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Data & Process Modeling

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Data Modeling

Data modeling – a technique for organizing and documenting a system’s data. Sometimes called database modeling.

Entity relationship diagram (ERD) – a data model utilizing several notations to depict data in terms of the entities and relationships described by that data.

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ERD Sample In an entity-

relationship diagram, entities are labelled with singular nouns and relationships are labelled with verbs. The relationship is interpreted as a simple English sentence.

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Persons: agency, contractor, customer, department, division, employee, instructor, student, supplier.

Places: sales region, building, room, branch office, campus.

Objects: book, machine, part, product, raw material, software license, software package, tool, vehicle model, vehicle.

Events: application, award, cancellation, class, flight, invoice, order, registration, renewal, requisition, reservation, sale, trip.

Concepts: account, block of time, bond, course, fund, qualification, stock.

ERD: Entity

Entity – a class of persons, places, objects, events, or concepts about which we need to capture and store data.

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ERD: Relationships

Relationship – a natural business association that exists between one or more entities.

The relationship may represent an event that links the entities or merely a logical affinity that exists between the entities.

Student CurriculumIs being studied by is enrolled in

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ERD: Relationships

Types of Relationships Three types of relationships can exist

between entities One-to-one relationship (1:1) One-to-many relationship (1:M) Many-to-many relationship (M:N)

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one-to-one Relationship

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one-to-many Relationship

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many-to-many Relationship

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ERD: Cardinality

Cardinality – the minimum and maximum number of occurrences of one entity that may be related to a single occurrence of the other entity.

Because all relationships are bidirectional, cardinality must be defined in both directions for every relationship.

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bidirectional

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Cardinality Notations

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JRP and Interview Questions for Data Modeling

Purpose Candidate Questions (see Table 8-4 in text for a more complete list)

Discover system entities What are the subjects of the business?

Discover entity keys What unique characteristic (or characteristics) distinguishes an instance of each subject from other instances of the same subject?

Discover entity subsetting criteria Are there any characteristics of a subject that divide all instances of the subject into useful subsets?

Discover attributes and domains What characteristics describe each subject?

Discover security and control needs Are there any restrictions on who can see or use the data?

Discover data timing needs How often does the data change?

Discover generalization hierarchies Are all instances of each subject the same?

Discover relationships? What events occur that imply associations between subjects?

Discover cardinalities Is each business activity or event handled the same way, or are there special circumstances?

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What is a Good Data Model?

A good data model is simple. Data attributes that describe any given entity

should describe only that entity. Each attribute of an entity instance can have only

one value.

A good data model is essentially nonredundant. Each data attribute, other than foreign keys,

describes at most one entity. Look for the same attribute recorded more than

once under different names.

A good data model should be flexible and adaptable to future needs.

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Data Analysis & Normalization

Data analysis – a technique used to improve a data model for implementation as a database.

Goal is a simple, nonredundant, flexible, and adaptable database.

Normalization – a data analysis technique that organizes data into groups to form nonredundant, stable, flexible, and adaptive entities.

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More Issues on ERD

Degree Binary & ternary relationship

Foreign Key Primary Key (Identification Entity) Normalization

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Process Modeling and DFDs

Process modeling – a technique used to organize and document a system’s processes.

Flow of data through processes Logic Policies Procedures

Data flow diagram (DFD) – a process model used to depict the flow of data through a system and the work or processing performed by the system. Synonyms are bubble chart, transformation graph, and process model.

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Data Flow Diagrams

A data flow diagram (DFD) shows how data moves through an information system but does not show program logic or processing steps

A set of DFDs provides a logical model that shows what the system does, not how it does it

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Differences Between DFDs and Flowcharts

Processes on DFDs can operate in parallel (at-the-same-time) Processes on flowcharts execute one at a time

DFDs show the flow of data through a system Flowcharts show the flow of control (sequence

and transfer of control)

Processes on a DFD can have dramatically different timing (daily, weekly, on demand) Processes on flowcharts are part of a single

program with consistent timing

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Data Flow Diagrams

DFD Symbols DFDs use four basic symbols that

represent processes, data flows, data stores, and entities

Gane and Sarson symbol set Yourdon symbol set

Symbols are referenced by using all capital letters for the symbol name

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DFD Symbols

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Process ConceptsProcess – work performed by a system in response to incoming data flows or conditions. A synonym is transform.

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Data flow – data that is input to or output from a process. A data flow is data in motion A data flow may also be

used to represent the creation, reading, deletion, or updating of data in a file or database (called a data store).

Data Flows

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External AgentsExternal agent – an outside person, organization unit, system, or organization that interacts with a system. Also called an external entity. External agents define the “boundary” or

scope of a system being modeled. As scope changes, external agents can become

processes, and vice versa. Almost always one of the following:

Office, department, division. An external organization or agency. Another business or another information system. One of your system’s end-users or managers

Named with descriptive, singular noun

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Data StoresData store – stored data intended for later use. Synonyms are file and database. Frequently implemented as a file or database. A data store is “data at rest” compared to a data

flow that is “data in motion.” Almost always one of the following:

Persons (or groups of persons) Places Objects Events (about which data is captured) Concepts (about which data is important)

Data stores depicted on a DFD store all instances of data entities (depicted on an ERD)

Named with plural noun

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Process DecompositionDecomposition – the act of breaking a system into sub-components. Each level of abstraction reveals more or less detail.

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Decomposition Diagrams

Decomposition diagram – a tool used to depict the decomposition of a system. Also called hierarchy chart.

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Data Flow Diagrams

Context Diagrams Top-level view of an information system

that shows the system’s boundaries and scope

Do not show any data stores in a context diagram because data stores are internal to the system

Begin by reviewing the system requirements to identify all external data sources and destinations

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Data Flow Diagrams

Context Diagrams Record the name of the entities and the

name and content of the data flows, and the direction of the data flows

What makes one system more complex than another is the number of components, the number of levels, and the degree of interaction among its processes, entities, data stores, and data flows

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Data Flow Diagrams

Conventions for DFDs1. Each context diagram must fit on one

page2. The process name in the context

diagram should be the name of the information system

3. Use unique names within each set of symbols

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Data Flow Diagrams

Conventions for DFDs4. Do not cross lines5. Use a unique reference number for

each process symbol

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Data Flow Diagrams

Strategies for Developing DFDs A set of DFDs is a graphical, top-down

model With a bottom-up strategy, you first

identify all functional primitives, data stores, entities, and data flows

The main objective is to ensure that your model is accurate and easy to understand

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Data Flow Diagrams

Strategies for Developing DFDs General rule of thumb is that a diagram

should have no more than nine process symbols

To construct a logical model of a complex system, you might use a combination of top-down and bottom-up strategies

The best approach depends on the information system you are modeling

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Illegal Process

Spontaneous generation Process with no inputs

Black Hole Process with no outputs

Gray Hole The inputs is insufficient to generate the

output

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Data Flow Packet Concept

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Illegal Data Flows

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More Issues on Process Model

Natural English Structured English Decision Table Use Cases

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Test Yourself

1. Data flow diagrams show what a system does, not how it does it (T/F).

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Test Yourself

1. Data flow diagrams show what a system does, not how it does it (T/F).

True

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Test Yourself

2. The following symbols are from the _____________ set. Name them:

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Test Yourself

2. The following symbols are from the Gane and Sarson set. Name them:

Data Store

Process

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Test Yourself

3. Select the correct example below.

CustomerCustomer CustomerCustomer

A) B)

Apply Payment

AccountsReceivable

PaymentPayment

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Test Yourself

3. Select the correct example below.A) is correct. An external entity can’t be directly connected to a data store.

A) B)

CustomerCustomer CustomerCustomer

Apply Payment

AccountsReceivable

PaymentPayment

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Test Yourself

4. Match the terms in the left column to the proper definitions in the right column.1. Black Hole

2. Spontaneous Generation Process

3. Gray Hole

a. A process with at least 1 input and output, but the input is insufficient to generate the shownoutput.

b. A process that has no output

c. Used to describe an unexplainedgeneration of data or information.

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Test Yourself

4. Match the terms in the left column to the proper definitions in the right column.1. Black Hole

2. Spontaneous Generation Process

3. Gray Hole

a. A process with at least 1 input and output, but the input is insufficient to generate the shownoutput.

b. A process that has no output

c. Used to describe an unexplainedgeneration of data or information.