DBMS | fundamentals of DBMS | Data | Information | Knowledge | Data Base | Feature of data in database | Operations performed on the database |Components of database | Advantages of DBMS | Disadvantages of DBMS | Schema | Sub schema | Instances. |DBMS Architecture

DBMS

Data

Data is a valuable asset for an organization. Data is generally thought to be numbers and text. In addition data also includes multimedia files such as an image, audio files, video clips etc.

Information

Information is the manipulated and processed form of data.

Knowledge

Information organized and evaluated in the human's mind so that it can be used purposefully is known as Knowledge.

Data Base

Data is a very valuable resource in the operation and management of an organization. Database is a well organized collection of data that are related in a meaningful way, which can be shared by multiple users but stored only once.

Feature of data in database

• It should be well organized.
• It should be related
• It should be flexible to change.
• It should be recoverable in case of damage
• It should be stored permanently
• It should be shared among different users as well as applications.

Operations performed on the database

• Insertion
• Updation
• Deletion
• Selection

Components of database system environment

• Data
• Hardware
• Software
• Users

Data : the data act as bridge between the machine parts and the users which directly access it.

Data may be of different types

Userdata

Metadata

Application metadata

Hardware : The hardware consists of the secondary storage devices such as magnetic disks, optical disks , magnetic tapes etc.

Software: The software part mainly consist of DBMS which acts as a bridge between user and the database

Users: users are those persons who need information from the database to carry out their primary business responsibilities.

Types of users

Database Administrator

Database Designers

End users

Application programmers

DBMS stand for Database Management System. DBMS is basically a collection of programs that enable user to store, modify, extract information from Database as per the requirements.

Advantages of DBMS

• Controlling data redundancy
• Elimination of inconsistency
• Better services to the users
• Flexibility of the system is improved
• Standards can be enforced
• Security can be improved
• Provide backup and recovery

Disadvantages of DBMS

• Increased complexity
• Confidentiality, privacy and security risk
• Threat to data quality and data integrity
• Enterprise vulnerability
• Complexity of backup and recovery

Schema

The overall design of the database is called schema. A schema shows an overall structure into which the values of all data items are fitted.

Sub schema

A subset of schema is called a sub schema. It inherits the same properties as that of a schema

Instances.

The collection of information stored in the database at a particular moment called instance.

DBMS Architecture

The DBMS architecture is a framework where the structure of the DBMS is described. ANSI-SPARC (American Nationals Standards Institute- Standards Planning and Requirements committee). THE ANSI-SPARC three level architecture consists of the following three levels.

External level

Conceptual level

Internal level

External level : external level also known as individual user view is the highest level of three level DBMS architecture. This level describe the user's view of the database.in this level, only those portions of the database are described that are relevant to the user or applications program and hides the rest of the database details.

Conceptual level : the conceptual level  sometimes known as logical level that describe the logical structure of the whole database for user i.e. global view of data. it is represented as the middle level in the three level architecture. The conceptual view is defined by the conceptual schema which describe all the database entities, attributes, and relationships together with constraints.

Internal level : the internal level is the lowest level of the three level architecture of DBMS. This level describe how the data will be stored and also describe the data structure and access method to be used by the database.

Mapping between views

The three levels of DBMS architecture don't exist independently of each other. There must be correspondence between the three levels. This correspondence between different levels is known as Mapping.

Types of Mapping

• Conceptual/internal Mapping
• External/Conceptual Mapping

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Graphics |Multimedia | its elements | components | features | advantages | disadvantages

Multimedia

Multimedia Information systems make use of many different ways of communication. These can include text, graphics, image, voice, and video. The term multimedia is generally used to describe more sophisticated systems that support moving images and audio.

In order to work with multimedia a personal computer typically requires a powerful microprocessor, large memory and storage capacity, a high quality monitor, external loudspeakers or headphones and a sound card for improved sound generation, and CD-ROM OR DVD-ROM drive, as well as special software to utilize many of these devices.

  

Interactive multimedia

It means to interface with these media typically with a computer keyboard, mouse, touch screen, on screen buttons, and text entry allowing a user to make decision as to what takes place next with this multimedia

Use of multimedia in education

• Used as reinforcement
• Used to clarify a concept
• Creates the positive attitude of individuals
• The content of topic can be carefully selected
• The length of time needed for instruction can be reduced

Elements of multimedia

• Audio
• Videos
• Graphics
• Animation\
• Texts 

Audio

Audio signals are continuous analog signals. They are first captured by a microphone and then digitized and store usually compressed as CD quality audio requires 16- bit sampling at 44.1 KHz

Video

• Video is a technology of electronically capturing , recording, processing, storing, transmitting a sequence of images representing scenes in motion.
• Video is stored as a standard computer files
• Digital video clearly needs to be compressed
• Analog video is usually captured by a video cameras and then digitized

Graphics

Graphics are the backbone of any multimedia products. Graphics are created using variety of tools including paint brush, AutoCAD, drawing software and digital camera

Graphics are usually conducted by the composition of primitives objects such as lines, polygons, circles, and arcs. Graphics are the visual presentation on some surface. graphics are used to provide the back ground or information content for multimedia product

Animation

Animation is the rapid display of a sequence of images of 2-D or 3-D artwork or model positions in order to create an illusion of movement.

2-D Animation 2-D figures are created and/ or edited on the computer using 2-D bitmap graphics or created and edited using 2D vector graphics. This include automated computerized versions of traditional animation techniques, morphing, onion skinning

3-D animation 3-D animation digital models manipulated by an animator. In order to manipulated a mesh, it is given a digital armature. This process is called rigging.

Text

Text plays an important part in almost all multimedia. The design and content of multimedia texts are so different from other type of texts such as newspaper/ magazine text and book texts.

Designing text

Designing multimedia text involvs controlling two very important characteristics of multimedia text

• Display
• Content

Display deals with "How" parameters of multimedia texts- like "how the text is going to be represented at a given place" which font is going to be used to represent this text and with what color etc.

Content design poor content fail to impress the user and result in loss of interest in the whole project. So the content of the project must be impressive.

Display design text display design in multimedia projects involves decisions in controlling two main display parameters involved with multimedia texts :font and colours

Components of multimedia systems

1. Capture devices
2. Storage devices
3. Communication networks
4. Computer systems
5. Display devices

Features of multimedia systems

1. Very high processing power
2. Multimedia capable file systems
3. Data representation
4. Efficient and high i/o
5. Special operating systems
6. Storage and memory
7. Network support
8. Software tools

Advantages of multimedia

1. Enhancement of text only messages
2. Improve over traditional audio-video presentation
3. Gains and holds attention
4. Good for "computer phobic"
5. Multimedia is entertaining as well as educational

Disadvantage of multimedia

1. Investment cost
2. Technical barriers
3. Legal problems

Soft Computing | Definition of Fuzzy or Fuzzy Logic | Crisp sets | Fuzzy Sets | Fuzzy Relations | Fuzzy Inference System | Fuzzy Expert System

Definition of fuzzy

Fuzzy – “not clear, distinct, or precise; blurred”

Definition of fuzzy logic

A form of knowledge representation suitable for notions that cannot be defined precisely, but which depend upon their contexts.

Introduction to fuzzy logic

Uncertainty is inherent in accessing information from large amount of data; for example words like near and slow in sentences like

“My house is near to the office”

“He drives slowly”

If we set slow as speeds <=20 and fast otherwise, then is 20.1 is fast?

Crisp sets

Crisp sets: In a crisp set, members belong to the group identified by the set or not

  slow = {s such that  0 <= s <= 40}

   fast = {s such that  40 < s <70}

 40.1 belongs to set fast, hence 40.1 is not slow

  Drawback of crisp sets: Suppose a physical system has to apply brakes if the speed of the vehicle is fast and release the brake if the speed is slow. If the speed is in the interval [39, 41], such a system would continuously keep jerking which is not desired

The crisp set is defined in such a way as to divide the individuals in some given universe of discourse into two groups: members and nonmembers.

However, many classification concepts do not exhibit this characteristic.

For example, the set of tall people, expensive cars, or sunny days.

Fuzzy sets

A fuzzy set can be defined mathematically by assigning to each possible individual in the universe of discourse a value representing its grade of membership in the fuzzy set.

For example: a fuzzy set representing our concept of sunny might assign a degree of membership of 1 to a cloud cover of 0%, 0.8 to a cloud cover of 20%, 0.4 to a cloud cover of 30%, and 0 to a cloud cover of 75%.

For example let us evaluate few dates 12, 13, 14, 15, 16 August 2014

Crisp set { (12,1), (13, 1), (14, 0), (15, 1), (16,0)}

Here 12, 13, 15 belongs to sunny set.

Fuzzy set {(12, 0.9), (13, 1), (14, 0.8), (15,1), (16,0.3)}

Here all belongs to sunny set but with definite grade of membership.

A membership function

A characteristic function: the values assigned to the elements of the universal set fall within a specified range and indicate the membership grade of these elements in the set.

Larger values denote higher degrees of set membership.

A set defined by membership functions is a fuzzy set.

The most commonly used range of values of membership functions is the unit interval [0,1].

Fuzzy Sets

To reduce the complexity of comprehension, vagueness is introduced  in crisp sets

Fuzzy set contains elements; each element signifies the degree or grade of membership to a fuzzy aspect

Membership values denote the sense of belonging of a member of a crisp set to a fuzzy set

Example of a fuzzy set

Consider a crisp set A with elements representing ages of a set of people in years

A = { 2, 4, 10, 15, 20, 30, 35, 40, 45, 60, 70}

Classify the age in terms of six fuzzy variables or names given to fuzzy sets as: infant, child, adolescent, adult, young and old

Membership is different from probabilities

Memberships do not necessarily add up to one

Fuzzy Terminology

Universe of Discourse (U): The range of all possible values that comprise the input to the fuzzy system

Fuzzy set: A set that has members with membership (real) values in the interval [0,1]

Membership function: It is the basis of a fuzzy set. The membership function of the fuzzy set A is given by µA: Uà [0,1]

Fuzzy Relations

Generalizes classical relation into one that allows partial membership

Describes a relationship that holds between two or more objects

Example: a fuzzy relation “Friend” describe the degree of friendship between two person (in contrast to either being friend or not being friend in classical relation!)

A fuzzy relation        is a mapping from the Cartesian space X x Y to the interval [0,1], where the strength of the mapping is expressed by the membership function of the relation m    (x,y)

The “strength” of the relation between ordered pairs of the two universes is measured with a membership function expressing various “degree” of strength [0,1]

Fuzzy If-Then Rules

General format:

If x is A then y is B

Examples:

If pressure is high, then volume is small.

If the road is slippery, then driving is dangerous.

If a tomato is red, then it is ripe.

If the speed is high, then apply the brake a little.

LINGUISTIC VARIABLES

A linguistic variable is a fuzzy variable.

The linguistic variable speed ranges between 0 and 300 km/h and includes the fuzzy sets slow, very slow, fast, …

Fuzzy sets define the linguistic values.

Hedges are qualifiers of a linguistic variable.

All purpose: very, quite, extremely

Probability: likely, unlikely

Quantifiers: most, several, few

Possibilities: almost impossible, quite possible

TRUTH TABLES

Truth tables define logic functions of two propositions. Let X  and Y be two propositions, either of which can be true or false.

The operations over the propositions are:

Conjunction (Ù): X AND Y.

Disjunction (Ú): X OR Y.

Implication or conditional (Þ):            IF X THEN Y.

Bidirectional or equivalence (Û): X IF AND ONLY IF Y.

FUZZY RULES

A fuzzy rule is defined as the conditional statement of the form

If x is A

THEN y is B

where x and y are linguistic variables and A and B are linguistic values determined by fuzzy sets on the universes of discourse X and Y.

The decision-making process is based on rules with   sentence conjunctives AND, OR and ALSO.

Each rule corresponds to a fuzzy relation.

Rules belong to a rule base.

Example: If (Distance x to second car is SMALL) OR (Distance y to obstacle is CLOSE) AND (speed v is HIGH) THEN (perform LARGE correction to steering angle q) ALSO (make MEDIUM reduction in speed v).

Three antecedents (or premises) in this example give rise to two outputs (consequences).

FUZZY INFERENCE SYSTEMS (FIS)

Fuzzy rule based systems, fuzzy models, and fuzzy expert systems are also known as fuzzy inference systems.

The key unit of a fuzzy logic system is FIS.

The primary work of this system is decision-making.

FIS uses “IF...THEN” rules along with connectors “OR” or “AND” for making necessary decision rules.

The input to FIS may be fuzzy or crisp, but the output from FIS is always a fuzzy set.

When FIS is used as a controller, it is necessary to have crisp output.

Hence, there should be a defuzzification unit for converting fuzzy variables into crisp variables along FIS.

There are two types of Fuzzy Inference Systems:

Mamdani FIS(1975)

Sugeno FIS(1985)

MAMDANI FUZZY INFERENCE SYSTEMS (FIS)

Fuzzify input variables:

Determine membership values.

Evaluate rules:

Based on membership values of (composite) antecedents.

Aggregate rule outputs:

Unify all membership values for the output from        all rules.

Defuzzify the output:

COG: Center of gravity (approx. by summation).

SUGENO FUZZY INFERENCE SYSTEMS (FIS)

The main steps of the fuzzy inference process namely,

fuzzifying the inputs and

applying the fuzzy operator are exactly the same as in MAMDANI FIS.

The main difference between Mamdani’s and Sugeno’s methods is that Sugeno output membership functions are either linear or constant.

FUZZY EXPERT SYSTEMS

An expert system contains three major blocks:

Knowledge base that contains the knowledge specific to the domain of application.

Inference engine that uses the knowledge in the knowledge base for performing suitable reasoning for user’s queries.

User interface that provides a smooth communication between the user and the system.

Fuzzy Inference Processing

1.There are three models for Fuzzy processing based on the expressions of consequent parts in fuzzy rules

Suppose xi are inputs and y is the consequents in fuzzy rules

Mamdani Model: y = A 

where A is a fuzzy number to reflect fuzziness

Though it can be used in all types of systems, the model is more suitable for knowledge processing systems than control systems

2. TSK (Takagi-Sugano-Kang) model:       

y = a0 + Ʃ ai xi     where ai are constants

The output is the weighted linear combination of input variables  (it can be expanded to nonlinear combination of input variables)

Used in fuzzy control applications

3. Simplified fuzzy model: y = c

where c is a constant

Thus consequents are expressed by constant values

Soft Computing | Difference between soft computing and hard computing | Applications of soft computing | Techniques of Soft Computing

SOFT COMPUTING

Soft computing (sometimes referred to as computational intelligence, though ci does not have an agreed definition) is the use of inexact solutions to computationally hard tasks such as the solution of np-complete problems, for which there is no known algorithm that can compute an exact solution in polynomial time. Soft computing differs from conventional (hard) computing in that, unlike hard computing, it is tolerant of imprecision, uncertainty, partial truth, and approximation. In effect, the role model for soft computing is the human mind. The guiding principle of soft computing is: exploit the tolerance for imprecision, uncertainty, partial truth, and approximation to achieve tractability, robustness and low solution cost.

Difference between hard computing and soft computing

1)hard computing, i.e., conventional computing, requires a precisely stated analytic model and often a lot of computation time. 

Soft computing differs from conventional (hard) computing in that, unlike hard computing, it is tolerant of imprecision, uncertainty, partial truth, and approximation. In effect, the role model for soft computing is the human mind.

2) hard computing based on binary logic, crisp systems, numerical analysis and crisp software

Soft computing based on fuzzy logic, neural nets and probabilistic reasoning.

3) hard computing requires programs to be written, uses two-valued logic, is deterministic,requires exact input data, is strictly sequential, produces precise answers;

 soft computing canevolve its own programs, can use multi valued or fuzzy logic, incorporates stochastic, can deal with ambiguous and noisy data, allows parallel computations,  can yield approximate answers.

Applications

1 actuarial science actuarial science is the discipline that applies mathematical and statistical methods to evaluate risk in the insurance and finance industries.

2 agricultural engineering agricultural engineering is the engineering discipline that applies engineering science and technology to agricultural production and processing. Agricultural engineering combines the disciplines of animal biology, plant biology, and mechanical, civil, electrical and chemical engineering principles with knowledge of agricultural principles.

Healthcare

3 computer engineering computer engineering is a discipline that integrates several fields of electrical engineering and computer science required to develop computer systems. Computer engineers usually have training in electronic engineering, software design, and hardware-software integration instead of only software engineering or electronic engineering.

4 data mining data mining is a subfield of computer science which is the computational process of discovering patterns in large data sets involving methods at the intersection of artificial intelligence, machine learning, statistics, and database systems. The overall goal of the data mining process is to extract information from a data set and transform it into an understandable structure for further use.

5 environmental engineering environmental engineering is the integration of science and engineering principles to improve the natural environment like air, water, and/or land resources, to provide healthy water, air, and land for human habitation like house or home and for other organisms, and to remediate pollution sites

6 fault-tolerance fault-tolerance is the property that enables a system to continue operating properly in the event of the failure of some of its components. If its operating quality decreases at all, the decrease is proportional to the severity of the failure, as compared to a naïvely-designed system in which even a small failure can cause total breakdown

7 image processing i n imaging science, image processing is any form of signal processing for which the input is an image, such as a photograph or video frame; the output of image processing may be either an image or a set of characteristics or parameters related to the image. Most image-processing techniques involve treating the image as a two-dimensional signal and applying standard signal processing techniques to it. Power for the design, production, and operation of machines and tools.

8 medical diagnosis medical diagnosis refers both to the process of attempting to determine or identify a possible disease and to the opinion reached by this process. From the point of view of statistics the diagnostic procedure involves classification tests.

9  pattern recognition pattern recognition generally aim to provide a reasonable answer for all possible inputs and to perform "most likely" matching of the inputs, taking into account their statistical variation.

10 process control process control is a statistics and engineering discipline that deals with architectures, mechanisms and algorithms for maintaining the output of a specific process within a desired range. . Process control enables automation, with which a small staff of operating personnel can operate a complex process from a central control room

Soft computing techniques

Neural networks

Neural networks are a computational approach which is based on a large collection of neural units loosely modeling the way the brain solves problems with large clusters of biological neurons connected by axons. Each neural unit is connected with many others, and links can be enforcing or inhibitory in their effect on the activation state of connected neural units. Each individual neural unit may have a summation function which combines the values of all its inputs together. There may be a threshold function or limiting function on each connection and on the unit itself such that it must surpass it before it can propagate to other neurons.

Neural networks typically consist of multiple layers or a cube design, and the signal path traverses from front to back. Back propagation is where the forward stimulation is used to reset weights on the "front" neural units and this is sometimes done in combination with training where the correct result is known. More modern networks are a bit more free flowing in terms of stimulation and inhibition with connections interacting in a much more chaotic and complex fashion. Dynamic neural networks are the most advanced in that they dynamically can, based on rules, form new connections and even new neural units while disabling others.

Fuzzy logic

Fuzzy logic is a form of many-valued logic in which the truth values of variables may be any real number between 0 and 1, considered to be "fuzzy". By contrast, in boolean logic, the truth values of variables may only be the "crisp" values 0 or 1. Fuzzy logic has been employed to handle the concept of partial truth, where the truth value may range between completely true and completely false. Furthermore, when linguistic variables are used, these degrees may be managed by specific (membership) functions.

Genetic algorithm

It is known as an evolved antenna. In the field of artificial intelligence, a genetic algorithm (ga) is a search heuristic that mimics the process of natural selection. This heuristic (also sometimes called a metaheuristic) is routinely used to generate useful solutions to optimization and search problems.

Project Management | Decision Support System | Knowledge Based Systems | Applications | Decision Management Components

DECISION SUPPORT SYSTEM

A decision support system (DSS) is a computer-based information System that supports business or organizational Decision Making activities. DSSs serve the management, operations, and planning levels of an organization (usually mid and higher management) and help people make decisions about problems that may be rapidly changing and not easily specified in advance—i.e. Unstructured and Semi-Structured decision problems. Decision support systems can be either fully computerized, human-powered or a combination of both.

1.     DSS tends to be aimed at the less well structured, underspecified Problem that upper level managers typically face;

2.     DSS attempts to combine the use of models or analytic techniques with traditional data access and retrieval functions;

3.     DSS specifically focuses on features which make them easy to use by non-computer-proficient people in an interactive mode; and

4.     DSS emphasizes flexibility and adaptability to accommodate changes in the environment and the Decision Making  approach of the user.

KNOWLEDGE-BASED SYSTEMS

DSSs include knowledge-based systems. A properly designed DSS is an interactive software-based system intended to help decision makers compile useful information from a combination of raw data, documents, and personal knowledge, or business models to identify and solve problems and make decisions. The knowledge management component, like that in an expert system, provides information about the relationship among data that is too complex for a database to represent. It consists of rules that can constrain possible solution as well as alternative solutions and methods for evaluating them.

Typical information that a decision support application might gather and present includes:

·         inventories of information assets (including legacy and relational data sources, cubes, data warehouses, and data marts),

·         comparative sales figures between one period and the next,

·         projected revenue figures based on product sales assumptions.

Applications

One example is the clinical decision support system for medical diagnosis. There are four stages in the evolution of clinical decision support system (CDSS): the primitive version is standalone and does not support integration; the second generation supports integration with other medical systems; the third is standard-based, and the fourth is service model-based

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. Due to DSS all the information from any organization is represented in the form of charts, graphs i.e. in a summarized way, which helps the management to take strategic decision.

DSS are also prevalent in forest management where the long planning horizon and the spatial dimension of planning problems demands specific requirements. All aspects of Forest management, from log transportation, harvest scheduling to sustainability and ecosystem protection have been addressed by modern DSSs. In this context the consideration of single or multiple management objectives related to the provision of goods and services that traded or non-traded and often subject to resource constraints and decision problems. The Community of Practice of Forest Management Decision Support Systems provides a large repository on knowledge about the construction and use of forest Decision Support Systems

A specific example concerns the Canadian National Railway system, which tests its equipment on a regular basis using a decision support system. A problem faced by any railroad is worn-out or defective rails, which can result in hundreds of derailments per year. Under a DSS, the Canadian National Railway system managed to decrease the incidence of derailments at the same time other companies were experiencing an increase
                                         
                                            Data Management Component

 The data management component performs the function of storing and maintaining the information that you want your Decision Support System to use. The data management component, therefore, consists of both the Decision Support System information and the Decision Support System database management system. The information you use in yourDecision Support System comes from one or more of three sources:

-Organizational information; you may want to use virtually any information available in the organization for your Decision Support System. What you use, of course, depends on what you need and whether it is available. You can design your Decision Support System to access this information directly from your company’s database and data warehouse. However, specific information is often copied to the Decision Support System database to save time in searching through the organization’s database and data warehouses.

-External information: some decisions require input from external sources of information. Various branches of federal government, Dow Jones, Compustat data, and the internet, to mention just a few, can provide additional information for the use with a Decision Support System.

-Personal information: you can incorporate your own insights and experience your personal information into your Decision Support System. You can design your Decision Support System so that you enter this personal information only as needed, or you can keep the information in a personal database that is accessible by the Decision Support System.

Project Management | Definition of project | Definition of Project Management | Problems in Software Projects

Project

The fundamental nature of a project is that it is a “temporary endeavor undertaken to create a unique product, service, or result

                                                  Project management

Project management is the process of the application of knowledge, skills, tools, and techniques to project activities to meet project requirements.” That is, project management is an interrelated group of processes that enables the project team to achieve a successful project. These processes manage inputs to and produce outputs from specific activities; the progression from input to output is the nucleus of project management and requires integration and iteration.

problems in Software Projects

Software projects are similar to traditional projects in the sense that the same

types of problems affect them both. However, the difference in managing these

problems lies in the approach that you take to the specific issue. For example, a

technology-related problem for a software project might be the low degree of

reuse of the software components created. However, for a car-manufacturing firm,

there is no chance of reusing a component such as a front axle

People-related problems

Process-related problems

Product-related problems

People-related problems

Low motivation: As the project manager it is your responsibility to ensure an

optimal level of motivation within the team. Lengthy projects, complex activitiesand scarce resources often decrease the motivation level in a

software development team. However, you need to lead in such a way that the

team is constantly motivated to do a good job

Problem employees: Some members of any team always create a problem.  Problem

employees raise the chances of conflicts and differences of opinions within

the development team. They lower the efficiency and productivity of other

team members and make it difficult to meet the objectives of the software

project within the specified time. You need to ensure that employees are not

allowed to create a problem for the rest of the team

Lack of stakeholder interest: For a software project to be a success, each

stakeholder needs to take an active interest in the progress of the project. Al1

stakeholders, including the customer, the management, and the software

development team, need to commit to the success of the project. For example,

if the software development team is not committed to the project, then their

contribution may not be to the optimum level

Process- related Problems

Unrealistic schedule: Assigning unrealistic deadlines for a software project is

a primary reason why software projects are delayed. Often, the marketing or

the management team commit a delivery date to the customer in the hope of

getting the project contract. However, these dates are not decided in

consultation with the development team. The rationale for assigning the

deadlines is unfounded. You need to ensure that the deadlines match the

ability of the software team to deliver the software product. 

.

·

Insufficient identification: Unidentified, partially identified, and unplanned

risks pose a threat to the success of a software project. You need to intensively

identify risks and evolve a risk management plan such that the project is

completed successfully, on time

unsuitable life cycle model selection: Different software projects require

different SDLC models. For example, a project to create banking .software is

different from software for a satellite where the concept needs to be

researched. For the former example, the Waterfall model is more applicable.

For the latter example, the Spiral model is more suitable. Selecting the correct

life cycle model is critical to the success of a software project.

Product-related Problems

Product scope changed toward the end of the project life cycle: The project

time, effort, and cost estimates for a software project can go up dramatically

when the customer changes the scope 9f the product toward the end of the

project. In such situations, you should verify the criticality of the scope

change. However, if the change request is not critical, you should retain the

original scope with a proper explanation to the customer. If the change request

is critical, you should explain the situation to the customer. Usually, a

customer gives more time and funds to a software project if proper

justification is provided. In some cases, the scope change may also be because

of a change in government policy. It may become mandatory for you to

include such change requests.

·

Research-oriented software development: Many software projects digress

from the original scope because of the nature of the software product or

technology used. When a totally new kind of software is developed 

or a new technology is used, the software development team can lose focus of the

objectives by getting into a research-oriented approach. It becomes your

responsibility as the project manager to maintain the focus on the objective