1. DECISION SUPPORT SYSTEM

Understand the decision support system
 Appreciate the framework for DSS Development
 Get a grip of various models
 Evolve the individual and organizational models

2. Decision support systems: Definitions
Decision support systems are a class of
computer-based information systems
including knowledge based systems that
support decision making activities.

3. Decision support systems
There are many approaches to decision-making and
because of the wide range of domains in which
decisions are made, the concept of decision support
system (DSS) is very broad. A DSS can take many
different forms. DSS is a computerized system for
helping to make decisions. A decision is a choice
between alternatives based on estimates of the
values of those alternatives. Supporting a decision
means helping people working alone or in a group
gather intelligence, generate alternatives and make
choices.

4. Decision Making
There are often confusion between
terms MIS and information system.
Information systems include systems
that are not intended for decision
making. MIS is referred to, in a
restrictive sense, as
information technology management

5. Framework for Developing Decision
Support System
A DSS is a system that aids the process
of decision making, but that cannot
bring out explicit decision suggestions
or solutions. DSS can bring out such
decision suggestions or solutions.

6. Framework for Developing Decision
Support System
DSS allows the decision maker (or its
advisor) to modify, complete, or refine
the decision suggestions provided by
the system, before sending them back
to the system for validation. The system
again improves, completes, and refines
the suggestions of the decision maker
and sends them back to for validation.

7. Framework for Developing Decision
Support System
A model-driven DSS emphasizes
access to and manipulation of a
statistical, financial, optimization, or
simulation model. Model-driven DSS use
data and parameters provided by users
to assist decision makers in analyzing a
situation; they are not necessarily data
intensive

8. Framework for Developing Decision
Support System
• A communication-driven DSS
supports more than one person working
on a shared task; examples include
integrated tools like Microsoft's Net
Meeting or Groove.

9. Framework for Developing Decision
Support System
• A data-driven DSS or data-oriented
DSS emphasizes access to and
manipulation of a time series of internal
company data and, sometimes, external
data.

10. Framework for Developing Decision
Support System
• A document-driven DSS manages,
retrieves and manipulates unstructured
information in a variety of electronic
formats.

11. Framework for Developing Decision
Support System
• A knowledge-driven DSS provides
specialized problem solving expertise
stored as facts, rules, procedures, or in
similar structures.

12. Decision Support System
Applications
 As mentioned above, there are theoretical
possibilities of building such systems in any
knowledge domain.
 Some of the examples is
Clinical decision support system for medical
diagnosis. Other examples include a bank loan officer
verifying the credit of a loan applicant or an
engineering firm that has bids on several projects and
wants to know if they can be competitive with their
costs.

13. Decision Support System
DSS is extensively used in business and
management. Executive dashboard and
other business performance software allow
faster decision making, identification of
negative trends, and better allocation of
business resources.

14. Decision Support System
A growing area of DSS application,
concepts, principles, and techniques is
in agricultural production, marketing for
sustainable development.

15. DSS characteristics and capabilities
 Support for decision makers in semi structured and
unstructured problems.
 Support managers at all levels.
 Support individuals and groups.
 Support for interdependent or sequential decisions.
 Support intelligence, design, choice, and
implementation.
 Support variety of decision processes and styles.
 DSS should be adaptable and flexible.
 DSS should be interactive and provide ease of use.
 Effectiveness balanced with efficiency (benefit must
exceed cost

16. Process of Building DSS
DSS is a computerized system for helping make
decisions. A decision is a choice between alternatives
based on estimates of the values of those
alternatives. Supporting a decision means helping
people working alone or in a group gather
intelligence, generate alternatives and make choices.
An interactive, flexible, and adaptable computer-
based information system, especially developed for
supporting the solution of a non-structured
management problem for improved decision making

17. Classification
They are: Passive, active, and cooperative DSS.
 A passive DSS is a system that aids the
process of decision making, but that cannot
bring out explicit decision suggestions or
solutions.
 An active DSS can bring out such decision
suggestions or solutions

18. Classification
A cooperative DSS allows the decision
maker (or its advisor) to modify,
complete, or refine the decision
suggestions provided by the system,
before sending them back to the system
for validation. The system again
improves, completes, and refines the
suggestions of the decision maker and
sends them back to her for validation

19. Classification DSS
A model-driven DSS emphasizes access
to and manipulation of a statistical,
financial, optimization, or simulation
model. Model-driven DSS use data and
parameters provided by users to assist
decision makers in analyzing a
situation; they are not necessarily data
intensive

20. Classification DSS
• A communication-driven DSS
supports more than one person working
on a shared task; examples include
integrated tools like Microsoft's
NetMeeting or Groove.

21. Classification DSS
A data-driven DSS or data-oriented
DSS emphasizes access to and
manipulation of a time series of internal
company data and, sometimes, external
data

22. Classification DSS
 A document-driven DSS manages,
retrieves and manipulates unstructured
information in a variety of electronic
formats.
 A knowledge-driven DSS provides
specialized problem solving expertise
stored as facts, rules, procedures, or in
similar structures.

23. Decision Support System
 Computer system at management level
of the organisation that combines data,
sophisticated analytical models and user
friendly software to support semi
structured and unstructured decision
making

24. DSS Components
 DSS database – a collection of current
or historical data from a no: of
applications or groups organised for
easy access by a range of applications
 DSS model base – a collection of
mathematical and analytical models that
can easily be made accessible to DSS
user

25. DSS Components
 DSS software permits easy interaction
between the user and database and the
model base

31. Characteristics of DSS
 Support semi structured and
unstructured problem analysis
 Incorporate the data of TPS/MIS and
the models of OR
 Used at many levels of the organisation

32. Core DSS Capabilities
 Representation
 Operation
 Memory Aids
 Control Aids

33. DSS Classes
 Model driven DSS
 Primarily stand alone system that uses
some type of model to perform analysis
 Data driven DSS
 A system that supports decision making by
allowing users to extract and analyze
useful information that was previously
buried in large databases

34. DSS Classes
 Customer decision support system
 System to support the decision making
process of an existing or potential
customer

35. Group DSS
 An interactive computer based system
to facilitate the solution to a problem by
a set of decision makers working
together as a group
 Components
 Hardware
 Software
 People

36. Group DSS
 Hardware
 Conference facility, display boards, audio
visual aids, computer, networking
equipment etc
 Software
 Electronic brainstorming tools,
questionnaires, idea organizers, tools for
voting and setting priorities, stakeholder
identification and analysis, group
dictionaries

37. Group DSS
 People
 Participants, facilitators etc

38. GDSS – Advantages
 Guaranteeing contributors anonymity
 Attendees can evaluate their own ideas
 Attendees can contribute without fear
 Structured methods for organizing and
evaluating ideas
 Easy documentation
 Increase the no: of ideas, thus the
quality of decisions

39. DATABASE MANAGEMENT SYSTEM
 Understand the importance of Data Base in
an organization.
 Examine the functions of DBMS.
 Analyze the presence of Data Structure
 Link various data types.
 Classify the DBMS types.
 Understand the functioning of System
Analysis and Design.
 Use of DFD

40. DATABASE MANAGEMENT SYSTEM
A database management system (DBMS) is
computer software designed for the purpose
of managing databases. Typical examples of
DBMSs include Oracle, DB2, Microsoft Access
, Microsoft SQL Server. A DBMS is a complex
set of software programs that controls the
organization, storage, management, and
retrieval of data in a database.

41. DATABASE MANAGEMENT SYSTEM
A DBMS includes:
 A modeling language to define the schema of
each database hosted in the DBMS, according
to the DBMS data model.
 The four most common types of organizations
are the hierarchical, network, relational and
object models. Inverted lists and other
methods are also used. A given database
management system may provide one or
more of the four models.

42. DATABASE MANAGEMENT SYSTEM
The dominant model in use today is the
ad hoc one embedded in SQL, despite
the objections of purists who believe
this model is a corruption of the
relational model, since it violates
several of its fundamental principles for
the sake of practicality and
performance.

43. DATABASE MANAGEMENT SYSTEM
 Data structures (fields, records, files and
objects) optimized to deal with very large
amounts of data stored on a permanent
data storage device.
 A database query language and report
writer to allow users to interactively
interrogate the database, analyze its data and
update it according to the users privileges on
data.
 A transaction mechanism.

44. Features and Abilities of DBMS
One can characterize a DBMS as an "attribute
management system" where attributes are
small chunks of information that describe
something.
DBMS roll together frequently-needed
services or features of attribute management.
This allows one to get powerful functionality
"out of the box" rather than program each
from scratch or add and integrate them
incrementally

45. Advantages of Data Base Management
System
 A database query language and report writer
to allow users to interactively interrogate the
database, analyze its data and update it
according to the users privileges on data.
 It also controls the security of the database.
Data security prevents unauthorized users
from viewing or updating the database
 Using passwords, users are allowed access to
the entire database or subsets of it called sub
schemas

46. Advantages of Data Base
Management System
Backup and replication
Copies of attributes need to be made
regularly in case primary disks or other
equipment fails. A periodic copy of attributes
may also be created for a distant organization
that cannot readily access the original.

47. Advantages of Data Base
Management System
Rule enforcement
Often one wants to apply rules to
attributes so that the attributes are
clean and reliable

48. Advantages of Data Base
Management System
Security
Often it is desirable to limit who can see
or change which attributes or groups of
attributes. This may be managed
directly by individual, or by the
assignment of individuals and privileges
to groups.

49. Advantages of Data Base
Management System
Computation
There are common computations
requested on attributes such as
counting, summing, averaging, sorting,
grouping, cross-referencing, etc.

50. Advantages of Data Base
Management System
Change and access logging
Often one wants to know who accessed
what attributes, what was changed, and
when it was changed. Logging services
allow this by keeping a record of access
occurrences and changes.

51. Advantages of Data Base
Management System
Physical view of Data
Physical Views is a pattern that shows
how to encapsulate a physical database
so that it can be easily accessed and
optimized without affecting upper layers
of software.

52. Data Flow Diagram
A data flow diagram (DFD) is a
graphical representation of the "flow" of
data through an information system. A
data flow diagram can also be used for
the visualization of data processing.
Data flow diagrams (DFDs) are one of the
three essential perspectives of
Structured Systems Analysis

53. Data Flow Diagram
Dataflow diagrams can be used to
provide the end user with a physical
idea of where the data they input
ultimately has an effect upon the
structure of the whole system from
order to dispatch to restock how any
system is developed can be determined
through a dataflow diagram.

54. Developing a DFD: Top-Down
Approach
The system designer makes a context
level DFD, which shows the interaction
(data flows) between the system
(represented by one process) and the
system environment (represented by
terminators).

55. Developing a DFD: Top-Down
Approach
 The system is decomposed in lower level
DFD (Zero) into a set of processes, data
stores, and the data flows between these
processes and data stores.
 Each process is then decomposed into an
even lower level diagram containing its
subprocesses.
 This approach then continues on the
subsequent subprocesses, until a necessary
and sufficient level of detail is reached
which is called the primitive process.

56. Event Partitioning Approach to
DFD
Construct detailed DFD.
 The list of all events is made.
 For each event a process is constructed.
 Each process is linked (with incoming data
flows) directly with other processes or via
datastores, so that it has enough information to
respond to a given event.
 The reaction of each process to a given event is
modeled by an outgoing data flow.

57. Data Structure
A collection of data with the best procedural
representation is called data structure. The
choice of the data structure often begins from
the choice of an abstract data type. A well-
designed data structure allows a variety of
critical operations to be performed, using as
few resources, both execution time and
memory space, as possible. Data structures
are implemented using the data types,
references and operations on them provided
by a programming language.

58. Common data structures
 Array
 Stacks
 Queues
 Linked lists
 Trees
 Graphs

59. ARRAY
In most programming languages each
element has the same data type and
the array occupies a contiguous area of
storage. Most programming languages
have a built-in array data type.
Multi-dimensional arrays are accessed
using more than one index: one for
each dimension

60. STACK
A stack is a temporary abstract data type and
data structure based on the principle of Last
In First Out (LIFO,).
Stacks are used extensively at every level of a
modern computer system. For example, a
modern PC uses stacks at the architecture
level, which are used in the basic design of an
operating system for interrupt handling and
operating system function calls.

61. STACK
A stack-based computer system is one
that stores temporary information
primarily in stacks, rather than
hardware CPU registers (a register-
based computer system).

62. QUEUE
A queue is a particular kind of collection
in which the entities in the collection
are kept in order and the principal (or
only) operations on the collection are
the addition of entities to the rear
terminal position and removal of entities
from the front terminal position.

63. QUEUE
Queues provide services in computer
science, transport and operations
research where various entities such as
data, objects, persons, or events are
stored and held to be processed later.

64. LINKED LIST
A linked list is one of the fundamental
data structures, and can be used to
implement other data structures. It
consists of a sequence of nodes, each
containing arbitrary data fields and one
or two references (“links”) pointing to
the next and/or previous nodes.

65. LINKED LIST
The principal benefit of a linked list over
a conventional array is that the order of
the linked items may be different from
the order that the data items are stored
in memory or on disk, allowing the list
of items to be traversed in a different
order

66. TREE
Tree is a widely-used data structure that
emulates a tree structure with a set of
linked nodes. A node may contain a
value or a condition or represents a
separate data structure or a tree of its
own.

67. GRAPH
A graph is a kind of data structure,
specifically an abstract data type (ADT),
that consists of a set of nodes and a set
of edges that establish relationships
(connections) between the nodes.

68. Database Management (DBM)
The Database Management Layer allows
script programmers to store information
as a pair of strings; a key, which is used
to find the associated value. Essentially,
a DBM adds more functionality and
better sorting during storage to the
binary flat-files that it uses.

69. Relational
The relational databases such as SQL,
Microsoft SQL Server and Oracle, have
a much more logical structure in the
way that it stores data. Tables can be
used to represent real world objects,
with each field acting like an attribute.

70. Type of Database
Databases have been in use since the
earliest days of electronic computing.
Unlike modern systems which can be
applied to widely different databases
and needs, the vast majority of older
systems were tightly linked to the
custom databases in order to gain
speed at the expense of flexibility.

71. Introduction to System Analysis
and Design
Systems are created to solve problems.
The subject System Analysis and Design,
mainly deals with the software
development activities.

72. Introduction to System Analysis
and Design
 understand a system
 understand the different phases of
system developments life cycle
 know the components of system
analysis
 know the components of system
designing

73. Defining A System
A collection of components that work
together to realize some objective
forms a system. Basically there are
three major components in every
system, namely input, processing and
output.

74. Defining A System
Input  Processing  Output

75. SYSTEM LIFE CYCLE
System life cycle is an organizational
process of developing and maintaining
systems. It helps in establishing a
system project plan, because it gives
overall list of processes and sub-
processes required developing a
system.

76. Phases of software development
cycle
 System study
 Feasibility study
 System analysis
 System design
 Coding
 Testing
 Implementation
 Maintenance

77. Phases of software development
cycle

78. PHASES OF SYSTEM DEVELOPMENT
LIFE CYCLE
System Study
System study is the first stage of system
development life cycle. This gives a clear picture of
physical system. In practice, the system study is
done in two phases. In the first phase, the
preliminary survey of the system is done which helps
in identifying the scope of the system. The second
phase of the system study is more detailed and in-
depth study in which the identification of user’s
requirement and the limitations and problems of the
present system are studied.

79. To describe the system study phase more
analytically…..
 Problem identification and project
initiation
 Background analysis
 Inference or findings

80. Feasibility Study
On the basis of result of the initial study,
feasibility study takes place. The
feasibility study is basically the test of
the proposed system in the light of its
workability, meeting user’s
requirements, effective use of
resources.

81. Feasibility Study
The main goal of feasibility study is not
to solve the problem but to achieve the
scope. In the process of feasibility
study, the cost and benefits are
estimated with greater accuracy.

82. System Analysis
Assuming that a new system is to be
developed, the next phase is system
analysis. Analysis involved a detailed
study of the current system, leading to
specifications of a new system..

83. System Analysis
Analysis is a detailed study of various
operations performed by a system and
their relationships within and outside
the system. During analysis, data are
collected on the available files, decision
points and transactions handled by the
present system

84. System Design
Based on the user requirements and the
detailed analysis of a new system, the
new system must be designed. This is
the phase of system designing.
The design proceeds in two stages :
 preliminary or general design
 structure or detailed design

85. Tools and techniques used for designing
 Flowchart
 Data flow diagram (DFDs)
 Data dictionary
 Structured English
 Decision table
 Decision tree

86. Structured Systems Analysis and
Design Method (SSADM)
SSADM is one particular implementation
and builds on the work of different
schools of development methods, some
of the key members of which included.

87. Logical data design
Also known as the logical system
specification stage. In this stage,
technically feasible options are chosen.
The development/implementation
environments are specified based on
this choice.

88. Logical data design
 Define BSOs (Business Systems Options). Its
purpose is to identify and define the possible
approaches to the physical implementation to
meet the function definitions. It also validates
the service level requirements for the
proposed system in the light of the technical
environment.
 Select BSO. This step is concerned with the
presentation of the BSOs to users and the
selection of the preferred option.

89. Logical process design
 Define user dialogue. This step defines the structure
of each dialogue required to support the on-line
functions and identifies the navigation requirements,
both within the dialogue and between dialogues.
 Define update processes. This is to complete the
specification of the database updating required for
each event and to define the error handling for each
event.
 Define enquiry processes. This is to complete the
specification of the database enquiry processing and
to define the error handling for each enquiry

90. Physical design
The following activities are part of this stage:
 Prepare for physical design
 Learn the rules of the implementation
environment
 Review the precise requirements for logical to
physical mapping
 Plan the approach
 Complete the specification of functions
 Incrementally and repeatedly develop the
data and process designs