Database Design Conceptual Data Modeling

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Database DesignConceptual Data Modeling
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Objectives

• Purpose of a design methodology.
• Database design has three main phases
  conceptual, logical, and physical design.
• How to decompose the scope of the design into
  specific users views of the enterprise.
• How to use ER modeling to build a local
  conceptual data model based on information given
  in a view of the enterprise.

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Objectives

• How to validate resultant conceptual model to
  ensure it is a true and accurate representation
  of a view of the enterprise.
• How to document process of conceptual database
  design.
• End-users play an integral role throughout
  process of conceptual database design.

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Acknowledgments

• These slides have been adapted from Thomas
  Connolly and Carolyn Begg

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Design Methodology

• Structured approach that uses procedures,
  techniques, tools, and documentation aids to
  support and facilitate the process of design.
• Database design methodology has 3 main phases
• Conceptual database design
• Logical database design
• Physical database design.

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Preliminary Phase Database Initial Study

• Purposes
• Analyze company situation
• Operating environment
• Organizational structure
• Define problems and constraints
• Define objectives
• Define scope and boundaries

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Business Rules

• What are business rules, what is their source,
  and why are they crucial?
• Business rules are precise statements, derived
  from a detailed description of the organization's
  operations, that define one or more of the
  following modeling components
• entities - in the E-R model corresponds to a
  table
• relationships are associations between
  entities
• attributes are characteristics of entities
• connectivities are used to describe the
  relationship classification
• cardinalities express the specific number of
  entity occurrences associated with one occurrence
  of the related entity
• constraints limitations on the type of data
  accepted

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Business Rules

• Examples of business rules are
• An invoice contains one or more invoice lines,
  but each invoice line is associated with a single
  invoice.
• A store employs many employees, but each employee
  is employed by only one store.
• A college has many departments, but each
  department belongs to a single college. (This
  business rule reflects a university that has
  multiple colleges such as Business, Liberal Arts,
  Education, Engineering, etc.)
• A driver may be assigned to drive many different
  vehicles, and each vehicle can be driven by many
  drivers. (Note Keep in mind that this business
  rule reflects the assignment of drivers during
  some period of time.)
• A client may sign many contracts, but each
  contract is signed by only one client.

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Changing Data into Information

• Data
• Raw facts stored in databases
• Need additional processing to become useful
• Information
• Required by decision maker
• Data processed and presented in a meaningful form
• Transformation

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The Information System

• Database
• Carefully designed and constructed repository of
  facts
• Part of an information system
• Information System
• Provides data collection, storage, and retrieval
• Facilitates data transformation
• Components include
• People
• Hardware
• Software

• Database(s)
• Application programs
• Procedures

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The Information System

• System Analysis
• Establishes need and extent of an information
  system
• System development
• Process of creating information system
• Database development
• Process of database design and implementation
• Creation of database models
• Implementation
• Creating storage structure
• Loading data into database
• Providing for data management

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Database Lifecycle (DBLC)
Figure 6.3
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Initial Study Activities
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Use case diagram
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Conceptual/Logical Database Design

• Conceptual database design
• Process of constructing a model of information
  used in an enterprise, independent of all
  physical considerations.
• Logical database design
• Process of constructing a model of information
  used in an enterprise based on a specific data
  model (e.g. relational), but independent of a
  particular DBMS and other physical
  considerations.

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Physical Database Design

• Process of producing a description of the
  implementation of the database on secondary
  storage it describes the base relations, file
  organizations, and indexes design used to achieve
  efficient access to the data, and any associated
  integrity constraints and security measures.

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Critical Success Factors in Database Design

• Work interactively with users as much as
  possible.
• Follow a structured methodology throughout the
  data modeling process.
• Employ a data-driven approach.
• Incorporate structural and integrity
  considerations into the data models.
• Combine conceptualization, normalization, and
  transaction validation techniques into the data
  modeling methodology.

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Critical Success Factors in Database Design

• Use diagrams to represent as much of the data
  models as possible.
• Use a Database Design Language (DBDL) to
  represent additional data semantics.
• Build a data dictionary to supplement the data
  model diagrams.
• Be willing to repeat steps.

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Methodology Overview - Conceptual Database Design

• Step 1 Build local conceptual data model for
  each user view
• Step 1.1 Identify entity types
• Step 1.2 Identify relationship types
• Step 1.3 Identify and associate attributes with
  entity or relationship types
• Step 1.4 Determine attribute domains
• Step 1.5 Determine unique identifier (will
  become a key) attributes
• Step 1.6 Consider use of enhanced modeling
  concepts (optional step)
• Step 1.7 Check model for redundancy
• Step 1.8 Validate local conceptual model
  against user transactions
• Step 1.9 Review local conceptual data model
  with user

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Class diagram
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Methodology Overview - Logical Database Design
for Relational Model

• Step 2 Build and validate local logical data
  model for each view
• Step 2.1 Remove features not compatible with the
  relational model (optional step)
• Step 2.2 Derive relations for local logical data
  model
• Step 2.3 Validate relations using normalization
• Step 2.4 Validate relations against user
  transactions
• Step 2.5 Define integrity constraints
• Step 2.6 Review local logical data model with
  user

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Methodology Overview - Logical Database Design
for Relational Model

• Step 3 Build and validate global logical data
  model
• Step 3.1 Merge local logical data models into
  global model
• Step 3.2 Validate global logical data model
  against the conceptual data
  model
• Step 3.3 Check for future growth
• Step 3.4 Review global logical data model with
  users

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Methodology Overview - Physical Database Design
for Relational Databases

• Step 4 Translate global logical data model for
  target DBMS
• Step 4.1 Design base relations
• Step 4.2 Design representation of derived data
• Step 4.3 Design enterprise constraints
• Step 5 Design physical representation
• Step 5.1 Analyze transactions
• Step 5.2 Choose file organization
• Step 5.3 Choose indexes
• Step 5.4 Estimate disk space requirements

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Methodology Overview - Physical Database Design
for Relational Databases

• Step 6 Design user views
• Step 7 Design security mechanisms
• Step 8 Consider the introduction of controlled
  redundancy
• Step 9 Monitor and tune the operational system

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Step 1 Build Local Conceptual Data Model for Each
View

• To build a local conceptual data model of an
  enterprise for each specific view.
• Step 1.1 Identify entity types
• To identify the main entity types that are
  required by the view.
• Step 1.2 Identify relationship types
• To identify the important relationships that
  exist between the entity types that have been
  identified.

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Step 1 Build Local Conceptual Data Model for Each
View

• Step 1.3 Identify and associate attributes with
  entity or relationship types
• To identify and associate attributes with the
  appropriate entity or relationship types and
  document the details of each attribute.
• Step 1.4 Determine attribute domains
• To determine domains for the attributes in the
  local conceptual model and document the details
  of each domain.

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Step 1 Build Local Conceptual Data Model for Each
View

• Step 1.5 Determine candidate and unique
  identifier attributes
• To identify the candidate key(s) for each entity
  and if there is more than one candidate key, to
  choose one to be the unique identifier.
• Step 1.6 Consider use of enhanced modeling
  concepts (optional step)
• To consider the use of enhanced modeling
  concepts, such as specialization /
  generalization, aggregation, and composition.

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Step 1 Build Local Conceptual Data Model for Each
View

• Step 1.7 Check model for redundancy
• To check for the presence of any redundancy in
  the model.
• Step 1.8 Validate local conceptual model against
  user transactions
• To ensure that the local conceptual model
  supports the transactions required by the view.
• Step1.9 Review local conceptual data model with
  user
• To review the local conceptual data model with
  the user to ensure that the model is a true
  representation of the users view of the
  enterprise.

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Extract from Data Dictionary for Staff View of
DreamHome Showing Description of Entities
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First-cut ER diagram for Staff View of DreamHome
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Extract from Data Dictionary for Staff View of
DreamHome Showing Description of Relationships
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Extract from Data Dictionary for Staff View of
DreamHome Showing Description of Attributes
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ER Diagram for Staff View of DreamHome with
Unique Identifiers Added
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Revised ER Diagram for Staff View of DreamHome
with Specialization / Generalization
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Example of a Non-Redundant Relationship FatherOf
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Using Pathways to Check that the Conceptual Model
Supports the User Transactions
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Data analysis and Requirements

• Focus on
• Information needs
• Information users
• Information sources
• Information constitution
• Data sources
• Developing and gathering end-user data views
• Direct observation of current system
• Interfacing with systems design group
• Business rules

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E-R Modeling is Iterative