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Page 1 of 24 MIS Chapter Prepared by Mr. K. Narayanan TPC-SSIET |Sources & Courtesy to: Internet | For Educational Use Only  MIS-Chapter 1, 2 & 3 Chapter 1- INFORMATION SYSTEM AND ORGANIZATION INTRODUCTION Planning for information systems, as for any other system, begins with the identification of needs. In order to be effective, dev elopment of any type of compu ter-based system should be a response to need--whether at the transaction processing level or at the more complex information and supp ort systems level s. Such plannin g for information systems is much li ke strategic planning in management. Objectives, prioritie s, and authorization fo r information systems projects need to b e formalized . The systems dev elopment pla n should identify specific projects slated for the future,priorities for each project and for resources, general procedures, and constraints for each application area. The plan must be specific enough to enable understanding of each application and to know where it stan ds in the order of developme nt. Also the pla n should be flexible so that priorities can beadjusted if necessary. King (King, 1995) in his recent article has argued that a strategic capability architecture - a flexible and continuously improving infrastructure of organizatio nal capabilities  is the primary basis for a company's sustainable competitive advantage. He has emphasized the need for continuously updating and improving the strategic capabilities architecture . SISP is the analysis of a corporation‘s information and processes using business information  models togethe r with the evaluation of risk, current needs and requirements. The result is an action plan showing the desired course of events necessary to align information use and needs with the strategic direction of the company (B attaglia, 1991). The same articl e emphasizes the need to note that SISP is a management function and not a technical one. The Perspective of Strategic Information Systems Planning In order to put the planning for strategic information systems in perspective, the evolution of information systems according to the three-era model of John Ward, et al.(1990) is pertinent. According to this model there are three distinct, albeit overlapping, eras of information systems, dating back to the 60‘s. The relationsh ip over time o f the three eras of information systems is shown in Table 1:

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MIS Chapter Prepared by Mr. K. Narayanan TPC-SSIET |Sources & Courtesy to: Internet | For Educational Use Only

MIS-Chapter 1, 2 & 3

Chapter 1- INFORMATION SYSTEM AND ORGANIZATION

INTRODUCTION

Planning for information systems, as for any other system, begins with the identification ofneeds. In order to be effective, development of any type of computer-based system shouldbe a response to need--whether at the transaction processing level or at the more complexinformation and support systems levels. Such planning for information systems is much likestrategic planning in management. Objectives, priorities, and authorization for informationsystems projects need to be formalized. The systems development plan should identifyspecific projects slated for the future,priorities for each project and for resources, generalprocedures, and constraints for each application area.

The plan must be specific enough to enable understanding of each application and to know

where it stands in the order of development. Also the plan should be flexible so thatpriorities can beadjusted if necessary. King (King, 1995) in his recent article has argued thata strategic capability architecture - a flexible and continuously improving infrastructure oforganizational capabilities – is the primary basis for a company's sustainable competitiveadvantage. He has emphasized the need for continuously updating and improving thestrategic capabilities architecture.

SISP is the analysis of a corporation‘s information and processes using business information models together with the evaluation of risk, current needs and requirements. The result isan action plan showing the desired course of events necessary to align information use andneeds with the strategic direction of the company (Battaglia, 1991). The same articleemphasizes the need to note that SISP is a management function and not a technical one.

The Perspective of Strategic Information Systems Planning

In order to put the planning for strategic information systems in perspective, the evolution ofinformation systems according to the three-era model of John Ward, et al.(1990) is pertinent.

According to this model there are three distinct, albeit overlapping, eras of informationsystems, dating back to the 60‘s. The relationship over time of the t hree eras of informationsystems is shown in

Table 1 :

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Table 2:

A. Impact Methodologies1. Value Chain Analysis: The concept of value chain is considered at length by MichaelPorter(1984). According to him, ‗every firm is a collection of activities that are performed to design,produce, market, deliver, and support its product. All these activities can be representedusing avalue chain.‘ Porter goes on to explain that information technology is one of the major support

acti vities for the value chain. ―Information systems technology is particularly pervasive in thevalue

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chain, since every value activity creates and uses information. .. The recent, rapidtechnological change in information systems is having a profound impact on competition andcompetitive advantage because of the pervasive role of information in the value chain...Change in the way office functions can be performed is one of the most important types oftechnological trends occurring today for many firms, though few are devoting substantialresources to it. .. A firm that can discover a better technology for performing an activity thanits competitors thus gains competitive advantage‖ (Porter, 1985). A typical value chain issummarized in the figure 2

Once the value chain is charted, executives can rank order the steps in importance todetermine which departments are central to the strategic objectives of the organization.Also, executives can then consider the interfaces between primary functions along the chainof production, and between support activities and all of the primary functions. This helps inidentifying critical points of inter-departmental collaboration. Thus, value chain analysis:

(a) is a form of business activity analysis which decomposes an enterprise into its parts.

Information systems are derived from this analysis.(b) helps in devising information systems which increase the overall profit available to a firm.

(c) helps in identifying the potential for mutual business advantages of componentbusinesses, in thesame or related industries, available from information interchange.

(d) concentrates on value-adding business activities and is independent of organizationalstructure.

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Strengths :

The main strength of value chain analysis is that it concentrates on direct value addingactivities of a firm and thus pitches information systems right into the realm of value addingrather than cost cutting.

Weaknesses : Although a very useful and intuitively appealing, value chain analysis suffersfrom a few weaknesses, namely,

(a) it only provides a higher level information model for a firm and fails to address thedevelopmental and implementation issues.

(b) because of its focus on internal operations instead of data, it fails to define a datastructure for the firm.

Critical Success Factor Analysis: Critical success factors analysis can be considered to beboth an impact as well as an alignment methodology. Critical Success Factors (CSF) in the

context of SISP are used for interpreting more clearly the objectives, tactics, and operationalactivities in terms of key information needs of an organization and its managers andstrengths and weaknesses of the organization‘s existing systems. Rockart (1979) definescritical success factors as being ‗for any business the limited number of areas in whichresults, if they are satisfactory, will ensure successful competitive performance for theorganization.‘ As shown in figure 3, CSFs can exist at a number of levels. Th ey representthe few key areas where things must go right for the business to flourish.

Consequently, critical success factors are areas of activity that should receive constant andcareful attention from management. Rockart originally developed the CSF approach as ameans to understanding the information needs of CEOs. The approach has subsequentlybeen applied to the enterprise as a whole and has been extended into a broader planning

methodology. It has been made the basis of many consulting practices and has achievedmajor results where it has been used well.

CSFs can exist at a number of levels, i.e., industry, organizational, business unit, ormanager‘s. CSFs at a lower level are derived from those at the preceding higher level. TheCSF approach introduces information technology into the initial stages of the planningprocess and helps provide a realistic assessment of the IT‘s contribution to the organization.

Hierarchy of CSF given below:

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Strengths :

CSF analysis provides a very powerful method for concentrating on key informationrequirements of an organization, a business unit, or of a manager. This allows themanagement to concentrate resources on developing information systems around theserequirements. Also, CSF analysis is easy to perform and can be carried out with fewresources.

Weaknesses :

(a) although a useful and widely used technique, CSF analysis by itself is not enoughto perform comprehensive SISP - it does not define a data architecture or providesautomated support for analysis.

(b) to be of value, the CSF analysis should be easily and directly related back to theobjectives of the business unit under review. It has been the experience of the people usingthis technique that generally it loses its value when used below the third level in an

organizational hierarchy (Ward,1990, p.164).(c) CSFs focus primarily on management control and thus tend to be internally focused andanalytical rather than creative (Ibid.).

(d) CSFs partly reflect a particular executive‘s management style. Use of CSFs as an aid inidentifying systems, with the associated long lead-times for developing these systems, maylead to giving an executive information that s/he does not regard as important (Ibid.).

(e) CSFs do not draw attention to the value-added aspect of information systems. WhileCSF analysis facilitates identification of information systems which meet the key informationneeds of an organization/business unit, the value derived from these systems is not

assessed.

IdentifyingKey Organizational Objective and Processes and Developing anInformation System

Abstract-Organizational Objective & Process

Development How, and to what extent, do people become involved in an organization and

committed to its goals? If an organization is to survive and to function effectively, it must

require not one, but several different types of behavior from most of its members, and the

motivations for these different types of behavior may also differ. How does a business

organization attract the kind of people it needs? How does it hold them? How does it induce

both reliable performance and spontaneous innovation an the part of its members? This

paper proposes an analytic framework for understanding the complexities of motivational

problems in an organization.

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Abstract- Developing an Information System

Several classification schemes of research are described and systems development isidentified as a developmental, engineering, and formulative type of research. A framework ofresearch is proposed to explain the dual nature of systems development as a researchmethodology and a research domain in IS research. Progress in several disciplinary areas isreviewed to provide a basis to argue that systems development is a valid researchmethodology. A systems development research process is presented from a methodologicalperspective. Software engineering, the basic method is applying the systems developmentresearch methodology, is then discussed. A framework to classify IS research domain andvarious research methodologies in studying systems development is presented. It issuggested that systems development and empirical research methodologies arecomplementary to each other. It is further proposed that an integrated multidimensional andmultimethodological approach will generate fruitful research results in IS research.

Information System Devlopment

System Development System is a set of components that interact to achieve a common goal. An information system is a collection of hardware, software, data, people and procedures that work togetherto produce quality information. An information system supports daily, short and long termactivities. System development is a set of activities used to build an information system.

System Development life cycle (SDLC)

The System Development Life Cycle consists of planning, analysis, design,implementation and operation, support and security.

Planning is to review and prioritize project requests as well as to form teams.

Conducting investigation and perform detailed analysis of activities is part of

analysis. Design requires you to acquire hardware and software and to also developdetails of the system.

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Implementation is clearly an important part of the life cycle and is when you developthe programs, install and test the program, train the users to use the program andconvert to the new system.

There are different ways to converting to the new system such as diving right into itor doing it gradually and piece by piece.

Monitoring system performance and assessing system security is part of the last partof the system development life cycle.

There are many different participants involved in system development.

A system analyst is responsible for designing and developing an information system.

The steering committee is the decision making body and vendors install and maintainthe operating system software as well as deal with technical support.

Application and system programmers convert the system design into the appropriateprogramming language.

The organizations website and web application are created and maintained by thewebmaster and web developers.

Project management is the process of planning, scheduling and controlling activities duringsystem development. They create a goal which is to deliver an acceptable system to theuser in an agreed time frame while maintaining costs.

Feasibility Assessment measures how suitable the development of a system will be to theorganization. There are many different types of feasibility assessments including: operation,schedule, technical and economic. Operational feasibility measures how well the proposedinformation system will work. Schedule feasibility measures whether the establisheddeadlines for the project are reasonable. Determining whether the organization has or canobtain the hardware, software and people need to deliver them is technicalfeasibility and economic feasibility, also called cost/benefit is whether the lifetime benefits of

the proposed information system will be greater that its lifetime costs.

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User role in Systems Development Process

1. Define requirements, value, and priority.

2. Assist in the design process where user interaction is called for.

3. Assist in testing or confirmation that requirements have been addressed insofar as theuser is the one interacting with the IS. Conformance with spec.

4. Assist in testing. Fitness for purpose.

The following picture explain the same in detail

Maintainability and Recoverability in System Design.

DESIGN FOR MAINTAINABILITY DESIGN CONSIDERATIONS

{A}

Factors that should be considered when designing for maintainability are provided below.

a. Non-Interference of Preventive Maintenance - Preventive maintenance should beminimized and require as little crew time as feasible.

b. Flexible Preventive Maintenance Schedule - Preventive maintenance schedules should besufficiently flexible to accommodate changes in the schedule of other mission activities.

c. Redundancy - If maintenance is necessary and system operations will be interrupted,

redundant installations should be considered in order to permit maintenance withoutinterrupting system operation.

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d. Goals of Designing for Maintainability - The following are goals for optimizing crewinvolvement in both preventive and corrective maintenance.

1. Reduce training requirements of crew.

2. Reduce certain skill requirements of crew.

3. Reduce time spent on preventive and corrective maintenance.

4. Increase maintenance capabilities during mission (especially corrective maintenance).

e. Corrective Maintenance - The following factors should be considered when designing forcorrective maintenance tasks.

1. The benefit gained from repair should be worth the time and effort expended on repair.

2. The time and effort involved in corrective maintenance should be weighed against the costand feasibility of carrying replacement units.

3. Required calibration, alignment, or adjustment should be easily and accuratelyaccomplished.

4. Automate fault detection and isolation tasks whenever possible.

12.3 DESIGN FOR MAINTAINABILITY DESIGN REQUIREMENTS

Equipment Design Requirements

All flight hardware and software shall be designed to facilitate on-orbit maintenance, check-out and shall be compatible with ground maintenance capabilities.

Equipment design shall minimize both complexity and time requirements for maintenance.

Equipment design for maintenance shall consider IVA as the prime resource; maintenanceby EVA shall be contingency only.

General Maintainability Design Requirements

General requirements to be followed when designing for maintainability are presented below.

a. Growth and Update - Facilities, equipment, and software design shall allowreconfiguration and growth during the mission.

b. Independence - Systems and subsystems shall be as functionally, mechanically,electrically, and electronically independent as practical to facilitate maintenance.

c. Maintenance Support Services - Maintenance support services ( e.g., electrical outlets)shall be accessible at potential problem locations or at a designated maintenance location.

d. Reliability - Equipment design shall reduce to a minimum the incidence of preventive and

corrective maintenance.

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e. Simplicity - Equipment design shall minimize maintenance complexity.

f. Time Requirements - Equipment design shall minimize the time requirements formaintenance.

g. Equipment - Maintenance equipment and tools shall be kept to a minimum.

h. Hazardous Conditions - System design shall preclude the introduction of hazardousconditions during maintenance procedures.

i. Critical Operations - Critical systems shall be capable of undergoing maintenance withoutthe interruption of critical services and shall be maintained.

j. Non-Critical Operations - Non-critical systems shall be designed to operate in degradedmodes while awaiting maintenance. Degraded mode operation shall not cause additionaldamage to the system or aggravate the original fault.

k. Redundancy Loss - Notification of loss of operational redundancy shall be providedimmediately to the crew.

l. Connectors - Quick-disconnect connectors shall be used.

Recoverability in System Design

One year after the 1997 publication of the first edition of this report, its essential findingremains unchanged and substantively unchallenged: The deployment of key recoverysystems designed to facilitate surreptitious government access to encrypted data andcommunications introduces substantial risks and costs. These risks and costs may not beappropriate for many applications of encryption, and they must be more fully addressed asgovernments consider policies that would encourage ubiquitous key recovery.

1. Access without end-user knowledge or consent -- Few commercial users need (orwant) covert mechanisms to recover keys or plaintext data they protect. On the contrary,business access rules are usually well known, and audit is a very important safeguardagainst fraud and error. Government specifications require mechanisms that circumvent thisimportant security practice.

2. Ubiquitous adoption -- Government seeks the use of key recovery for all encryption,regardless of whether there is benefit to the end-user or whether it makes sense in context.

In fact, there is little or no demand for key recovery for many applications and users. Forexample, the commercial demand for recovery of encrypted communications is extremelylimited, and the design and analysis of key recovery for certain kinds of communicationsprotocols is especially difficult.

3. Fast paths to plaintext -- Law enforcement demands fast (near real-time), 24-hour-a-day, 365-day-a-year access to plaintext, making it impossible to employ the full range ofsafeguards that could ameliorate some of the risks inherent in commercial key recoverysystems.

These special demands significantly increase the risks and costs identified in this report.While key recovery systems designed to meet commercial needs also have associated costs

and risks, we address most of our attention to the effects caused by the special demands --rapid, covert access to all encrypted data -- of government-access systems.

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Chapter-2 REPRESENTATION AND ANALYSIS OF SYSTEM STRUCTURE

1. Mathematical Model Representation

A mathematical model is a description of a system using mathematical concepts andlanguage. The process of developing a mathematical model is termed mathematicalmodelling . Mathematical models are used not only in the natural sciences (suchas physics, biology , earth science , meteorology ) and engineering disciplines (e.g. computerscience, artificial intelligence), but also in the social sciences (such aseconomics, psychology, sociology and political science) physicists, engineers, statisticians,operations research analysts and economists use mathematical models most extensively.

A model may help to explain a system and to study the effects of different components, and

to make predictions about behaviour.

Mathematical models can take many forms, including but not limited to dynamicalsystems, statistical models, differential equations, or game theoretic models. These andother types of models can overlap, with a given model involving a variety of abstractstructures. In general, mathematical models may include logical models, as far as logic istaken as a part of mathematics. In many cases, the quality of a scientific field depends onhow well the mathematical models developed on the theoretical side agree with results ofrepeatable experiments. Lack of agreement between theoretical mathematical models andexperimental measurements often leads to important advances as better theories aredeveloped.

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2. Graphical Model Representation

A graphical model is a probabilistic model for which a graph denotes the conditionalindependence structure between random variables. They are commonly used in probabilitytheory, statistics —particularly Bayesian statistics —and machine learning.

Types of Graphical Model

a) Bayesian network

If the network structure of the model is a directed acyclic graph, the model represents afactorization of the joint probability of all random variables. More precisely, if the eventsare then the joint probability satisfies

b) Markov random field

A Markov random field, also known as a Markov network, is a model over an undirectedgraph. A graphical model with many repeated subunits can be represented with platenotation.

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3. Hierarchical Model Representation

A hierarchical database model is a data model in which the data is organized into a tree -like structure. The structure allows representing information using parent/child relationships:each parent can have many children, but each child has only one parent (also known as a 1-to-many relationship ). All attributes of a specific record are listed under an entity type.

Example of an hierarchical model

Organization Chart

An organizational chart (often called organization chart , org chart ,organigram(me) ,or organogram(me) ) is a diagram that shows the structure of a norganization and therelationships and relative ranks of its parts and positions/jobs. The term is also used for

similar diagrams, for example ones showing the different elements of a field of knowledge ora group of languages.

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Tree Diagram

The term tree diagram refers to a specific type of diagram that has a unique network topology. It canbe seen as a specific type o fnetwork diagram, which in turn can be seen as a special kind of clusterdiagram.

The Information Flow / Data Flow

The graphic representation of data collection, data processing, and report distributionthroughout an organization. A data flow diagram (DFD) is a design tool to represent theflow of data through an information system.

A "context level" DFD can be used to show the interaction between a system and outsideentities; it can also show the internal data flows within a system. This version is also calleda context diagram . It often shows the information system as a single circular shape with nodetails of its inner workings: what it shows is its relationships with the external entities .

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Process Flow Definition

Creating repeatable business processes is an important part of building and running an effective organization. Well-designed and documented business processes are critical for the success of business activities, whether the company is a small two- or three-person business or a large corporation with hundreds of thousands of employees. Businesses that don't take the time and care to create repeatable processes run the risk of quality and consistency issues.

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Heuristic

In computer science and optimization a heuristic is a rule of thumb learned from experience but notalways justified by an underlying theory. Heuristics are often used to improve efficiency or

effectiveness of optimization algorithms, either by finding an approximate answer when theoptimal answer would be prohibitively difficult or to make an algorithm faster. Usually,heuristics do not guarantee that an optimal solution is ever found. On the other hand, resultsabout NP-hardness in theoretical computer science make heuristics the only viablealternative for many complex optimization problems which are significant in the real world.

An example of an approximation is one Jon Bentley described for solving the travellingsalesman problem (TSP) where it was selecting the order to draw using a pen plotter. TSP isknown to be NP-hard so an optimal solution for even moderate size problem is intractable.Instead the greedy algorithm can be used to to give a good but not optimal (it is anapproximation to the optimal answer) in a short amount of time. The greedy algorithmheuristic says to pick whatever is currently the best next step regardless of whether thatprecludes good steps later. It is a heuristic in that practice says it is a good enough solution,theory says there are better solutions (and even can tell how much better in some cases) . [1]

Method

In object-oriented programming, a method is a subroutine (or procedure or function )

associated with a class . Methods define the behavior to be exhibited by instances of theassociated class at program run time. Methods have the special property that at runtime,they have access to data stored in an instance of the class (or class instance or class object or object ) they are associated with and are thereby able to control the state of theinstance. [1] The association between class and method is called binding . A methodassociated with a class is said to be bound to the class. Methods can be bound to a class atcompile time ( static binding ) or to an object at runtime (dynamic binding ).

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Decomposition

Decomposition in computer science, also known as factoring , refers to the process bywhich a complex problem or system is broken down into parts that are easier to conceive,understand, program, and maintain.

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Aggregation

Aggregate function is which performs a calculation or computation on certain set of valuesrather than on a single value. In computer science all the management and spreadsheetsystems support a set of functions that can operate on a set of selected records or cells.Usually aggregate functions include things such as average, count, maximum, median,minimum, mode and sum. These functions are common in database management systemsand spreadsheets.

In addition aggregate function returns the requested data by retrieving stored values andcalculating the remaining values. For instance for ‗return value‘ the same data type as theaggregated variable will be shown. In syntax things such as aggregate, var, using, aggmap,from, fromspec, fromvar and textvar are used.

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Information architecture

(IA) is the art and science of organizing and labeling websites, intranets, onlinecommunities and software to support usability .[1] It is an emerging discipline and community of practice focused on bringing together principles of design and architecture to the digital

landscape . Typically it involves a model or concept of information which is used and appliedto activities that require explicit details of complex information systems. These activitiesinclude library systems and database development.

Application of System Representation to Case Studies.

Currently in use by many of the world's largest IT Operations groups, Hyperic's productsmanage some of the world's most complex Web infrastructures, including those of severalFortune 100 companies.

The list below shows some of our customers who are successfully using Hyperic HQ tomanage their infrastructure and have allowed their logos to be used on our site.

u is the cloud hosting division of Racks-pace, an industry leader that currently manages over 40,000 servers and devices for customers all over the world. Cloud hosting is the next iteration of hosting and Mosso is a world-class solution.

If I had to grow my IT team by 60 percent every three months just to keep up with our traffic growth, the business model simply would not scale. We need our people to be working on special projects, not routine maintenance

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Contegix provides high-level managed hosting solutions for enterprise applications. Wedeliver proactive, passionate support that is unparalleled in the industry. Contegix is now anational company with a hard-won reputation for reliable hosting, reasonable pricing andexpert support that goes beyond managed hosting.

We were immediately impressed with Hyperic's Auto-Discovery feature. It provided us withinstant monitoring for the majority of our key applications —even those that spannedvirtualized resources.

Chapter – 3 SYSTEMS, INFORMATION AND DECISION THEORY

Information Theory

In information theory, entropy is a measure of the uncertainty associated with a randomvariable. The term by itself in this context usually refers to the Shannon entropy, whichquantifies, in the sense of an expected value, the information contained in a message,usually in units such as bits. Equivalently, the Shannon entropy is a measure of the averageinformation content one is missing when one does not know the value of the randomvariable.

Information Content and Redundancy

Redundancy of information content in the genome of mammalian species may serve as agenetic protective mechanism by insuring a minimum degree of essential information toremain mutation-free over a given length of time. This type of redundancy can provideprotection against mutations caused by either random base modification or by replicationerrors. If the resultant mutation rate is significant, then the degree of redundancy containedin a genome would be expected to play an important role in determining its functionallifespan which may, in turn, determine the aging rate of the entire organism. The possiblecorrelation between the amount of redundant information and the aging rate of different

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mammalian species was evaluated by a comparison of the nucleotide sequence complexityand the redundancy of ribosomal and messenger RNA genes. No clear correlation wasfound between percentage reiterated nucleotide sequences or the amount of ribosomalgene redundancy and aging rate. However, there does appear to be a general decrease inthe percentage usage of ribosomal and transfer RNA genes as a mammalian species' aging

rate decreases. In addition, genes only involved in transcribing messenger RNA in the braintissues of mouse, cow and human were found to increase by an average redundancy valueof 1.3, 1.5, and 2.1, respectively. These results are discussed with respect to a unified theoryof aging for mammalian species .

Classification

In metadata a classification scheme is a hierarchical arrangement of kinds of things (classes)or groups of kinds of things. Typically it is accompanied by descriptive information of theclasses or groups. A classification scheme is intended to be used for an arrangement ordivision of individual objects into the classes or groups. The classes or groups are based oncharacteristics which the objects (members) have in common. In linguistics , the subordinateconcept is called a hyponym of its superordinate

Compression

In Computer Science The process by which data is compressed into a form that minimizes the spacerequired to store or transmit it.

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Summarizing

Summarization is the restating of the main ideas of the text in as few words as possible. Itcan be done in writing, orally, through drama, through art and music, in groups andindividually. There is extensive research that shows that summarization is among the top

nine most effective teaching strategies in the history of education (Marzano, Pickering, andPollock, 2001). Teachers who start a lesson by summarizing the big points in the day'slesson and end by having students summarize their learning see gains in the retention of thematerial. Several summarization techniques have been provided.

Filtering

Computer filters are software programs designed to protect a computer system. They come in twomain varieties: parental control filters, which block access to sites or categories of sites from aparticular computer, and anti-virus filters, which prevent malicious software from infecting theprotected computer.

Filters are must-haves for anyone with Internet access; they can prevent computer viruses fromstealing personal information, and they can help parents keep inappropriate content away fromkids.

Inference

Inference is the act or process of deriving logical conclusions from premises known orassumed to be true. The conclusion drawn is also called an idiomatic. The laws of validinference are studied in the field of logic.

Uncertainty

The uncertainty principle says that it is impossible to make an unambiguous choice betweennear-simultaneous events under a deadline. This principle affects the design of logic circuitsin computer hardware, real-time systems, and decision systems.

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Identifying Information needed to Support Decision Making

DECISION MAKING

Decision making is the developing concepts leading to the

selection of a course of action among variations. Every decisionmaking process produces a final choice It can be an action or anopinion. It begins when we need to do something but we do notknow what e.g. Decision to raise a Purchase Order

Characteristics and Capabilities of DSS

1. Support for decision makers in semi-structured andunstructured problems.

2. Support managers at all levels.

3. Support individuals and groups.

4. Support for interdependent or sequential decisions.

5. Support intelligence, design, choice, and implementation.

6. Support variety of decision processes and styles.

7. DSS should be adaptable and flexible.

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Human Factors

Definition: Human Factors is a discipline of study that deals with human-machine interface.Human Factors deals with the psychological, social, physical, biological and safetycharacteristics of a user and the system the user is in.

It is sometimes used synonymously with ergonomics, but ergonomics is actually a subset ofHuman Factors.

Problem Characteristics and Information System Capabilities in DecisionMaking.

Decision-Making Problems

Realistically, not every decision lends itself to automation and management. As notedpreviously, in most organizations, there‘s a spectrum from strategic decisions (where to

build the next distribution center, which new product will make the most profit)to operational decisions (cross-sell offers, eligibility). The right approach to supportingthese decisions varies, too, from a focus on decision support for knowledge workers to afocus on decision automation, as shown in the following figure. Operational decisions areideal for automation. Tactical decisions , those falling in the gray area between strategicand operational decisions, might also be good automation candidates but usually for partialautomation —a guided script, for example —rather than complete automation.