1. Revision

2. Knowledge Engineering Process of acquiring knowledge from experts and building knowledge base –Narrow perspective Knowledge acquisition, representation, validation, inference, maintenance –Broad perspective Process of developing and maintaining intelligent system © 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang 11-2

3. Knowledge Engineering Process Acquisition of knowledge –General knowledge or metaknowledge –From experts, books, documents, sensors, files Knowledge representation –Organized knowledge Knowledge validation and verification Inferences –Software designed to pass statistical sample data to generalizations Explanation and justification capabilities © 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang 11-3

4. © 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang 11-4 Development of a Real-Time Knowledge-lead to success. Problems with fermentation process –Quality parameters difficult to control –Many different employees doing same task –High turnover Expert system used to capture knowledge –Expertise available 24 hours a day Knowledge engineers developed system by: –Knowledge elicitation Interviewing experts and creating knowledge bases –Knowledge fusion Fusing individual knowledge bases –Coding knowledge base –Testing and evaluation of system

5. Introduction to Knowledge Management Knowledge management concepts and definitions. –Knowledge management The active management of the expertise in an organization. It involves collecting, categorizing, and disseminating knowledge. –Intellectual capital The invaluable knowledge of an organization’s employees. 11-5

6. © 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang 11-6 Elicitation Methods Manual –Based on interview –Track reasoning process –Observation Semiautomatic –Build base with minimal help from knowledge engineer –Allows execution of routine tasks with minimal expert input Automatic –Minimal input from both expert and knowledge engineer

7. © 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang 11-7 Manual Methods Case analysis Critical incident User discussions Expert commentary Graphs and conceptual models Brainstorming Prototyping Clustering of elements Iterative performance review

8. © 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang 11-8 Semiautomatic Methods Repertory grid analysis –Personal construct theory Organized, perceptual model of expert’s knowledge Expert identifies domain objects and their attributes Expert determines characteristics and opposites for each attribute Expert distinguishes between objects, creating a grid Expert transfer system –Computer program that elicits information from experts –Rapid prototyping –Used to determine sufficiency of available knowledge

9. © 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang 11-9 Semiautomatic Methods, continued Computer based tools features: –Ability to add knowledge to base –Ability to assess, refine knowledge –Visual modeling for construction of domain –Creation of decision trees and rules –Ability to analyze information flows –Integration tools

10. © 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang 11-10 Automatic Methods Data mining by computers Inductive learning from existing recognized cases Neural computing mimicking human brain Genetic algorithms using natural selection

11. © 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang 11-11 Evaluation, Validation, Verification Dynamic activities –Evaluation Assess system’s overall value –Validation Compares system’s performance to expert’s Concordance and differences –Verification Building and implementing system correctly Can be automated

12. © 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang 11-12 Artificial Intelligence Rules Advantages –Easy to understand, modify, maintain –Explanations are easy to get. –Rules are independent. –Modification and maintenance are relatively easy. –Uncertainty is easily combined with rules. Limitations –Huge numbers may be required –Designers may force knowledge into rule-based entities –Systems may have search limitations; difficulties in evaluation

13. © 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang 11-13 Generating Explanations Static explanation –Preinsertion of text Dynamic explanation –Reconstruction by rule evaluation Tracing records or line of reasoning Justification based on practical associations Strategic use of metaknowledge

14. © 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang 11-14 Uncertainty Probability Ratio –Degree of confidence in conclusion –Chance of occurrence of event Bayes Theory –Subjective probability for propositions Imprecise Combines values Dempster-Shafer –Belief functions –Creates boundaries for assignments of probabilities Assumes statistical independence

15. Approaches to Knowledge Management Process approach to knowledge management attempts to organize organizational knowledge through formalized controls, processes and technologies –Focuses on explicit knowledge and IT Practice approach focuses on building the social environments or communities of practice necessary to facilitate the sharing of tacit understanding –Focuses on tacit knowledge and socialization 11-15

16. Approaches to Knowledge Management Hybrid approaches to knowledge management –The practice approach is used so that a repository stores only explicit knowledge that is relatively easy to document –Tacit knowledge initially stored in the repository is contact information about experts and their areas of expertise –Increasing the amount of tacit knowledge over time eventually leads to the attainment of a true process approach 11-16

17. Information Technology (IT) in Knowledge Management The KMS cycle –KMS usually follow a six-step cycle: 1. Create knowledge 2. Capture knowledge 3. Improve (refine) knowledge 4. Store knowledge 5. Manage knowledge 6. Distribute (disseminate) knowledge 11-17

18. Information Technology (IT) in Knowledge Management Components of KMS –KMS are developed using three sets of core technologies: 1. Communication 2. Collaboration 3. Storage and retrieval –Technologies that support KM Artificial intelligence Intelligent agents Knowledge discovery in databases Extensible Markup Language (XML) 11-18

19. Information Technology (IT) in Knowledge Management Intelligent agents –Intelligent agents are software systems that learn how users work and provide assistance in their daily tasks –They are used to cause and identify knowledge See ibm.com, gentia.com for examples –Combined with enterprise knowledge portal to proffecienly disseminate knowledge 11-19

20. Roles of People in Knowledge Management Chief knowledge officer (CKO) The person in charge of a knowledge management effort in an organization –Sets KM strategic priorities –Establishes a repository of best practices –Gains a commitment from senior executives –Teaches information seekers how to better elicit it –Creates a process for managing intellectual assets –Obtain customer satisfaction information –Globalizes knowledge management 11-20

21. Roles of People in Knowledge Management Skills required of a CKO include: –Interpersonal communication skills –Leadership skills –Business wisdom –Strategic thinking –Collaboration skills –The ability to institute effective educational programs –An understanding of IT and its role in advancing knowledge management 11-21

22. Roles of People in Knowledge Management The CEO, other chief officers, and managers –The CEO is responsible for championing a knowledge management effort –The officers make available the resources needed to get the job done CFO ensures that the financial resources are available COO ensures that people begin to embed knowledge management practices into their daily work processes CIO ensures IT resources are available –Managers also support the KM efforts by providing access to sources of knowledge 11-22

23. Roles of People in Knowledge Management Community of practice (CoP) A group of people in an organization with a common professional interest, often self-organized for managing knowledge in a knowledge management system –See Application Case 11.7 as an example of how Xerox successfully improved practices and cost savings through CoP 11-23

24. Ensuring the Success of Knowledge Management Efforts Useful applications of KMS –Finding experts electronically and using expert location systems Expert location systems (know-who) Interactive computerized systems that help employees find and connect with colleagues who have expertise required for specific problems—whether they are across the county or across the room—in order to solve specific, critical business problems in seconds 11-24

25. Ensuring the Success of Knowledge Management Efforts Causes of knowledge management failure –The effort mainly relies on technology and does not address whether the proposed system will meet the needs and objectives of the organization and its individuals –Lack of emphasis on human aspects –Lack of commitment –Failure to provide reasonable incentive for people to use the system… 11-25

26. Ensuring the Success of Knowledge Management Efforts Factors that lead to knowledge management success –A link to a firm’s economic value, to demonstrate financial viability and maintain executive sponsorship –A technical and organizational infrastructure on which to build –A standard, flexible knowledge structure to match the way the organization performs work and uses knowledge 11-26

27. Ensuring the Success of Knowledge Management Efforts Factors that lead to knowledge management success –A knowledge-friendly culture that leads directly to user support –A clear purpose and language, to encourage users to buy into the system –A change in motivational practices, to create a culture of sharing –Multiple channels for knowledge transfer 11-27

28. Ensuring the Success of Knowledge Management Efforts Factors that lead to knowledge management success –A significant process orientation and valuation to make a knowledge management effort worthwhile –Nontrivial motivational methods to encourage users to contribute and use knowledge –Senior management support 11-28

29. © 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang 13-29 Agents Can act on own or be empowered Can make some decisions Can decide when to initiate actions Unscripted actions Designed to interact with other agents, programs, or humans Automates repetitive, narrowly defined tasks Continuously running process Must be believable Should be transparent Should work on a variety of machines May be capable of learning

30. © 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang 13-30 Successful Intelligent Agents Decision support systems Employee empowerment for customer service Automation of routine tasks Search and retrieval of data Expert models

31. © 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang 13-31 Classifications Intelligent Agents Organization agents –Task execution for processes or applications Personal agents –Perform tasks for users Private or public agents –Used by single user or many Software or intelligent agents –Ability to learn Franklin and Graesser’s autonomous agents

32. © 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang 13-32 Characteristics Intelligent Agents Agency –Degree of measurable autonomy –Ability to run asynchronously Intelligence –Degree of reasoning and learned behavior Mobility –Degree to which agents move through networks and transmit and receive data Mobile agents –Nonmobile are two dimensional –Mobile are three dimensional

33. © 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang 13-33 Advantages and Limitations Intelligent Agents Advantages: –Easy to understand –Systems and modules easily integrated –Saves development time and expense Allows for incremental and rapid development –Updates automatically –Resources reuse Limitations: –Oversimplified graphical representation –Needs additional tools –Incorrect definitions –Information may be incorrect or inconsistent –Security

34. © 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang 13-34 Management Issues regarding Intelligent Agents Expense Security Systems integration and flexibility Hardware and software requirements Agent accuracy Agent learning Invasion of privacy Competitive intelligence and industrial intelligence Other ethical issues Heightened expectations Systems acceptance

35. Knowledge Explicit knowledge –Objective, rational, technical –Policies, goals, strategies, papers, reports –Codified (organized) –Leaky knowledge Tacit knowledge –Subjective, cognitive, experiential learning –Highly personalized –Difficult to formalize –Sticky knowledge (when the one want to keep for himself or when it turns to be a hidden weapon) 9-35

36. Organizational Learning Learning organization –Ability to learn from past –To improve, organization must learn –Issues (terminologies) Meaning, management, measurement –Activities Problem-solving, experimentation, learning from past, learning from acknowledged best practices, transfer of knowledge within organization –Must have organizational memory, way to save and share it Organizational learning –Develop new knowledge –Corporate memory history Organizational culture –Pattern of shared basic assumptions based on the previous culture. 9-36

37. © 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang 1-37 Decision Support Frameworks A structured decision (Programmed) is one in which the phases of the decision-making process (intelligence: Searching for conditions that call for decisions. Design: Inventing, developing and analyzing possible courses of action. and choice: Selecting a course of action from those available.) have standardized procedures, clear objectives, and clearly specified input and output. There exists a procedure for arriving at the best solution.(SIMON’S Idea) An unstructured decision (Unprogrammed) is one where not all of the decision-making phases are structured and human plays an important role. (SIMON’S Idea) A semistructured decision has some, but not all, structured phases where standardized procedures may be used in combination with individual judgment. By intuition Gorry and Scott Morton

38. Turban, Aronson, Liang Sauter 38 Emerging Technologies Grid computing –Cluster computing power in an organization and utilize unused cycles for problem solving and other data processing needs. Improved GUIs –Due to improvements in web, expectations have risen. Model-driven architectures with code reuse –Software reuse and machine generated software by the computer aided software engineering tools has become prevalent. M-based and L-based wireless computing –As cellular phones and wireless pc cards are getting less expesive, m- commerce is evolving. Ex: FedEx uses mobile computer to track shipping packages and analyze patterns Intelligent agents: –help users and assist in e-commerce negotiations. Genetic algorithms, heuristics and new problem-solving techniques –Distributed as part of Java middleware and other platforms.

39. © 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang 2-39 Models Used for DSS Iconic –Small physical replication of system, it may be three dimensional such as that of an airplane, car, or production line. Or two-dimensional such as photographs. Analog –Behavioral representation of system –May not look like system Ex. Stock market charts that represent the price movements of stocks. Animations, videos, and movies. Quantitative (mathematical) –Demonstrates relationships between systems used in management science.

40. The benefits of Models Model manipulation is much easier. Models enable the compression of time. The cost of modeling is cheaper. The cost of making mistake over trial and error is much less. Risk could be estimated. Mathematical model use for massive products. Can model large and extremely complex systems with possibly infinite solutions Enhance and reinforce learning, and enhance training. © 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang 2-40

41. 41 Mathematical Model Identify variables Establish equations describing their relationships Simplifications through assumptions Balance model simplification and the accurate representation of reality. Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson, 6th edition, Copyright 2001, Prentice Hall, Upper Saddle River, NJ

42. © 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang 2-42 Decision Support Systems Intelligence Phase –Automatic Data Mining –Expert systems, CRM, neural networks –Manual OLAP KMS –Reporting Routine and ad hoc

43. © 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang 2-43 Decision Support Systems Design Phase –Financial and forecasting models –Generation of alternatives by expert system –Business process models from CRM, ERP, and SCM

44. © 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang 2-44 Decision Support Systems Choice Phase –Identification of best alternative –Identification of good enough alternative –What-if analysis –Goal-seeking analysis –May use KMS, GSS, CRM, ERP, and SCM systems

45. © 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang 2-45 Decision Support Systems Implementation Phase –Improved communications –Collaboration –Training –Supported by KMS, expert systems, GSS

46. © 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang 4-46 DSS Models Algorithm-based models Statistic-based models Linear programming models Graphical models Quantitative models Qualitative models Simulation models

47. © 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang 4-47 Major Modeling Issues Problem identification –Environmental scanning and analysis –Business intelligence tools // they can help identifying the problem by scanning for them. Identify variables and relationships –Influence diagrams –Cognitive maps Forecasting –Fueled by e-commerce –Increased amounts of information available through technology

48. © 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang 4-48

49. © 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang 4-49 MSS Mathematical Models –Decision variables describe alternative choices they could be people, time and schedules. –Uncontrollable variables are outside decision- maker’s control these factors con be fixed, in which case they are called parameters and they can vary. –Fixed factors are parameters. –Intermediate outcomes produce intermediate result variables. –Result variables are dependent on chosen solution and uncontrollable variables.

50. © 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang 4-50 Mathematical Programming optimization linear programming LP Characteristics: A limited quantity of economic resources is available for allocation. The resources are used in the production of products or services. There are two or more ways in which the resources can be used. Each is called a solution or a program. Each activity (product or service) in which the resources are used yields a return in terms of the stated goal. The allocation is usually restricted by several limitations & requirements called constraints.

51. © 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang 4-51 Mathematical Programming optimization linear programming LP allocation model is based on the following rational economic assumptions: Return from different allocation can be measured & compared. The return from any allocation is independent of other allocations. The total return is the sum of the returns yielded by the different activities. All data are known with certainty. The resources are to be used in the most economical manner.

52. © 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang 4-52 Mathematical Programming optimization linear programming

53. © 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang 4-53 Mathematical Programming optimization The most common optimization models can be solved by a variety of mathematical programming methods, they are:

54. © 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang 5-54 Data Warehouse

55. © 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang 5-55 Data Warehouse

56. Data Warehouse Design Star schema The data warehouse is based on the concept of dimensional modeling. Dimensional modeling is a retrieval based model that supports high volume query access. The star schema is the means of implementing the dimensional modeling. © 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang 5-56

57. © 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang 5-57 Data Warehouse Development Data warehouse implementation techniques –Top down –Bottom up –Hybrid –Federated Projects may be data centric or application centric Implementation factors –Organizational issues –Project issues –Technical issues Scalable = expandable. Flexible

58. © 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang 5-58 Data Warehouse

59. © 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang 5-59 Data Marts Data marts is a subset of a data warehouse, typically consisting of a single subject area (e.g., marketing, personnel…). Data mart can be either dependent or independent

60. © 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang 5-60 Data Marts Dependent is : –Created from warehouse by replication. It is copied from data warehouse, it is built after the building of warehouse. Functional subset of warehouse Independent –Scaled down, less expensive version of data warehouse –Designed for a department or SBU –Organization may have multiple data marts Difficult to integrate

61. © 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang 5-61 Business Intelligence and Analytics Business intelligence –Acquisition of data and information for use in decision-making activities Business analytics –Models and solution methods Data mining –Applying models and methods to data to identify patterns and trends

62. DASHBOARDS Dashboard provides the managers with exactly the information they need in the correct format at the correct time. BI systems are the foundation of dashboard, dashboards and scorecards measure and display what is important. It provide a real time view of data. © 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang 5-62

63. © 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang 5-63 OLAP Activities performed by end users in online systems –Specific –User can ask open-ended questions –Query generation SQL –Ad hoc reports –Statistical analysis –Building DSS applications Modeling and visualization capabilities Special class of tools // using SQL is helpful but not sufficient for OLAP here a special class of tools is used, known as :- –DSS/BI/BA front ends –Data access front ends –Database front ends –Visual information access systems

64. © 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang 5-64 OLAP The rules to evaluate OLAP on are : - 1.Accessibility. 2.Transparency. 3.Multimedia conceptual view. 4.Consistence reporting performance. 5.Client – server architecture. 6.Generic dimensionality. 7.Multi- user support. 8.Flexible reporting. 9.Intuitive data manipulation. 10.Unlimited dimension & aggregation level. 11.Unrestricted cross dimensional operation.

65. © 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang 5-65 Data Mining Data mining solving these classes of problems –Classification –Clustering –Association –Sequencing // like association but over a period of time. –Regression // form of estimation. –Forecasting –Others Hypothesis (we assume a situation & start investigation) or discovery driven (it come from the facts).

66. © 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang 5-66

67. © 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang 6-67 Agile Development Rapid prototyping Used for: –Unclear or rapidly changing requirements –Speedy development Heavy user input Incremental delivery with short time frames Tend to have integration problems

68. © 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang 6-68 DSS Prototyping Advantages –User and management involvement –Learning explicitly integrated –Prototyping bypasses information requirement –Short intervals between iterations –Low cost –Improved user understanding of system Disadvantages –Changing requirements –May not have thorough understanding of benefits and costs –Poorly tested –Dependencies, security, and safety may be ignored –High uncertainty –Problem may get lost –Reduction in quality –Higher costs due to multiple productions

69. © 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang 6-69

70. DSS © 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang 6-70

71. DSS © 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang 6-71