Using Data Flow Diagrams

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Using Data Flow Diagrams

SOURCE: Systems Analysis and Design, 9e

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Telkom University



Comprehend the importance of using logical and physical data flow diagrams

(DFDs) to graphically depict movement for humans and systems in an

organization.



Create, use, and explode logical DFDs to capture and analyze the current system

through parent and child levels.



Develop and explode logical DFDs that illustrate the proposed system.



Produce physical DFDs based on logical DFDs you have developed.



Understand and apply the concept of partitioning of physical DFDs.

(4)

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

Graphically characterize data processes and flows in a business

system



Depict:



System inputs



Processes



Outputs

Data Flow Diagrams

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

Data flow diagram symbols



Data flow diagram levels



Creating data flow diagrams



Physical and logical data flow diagrams



Partitioning



Communicating using data flow diagrams

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

Freedom from committing to the technical implementation too early



Understanding of the interrelatedness of systems and subsystems



Communicating current system knowledge to users



Analysis of the proposed system

Advantages of the Data Flow Approach

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

A double square for an external entity



An arrow for movement of data from one point to another



A rectangle with rounded corners for the occurrence of a

transforming process



An open-ended rectangle for a data store

(8)

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The Four Basic Symbols Used in Data Flow Diagrams, Their Meanings, and Examples

(Figure 7.1)

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

Represent another department, a business, a person, or a machine



A source or destination of data, outside the boundaries of the system



Should be named with a noun

External Entities



Shows movement of data from one point to another



Described with a noun



Arrowhead indicates the flow direction



Represents data about a person, place, or thing

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_

_{Denotes a change in or transformation of data}



Represents work being performed in the system



Naming convention:



Assign the name of the whole system when naming a high-level process



To name a major subsystem attach the word subsystem to the name



Use the form verb-adjective-noun for detailed processes

Process



A depository for data that allows examination, addition, and retrieval of data



Named with a noun, describing the data



Data stores are usually given a unique reference number, such as D1, D2, D3



Represents a:



Database



Computerized file

Data Store

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Steps in Developing Data Flow Diagrams

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

The highest level in a data flow diagram



Contains only one process, representing the entire system



The process is given the number 0



All external entities, as well as major data flows are shown

Creating the Context Diagram



The data flow diagram must have one process



Must not be any freestanding objects



A process must have both an input and output data flow



A data store must be connected to at least one process



External entities should not be connected to one another

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Representation of

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

IPO:

Input →Process → Output

Data→ Process → Information



Data flow diagram

is a graphical technique that depicts information

flow and the transforms that are applied as data moves from input

to output.



DFDs use four basic symbols that represent processes, data flows,

data stores, and entities



Gane and Sarson symbol set



Yourdon symbol set

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Symbols for DFD

Data store

Source or destination of data

Process:

Action on data

Data Store:

Storage of data

Data Flow:

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

A context diagram is a top level (also known as Level 0) data flow diagram.



It only contains one process node (process 0) that generalizes the function of the

entire system in relationship to external entities.

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

Create a graphical model of the information system based

on your fact-finding results



_{Performing three main tasks}



Step 1: Draw a context diagram



Step 2: Draw a DFD level 1



Step 3: Draw the lower-level diagrams

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

Drawing Guidelines

1. Draw the context diagram so it fits on one page

2. Use the name of the information system as the process name in the

context diagram

3. Use unique names within each set of symbols

4. Do not cross lines

5. Provide a unique name and reference number for each process

6. Obtain user input and feedback

(19)

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

External entity represents the sources and destination of data

created by the system.



External entity represents the immediate interface of the system

with the external world.



When an external source of data is also a destination for data, a

loop or occurrence number may be used.



In case the destination or use of data created by the process are not

known, the flow simply points outside the system. Similarly, data

flows may originate from “

nowhere

”.

(20)

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

Each processes box in a DFD describes an action on data.



The Identifier

. A number indicating the sequence of the process.



The Action

. A verb specifying the action on which it is performed on

the data.



The Actor or Place

. A noun indicating who performs the action or

where it is performed.

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

Data flow arrows link all the process boxes and data stores in DFDs.



Data flows should be labeled, except in case the data flows into and

out of simple files.



DFDs show only the flow of data, not materials.



A DFD depicts information flow without explicit representation of

procedural logic (e.g., conditions or loops).

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

Data stores can be manual files or computer files. The type of file is

not indicated.



Only in case the data store is altered the flow is not indicated. A

simple access is not indicated.



A data store is never the direct recipient of unprocessed data from

external sources or from other data stores nor is data from a data

store ever directly delivered to an external sources. There must be a

process step in between.

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Examples of Data Stores

Read

Write

Read/

Write

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DFD Not Allowed Flows

If part of our system

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

Only one direction of flow between processes

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

Joins & forks allowed only if exactly the same data

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

Cannot go directly back to the process it leaves

Data Flows

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

Data which moves together should be shown in

a single data flow

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DFD Rules

Incorrect

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(34)

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Level 1 DFD

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Level 2 DFD

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

The conservation of input and output flows

through different levels

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

A balanced DFD Fragment



source: www.yourdon.com

Example

E1

E2

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Example of Context Diagram

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Example of

DFD Level 1

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Top Level Diagram (Level 1)

1. + Completion Date

Clients

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Level 2 Diagram

5.1 Set Client

Contact

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

The explosion of the context diagram



May include up to nine processes



Each process is numbered



Major data stores and all external entities are included

Drawing Diagram 0



Start with the data flow from an entity on the input side



Work backward from an output data flow



Examine the data flow to or from a data store

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

Data flow diagrams are built in layers



The top level is the context level



Each process may explode to a lower level



The lower level diagram number is the same as the parent process

number



Processes that do not create a child diagram are called primitive

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

Each process on diagram 0 may be exploded to create a child diagram



A child diagram cannot produce output or receive input that the parent

process does not also produce or receive



The child process is given the same number as the parent process



Process 3 would explode to Diagram 3

Creating Child Diagrams



Entities are usually not shown on the child diagrams below Diagram 0



If the parent process has data flow connecting to a data store, the child

diagram may include the data store as well

(49)

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

Forgetting to include a data flow or pointing an arrow in the wrong

direction



Connecting data stores and external entities directly to each other



Incorrectly labeling processes or data flow

Data Flow Diagrams Error Summary



Including more than nine processes on a data flow diagram



Omitting data flow

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

Forgetting to include a data flow or pointing an arrow in the wrong

direction

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Connecting data stores and external entities directly to each other

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

Logical



Focuses on the business and how the business operates



Not concerned with how the system will be constructed



Describes the business events that take place and the data required and

produced by each event

Logical and Physical Data Flow Diagrams



Physical



Shows how the system will be implemented

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The Progression of Models from Logical to Physical

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

Better communication with users



More stable systems



Better understanding of the business by analysts



Flexibility and maintenance



Elimination of redundancy and easier creation of the physical model

Developing Logical Data Flow Diagrams



Clarifying which processes are performed by humans and which are automated



Describing processes in more detail



Sequencing processes that have to be done in a particular order



Identifying temporary data stores

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(61)

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

The acronym CRUD is often used for



Create



Read



Update



Delete



These are the activities that must be present in a system for each

master file



A CRUD matrix is a tool to represent where each of these processes

occurs in a system

(62)

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

An input flow from an external entity is sometimes called a trigger because it

starts the activities of a process



Events cause the system to do something and act as a trigger to the system



An approach to creating physical data flow diagrams is to create a data flow

diagram fragment for each unique system event

Event Modeling and Data Flow Diagrams



An event table is used to create a data flow diagram by analyzing each event

and the data used and produced by the event



Every row in an event table represents a data flow diagram fragment and is

(64)

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An Event Response Table for an Internet Storefront

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

Each use case defines one activity and its trigger, input, and output



Allows the analyst to work with users to understand the nature of

the processes and activities and then create a single data flow

diagram fragment

Use Cases and Data Flow Diagrams

Kendall & Kendall



Partitioning is the process of examining a data flow diagram and

determining how it should be divided into collections of manual

procedures and computer programs



A dashed line is drawn around a process or group of processes that

should be placed in a single computer program

(67)

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

Different user groups



Timing



Similar tasks



Efficiency



Consistency of data



Security

Reasons for Partitioning



Improves the way humans use the site



Improves speed of processing

(68)

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

Use unexploded data flow diagrams early when ascertaining

information requirements



Meaningful labels for all data components

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

Data flow diagrams



Structured analysis and

design tools that allow the

analyst to comprehend the

system and subsystems

visually as a set of

interrelated data flows



DFD symbols



Rounded rectangle



Double square



An arrow



Open-ended rectangle

Summary



Creating the logical DFD



Context-level data flow diagram



Level 0 logical data flow diagram



Child diagrams



Creating the physical DFD



Create from the logical data flow diagram



Partitioned to facilitate programming



Partitioning data flow diagrams



Whether processes are performed by different user groups



Processes execute at the same time

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(71)

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

Understand database concepts.



Use normalization to efficiently store data in a database.



Use databases for presenting data.



Understand the concept of data warehouses.



Comprehend the usefulness of publishing databases to the Web.



Understand the relationship of business intelligence to data warehouses, big

data, business analytics and text analytics in helping systems and people make

decisions.

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

Databases



Normalization



Key design



Using the database



Data warehouses



Data mining



Business intelligence

(74)

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

The data must be available when the user wants to use them



The data must be accurate and consistent



Efficient storage of data as well as efficient updating and retrieval



It is necessary that information retrieval be purposeful

Data Storage



There are two approaches to the storage of data in a

computer-based system:



Store the data in individual files, each unique to a particular application



Store data in a database

(75)

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

Effectiveness objectives of the database:



Ensuring that data can be shared among users for a variety of applications



Maintaining data that are both accurate and consistent



Ensuring data required for current and future applications will be readily

available



Allowing the database to evolve as the needs of the users grow



Allowing users to construct their personal view of the data without concern for

the way the data are physically stored

(76)

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

Reality



The real world



Data



Collected about people, places, or events in reality and eventually stored in a

file or database



Metadata



Information that describes data

(77)

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

Any object or event about which someone chooses to collect data



May be a person, place, or thing



May be an event or unit of time

Entities



An entity subtype is a special one-to-one relationship used to represent

additional attributes, which may not be present on every record of the first

entity



This eliminates null fields stored on database tables



For example, students who have internships: the STUDENT MASTER should

not have to contain information about internships for each student

(79)

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

Relationships



One-to-one



One-to-many



Many-to-many



A single vertical line represents one



A crow’s foot represents many

(80)

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Entity-Relationship Diagrams Associations

(Figure 13.2, Part 1)

(81)

one-to-Telkom University

Entity-Relationship Diagrams Associations

(82)

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Entity-Relationship Diagrams Associations

(Figure 13.2, Part 3)

(83)

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(84)

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The Entity-Relationship Diagram for Patient Treatment (Figure 13.4)

(85)

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

Attributes represent some characteristic of an entity



Records are a collection of data items that have something in

common with the entity described



Keys are data items in a record used to identify the record

Attributes, Records, and Keys



Key types are:



Primary key

—

unique attribute for the record



Candidate key

—

an attribute or collection of attributes, that can serve as a primary key

(86)

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

Data about the data in the file or database



Describe the name given and the length assigned each data item



Also describe the length and composition of each of the records

(87)

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Metadata (Figure 13.7)

Metadata

_{includes a}

(88)

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

A file contains groups of records used to provide information for

operations, planning, management, and decision making



Files can be used for storing data for an indefinite period of time, or

they can be used to store data temporarily for a specific purpose

Files



Master file



Table file



Transaction file



Report file

File Types

Kendall & Kendall

(89)

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

Master files:



Contain records for a group of entities



Contain all information about a data entity



Table files:



Contains data used to calculate more data or performance measures



Usually read-only by a program

(90)

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

Transaction records:



Used to enter changes that update the master file and produce reports



Report files:



Used when it is necessary to print a report when no printer is available



Useful because users can take files to other computer systems and output to

specialty devices

(91)

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

A database is intended to be shared by many users



There are three structures for storing database files:



Relational database structures



Hierarchical database structures



Network database structures

(92)

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Database Design (Figure 13.8)

Database design

includes

synthesizing

user reports,

user views, and

logical and

(93)

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Relational Data Structure (Figure 13.9)

In a relational

data structure,

data are

(94)

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

Normalization is the transformation of complex user views and data

stores to a set of smaller, stable, and easily maintainable data

structures



The main objective of the normalization process is to simplify all the

complex data items that are often found in user views

(95)

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Normalization of a Relation Is Accomplished in Three Major Steps

(96)

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

Shows data associations of data elements



Each entity is enclosed in an ellipse



Arrows are used to show the relationships

(97)

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Drawing Data Model (Figure 13.13)

Drawing data model

diagrams for data

associations

(98)

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

Remove repeating groups



The primary key with repeating group attributes are moved into a

new table



When a relation contains no repeating groups, it is in first normal

form

(99)

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The Original Unnormalized Relation (Figure 13.16)

The original

unnormalized relation

SALES-REPORT is

separated into two

relations,

(100)

SALESPERSON-Telkom University



Remove any partially dependent attributes and place them in

another relation



A partial dependency is when the data are dependent on a part of a

primary key



A relation is created for the data that are only dependent on part of

the key and another for data that are dependent on both parts

(101)

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Second Normal Form (Figure 13.18 )

The relation

SALESPERSON-CUSTOMER is separated into a

relation called

(102)

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

Must be in 2NF



Remove any transitive dependencies



A transitive dependency is when nonkey attributes are dependent

not only on the primary key, but also on a nonkey attribute

(103)

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Third Normal Form

(Figure 13.20)

The relation

CUSTOMER-WAREHOUSE is

separated into two

relations called

CUSTOMER

(1NF) and

(104)

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Kendall & Kendall

Copyright

(105)

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

When the relationship is one-to-many, the primary key of the file at

the one end of the relationship should be contained as a foreign key

on the file at the many end of the relationship



A many-to-many relationship should be divided into two

one-to-many relationships with an associative entity in the middle

(106)

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

Each separate data entity should create a master database table



A specific data field should exist on one master table



Each master table or database relation should have programs to

create, read, update, and delete the records

Kendall & Kendall

(107)

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

Entity integrity



Referential integrity



Domain integrity

Integrity Constraints



The primary key cannot have a null value



If the primary key is a composite key, none of the fields in the key

can contain a null value

(108)

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

Referential integrity governs the nature of records in a one-to-many relationship



Referential integrity means that all foreign keys in the many table (the child table) must

have a matching record in the parent table

Referential Integrity

Referential integrity implications:



You cannot add a record in the child (many) table without a matching record in the parent table



You cannot change a primary key that has matching child table records



You cannot delete a record that has child records



Implemented in two ways:



A restricted database updates or deletes a key only if there are no matching child records

(109)

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

Domain integrity rules are used to validate the data



Domain integrity has two forms:



Check constraints, which are defined at the table level



Rules, which are defined as separate objects and can be used within a number

of fields

Domain Integrity



Data redundancy



Insert anomaly

(110)

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

When the same data is stored in more than one place in the

database



Solved by creating tables that are in third normal form

(111)

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

Occurs when the entire primary key is not known and the database

cannot insert a new record, which would violate entity integrity



Can be avoided by using a sequence number for the primary key

Insert Anomaly



Happens when a record is deleted that results in the loss of other

related data

Deletion Anomaly



When a change to one attribute value causes the database to either

contain inconsistent data or causes multiple records to need

(112)

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

Choose a relation from the database



Join two relations together



Project columns from the relation



Select rows from the relation



Derive new attributes



Index or sort rows



Calculate totals and performance measures



Present data

(113)

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

Data warehouses are used to organize information for quick and effective queries



In the data warehouse, data are organized around major subjects



Data in the warehouse are stored as summarized rather than detailed raw data



Data in the data warehouse cover a much longer time frame than in a traditional

transaction-oriented database



Data warehouses are organized for fast queries

Data Warehouses and Database Differences



Data warehouses are usually optimized for answering complex queries, known as OLAP



Data warehouses allow for easy access via data-mining software

(114)

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

Online analytic processing (OLAP) is meant to answer decision

makers’ complex questions by defining a multidimensional database

Online Analytic Processing



Software



Statistical analysis



Decision trees



Neural networks



Intelligent agents



Fuzzy logic



Data visualization

Data-Mining Decision Aids



Associations

—

patterns that occur

together



Sequences

—

patterns of actions that

take place over a period of time



Clustering

—

patterns that develop

among groups of people



Trends

—

the patterns that are

noticed over a period of time

(115)

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Data Mining (Figure 13.27)

Data mining collects

personal information

about customers in

an effort to be more

specific in

(116)

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

Costs may be too high to justify



Has to be coordinated



Ethical aspects

(117)

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

Business intelligence is a decision support system (DSS) for organizational decision makers



It is composed of features that gather and



store data



It uses knowledge management approaches combined with analysis



This becomes input to decision makers’ decision

-making processes

Business Intelligence (BI)



Business intelligence is built around processing large volumes of data



Big data is when data sets become too large or too complex to be handled with

traditional tools or within traditional databases or data warehouses

(118)

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

Five prominent methods are used for analyzing business intelligence



Slice-and-dice drilldown



Ad hoc queries



Real-time analysis



Forecasting



Scenarios

(119)

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

Text analytics is a way to structure the unstructured



Turning qualitative material into quantitative material



The broader view is to tap into qualitative unstructured data that

can be of use to decision makers who must recommend courses of

action to their organizations that are backed by data

(120)

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

Sources of big data for text analytics include unstructured, qualitative, or “soft,”