipulating databases. Includes logical view (schema, sub-schema), physical view (access methods, clustering), data manipulation language, data definition language, utilities
- security, recovery, integrity, etc.
database administrator (DBA) -- person or group responsible for the effective use of database technology in an organization or enterprise. Motivation: control over all phases of the lifecycle.
Objectives of Database Management
- Data availability—make an integrated collection of data available to a wide variety of users
- at reasonable cost—performance in query update, eliminate or control data redundancy
- in meaningful format—data definition language, data dictionary
- easy access—query language (4GL, SQL, forms, windows, menus); embedded SQL, etc.; utilities for editing, report generation, sorting
- Data integrity—insure correctness and validity
- checkpoint/restart/recovery
- concurrency control and multi-user updates
- accounting, audit trail (financial, legal)
- Privacy (the goal) and security (the means)
- schema/sub-schema, passwords
- Management control—DBA: lifecycle control, training, maintenance
Characteristics of a Good Database Design Process
- iterative requirements analysis
- interview top-down
- use simple models for data flow and data relationships
- verify model
stepwise refinement and iterative re-design
well-defined design review process to reduce development costs review team -database designers -DBMS software group -end users in the application areas when to review
- after requirements analysis & conceptual design
- after physical design
- after implementation (tuning) meeting format
- short documentation in advance
- formal presentation
- criticize product, not person
- goal is to locate problems, do solutions off line
- time limit is 1-2 hours
II. Requirements Analysis
Purpose - identify the real-world situation in enough detail to be able to define database components. Collect two types of data: natural data (input to the database) and processing data (output from the database).
Natural data requirements (what goes into the database)
- Organizational objectives
- move into to recreational vehicle market
- Information system objectives
- keep track of competitors’ products and prices
- improve quality and timing of data to management regarding production schedule delays, etc.
- keep track of vital resources needed to produce and market a product
- Organizational structure/chart
- Administrative and operational policies
- annual review of employees
- weekly progress reports
- monthly inventory check
- trip expense submission
- Data elements, relationships, constraints, computing environment
Processing requirements (what comes out of the database)
- Existing applications - manual, computerized
- Perceived new applications
quantifies how data is used by applications
should be a subset of data identified in the natural relationships (but may not be due to unforeseen applications)
problem - many future applications may be unknown
Interviews at different levels
Top management - business definition, plan/objectives, future plans
Middle management - functions in operational areas, technical areas, job-titles, job functions
Employees - individual tasks, data needed, data out
Specific end-users of a DBMS - applications and data of interest
Basic rules in interviewing
- Investigate the business first
- Agree with the interviewee on format for documentation (ERD, DFD, etc.)
- Define human tasks and known computer applications
- Develop and verify the flow diagram(s) and ER diagram(s)
- Relate applications to data (this helps your programmers)
Example: order entry clerk
Function: Take customer orders and either fill them or make adjustments. Frequency: daily
Task Def Volume Data Elements
- Create order 2000 A, B, E, H
- Validate order 2000 A, B, G, H, J
- Fill out error form 25 A, C
- Reserve item/price 6000 A, D, H
- Request alternate items 75 A, E, I, K,M
- Enter unit price 5925 A, F, J, N
III. Entity-Relationship (ER) Modeling
Basic ER Modeling Concepts
Entity - a class of real world objects having common characteristics and properties about which we wish to record information.
Relationship - an association among two or more entities
occurrence - instance of a relationship is the collective instances of the related entities
degree - number of entities associated in the relationship (binary, ternary, other n-ary)
connectivity - one-to-one, one-to-many, many-to-many
existence dependency (constraint) - optional/mandatory
Attribute - a characteristic of an entity or relationship
Identifier - uniquely determines an instance of an entity
Identity dependence - when a portion of an identifier is inherited from another entity
Multi-valued - same attribute having many values for one entity
Surrogate - system created and controlled unique key (e.g. Oracle’s “create sequence”)
Constraints
Constraints in ER modeling
role - the function an entity plays in a relationship
existence constraint (existence dependency) - weak entity
exclusion constraint - restricts an entity to be related to only of several other
entities at a given point in time
- mandatory/optional
- specifies lower bound of connectivity of entity instances
- participating in a relationship as 1 or 0
- uniqueness constraint – one-to-one functional dependency among key attributes in a relationship: binary, ternary, or higher n-ary
