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EXTRAS

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  1. EXTRAS

  2. Table 2.2 First Generation Computers

  3. Table 2.4 Third Generation Computers

  4. Table 2.3 Second Generation Computers

  5. Table 2.5 Fourth Generation Computers

  6. Table 2.6 Fifth Generation Computers

  7. Table 3.1 Operating Systems and their features

  8. Table 3.2 Data Processing Steps

  9. Data Collection Fig. 3.3 Data Processing Steps Data Collation Data Conversion Data Written in Documents Data in Machine Readable Form Input Unit Memory CPU Processed Data in Internal Form Output Unit Data Transformed to a readable form

  10. Fig. 4.2 Decision making at different levels of organization Unstructured Policy Planning (Strategic) Tactical Planning Operational Planning Transaction Planning Structured

  11. Table 4.1Steps in decision making process as illustrated by Griffin

  12. Fig 5.1 Data Processing Stored Data Input (Data) Processing (Processor) Output (Information)

  13. Table 5.2 Difference between planning and control information

  14. Data Storage Stored Frames Of Reference Mental Processing Input Data Decisions Fig. 5.5 Human information processing mechanism and decision-making process

  15. Receptors Processor Environment Memory Effectors Fig 5.7 Human Information Processing System

  16. Table 6.1 Summarizations of DSS approach

  17. Table 6.2 Core capabilities of DSS

  18. Table 6.4 Decision and type of system required

  19. Fig. 6.6 The AI Onion Model Natural Language Processing Heuristic Search Modelling and Representation of Knowledge Problem Solving and Planning Computer Vision AI Language And Tools Common Sense Reasoning and Logic Expert Systems

  20. Fig. 7.4 Usefulness of Feed back Desired Performance actual Performance Implement Course Correcting Programme Actual Performance Measurement Programme for Correcting Action Actual Vs Standard Performance Compared Analyse Causes for Deviation Identify Deviation

  21. Determine Business Objectives

  22. Table 8.1 Capabilities of CASE Tools

  23. Computer Based Personnel System Table 9.1 DBMS : IIIustration Employees Name Address Position Marital Status Date of Joining Personnel Dept. Personnel Application Programm Payroll Grade/Scale Income Tax Professional Tax Misc Net Salary Database Management System Payroll Application Program Establishment Dept. Benefits Application Program Benefits Group Insurance Medicaim ESI PF Pension Benefits Dept.

  24. Fig. 10.3 Flow of data inside the Data Wherehouse

  25. Fig. 10.4 Benefits from data Warehousing LOCAL IMPACT EASY TO MEASURE TIME SAVINGS FOR DATA SUPPLIERS AND FOR USERS MORE AND BETTER INFORMATION BETTER DECISIONS IMPROVEMENT OF BUSINESS PROCESSES SUPPORT FOR THE ACCOMPLISHMENT OF STRATEGIC BUSINESS OBJECTIVES GLOBAL IMPACT HARD TO MEASURE

  26. Table 12.2 Quality Factors

  27. The Pay Off Matrix

  28. Information Process

  29. Long Term Memory Processor Short Term Memory Output Input Elementary Processor Interpreter Newell – Simon Model

  30. Steps in defining a proposed information architecture in Business Systems Planning

  31. Cyclical functioning of Data Mining Iteration Measure results Understand Situation Initiate appropriate action Develop Model Understand analysis

  32. Quality Profile Model Transcendental Properties (not quantifiable) Quality factors (objectively measurable) Merit Indices (Subjectively measurable) Quality Ratings (Quantifications of value judgement) Quality Attributes (indicates presence or absence of a property) Quality Metrics (Quantifiable)

  33. Data Mining Association Case Consider sitting in an English pub and buying a pint of beer but not a bar meal. While servicing the request, the barkeep asks if you are interested in a bag of chips as well. Why would the keep ask such a question? Because it is the goal of the keep, in some regards, to be profitable and maximize the amount of revenue per transaction. By asking if you wanted chips, the barkeep may make a bigger tip or the bar may make more revenue. The barkeep knew to ask you this question, and knew there was a good chance (a high probability) that you would also take the chips. The barkeep had this knowledge from experience, specifically from previous interactions with customers. Similarly, the association rule finding algorithm is trained on historical data, i.e. past transactions. The data contains checkout information and a list of products that were purchased in each transaction, perhaps along with other information (volume, sale amount, although in many cases just the presence or absence of a product in a transaction is sufficient). While training, the algorithm may identify a relationship (a form of an association) between beer and no bar meals, and predict you are more likely to buy crisps (US. chips) over someone not identified with that relationship. Typically the relationship will be in the form of a rule such as: IF {beer, no bar meal} THEN {crisps} The probability that a customer will buy beer without a bar meal (i.e. that the antecedent is true) is referred to as the support for the rule. The conditional probability that a customer will purchase crisps is referred to as the confidence of the rule.