Critical Issues in Information Systems

1. Critical Issues in Information Systems BUSS 951 Lecture 9 Systems for Organisations 3: Knowledge Technologies

2. Notices (1)General • Assignment 2 is due today, and will be available at the beginning of week 11 • Assignment 3 will be put up on the web during the first week of the holidays- download it at your leisure • Please check that your Assignment 1 mark is correctly recorded, current marks are available on the departmental notice board and pick up assignments if you have not yet done so • BUSS951 is supported by a website (available from Tomorrow), where you can find out the latest Notices get Lecture Notes, Tutorial Sheets, Assignments etc www.uow.edu.au/~rclarke/buss951/buss951.htm

3. Notices (2)Readings for this week • Last week we postponed seminar discussion of the allocated readings in order to discuss arguments for Assignment 2, • this week we will discuss these readings: • Yu, E. (1998) “Why agent-oriented requirements engineering” Reading 6 • Yu, E. S. K and J. Mylopoulos (1994) “From E-R’ to A-R’- Modelling Strategic Actor Relationships for Business Process Reengineering” Reading 7

4. Agenda (1) • Discuss some problems with traditional systems analysis views of work in offices • Promote a view which looks at office work in terms of action and human communication (similar to a Systems Auditors View of an IS) • Introduce the ideas behind Action Workflow (one type of LAP approach)

5. Decision Support Systems

6. Programmed Decisions Nonprogrammed Decisions Continuum Decision Support Systems (1) • information producing system aimed at a particular problem: • that a manager must solve • decisions that a manager must make • Programmed Decisions • repetitive and routine • definite procedure for handling them • Nonprogrammed Decisions • novel and unstructured • problem hasn’t arisen before

7. Decision Support Systems (2)Phases of Decision Making • Intelligence Activity • searching environment for problems • Design Activity • inventing, developing, analysing possible courses of action • Choice Activity • selecting a course of action • Review Activity • assessing past choices

8. Developing DSS • using Simons phases • can build systems which perform this work • use a special form of systems development called rapid prototyping (see appropriate lecture)

9. DSS Capabilities (1) • May use one or more techniques depending on the problem: • Mathematical Modelling • Simulation • Graphics • Visualisation (Virtual Reality)

10. DSS Capabilities (2) • Mathematical Modelling • Static Model (doesn’t include time) • Dynamic Model (includes time) • Deterministic Model (avoids probability) • Probabilistic Models (includes probability) • Optimising Model (selects best solution) • Suboptimising Model (‘satisficing’ solution)

11. DSS Capabilities(3) Steps towards Simulation: • scenario & scenario data elements • managers input is referred to decision variables (With McDonalds Example: people, number of queues, expected load over a day)

12. DSS Model Individual Problem Solvers Other Group Members Report writing software Math Models GDSS Software Database Environment Environment Coms Data Info

13. DSS/ES • Decision Support Systems applications are beginning use a technology called Expert Systems • an Expert System (ES) comprises a collection of facts and a set of rules which are processed by an inference engine • we will discuss these in greater depth latter in the lecture

14. Executive Information Systems

15. Executive IS (1) • Executive Information Systems (EIS) are designed specifically for managers on the strategic planning level • executive activity is not very structured- difficulties in establishing an understanding of executive problem solving • Warning: problems with acronyms- the term EIS has a variety of definitions it is also been defined as Enterprise Information Systems Executive Information Systems

16. Executive IS (2) • three ways of achieving EIS: • develop custom software • application end-user development software (spreadsheets, database, graphics) • install special EIS software • EIS characteristic- drill down • executive begins with an overview • gradually retrieve more information

17. executive begins with an overview and then gradually retrieves more specific information often by accessing lower level systems (transaction processing or operational systems) Executive IS EIS Characteristics- Drill down

18. taking a large database progressively searching through the data using various combinations of variables in order to understand a complex problem which is resolvable Executive IS EIS Characteristics- Slice and Dice

19. Executive IS (3) • sometimes a distinction is made between EIS and ESS • Executive Support Systems (ESS) • support information needs • also communications& analysis needs • does this by providing intelligenceie. by understanding how information affects operations. Does this distinction make any sense?

20. Personal Computer Make Corp. Info. available EIS Model Executive Workstation Info Requests Info Display Executive database To other Workstations To other Workstations Corp. Database Current news, explanations External data & info. Electronic Databases Software Library Corporate Mainframe

21. Concept of Organisational Information Subsystems (1) • subsets of MIS tailored to meet users needs in functional areas • examples: • marketing information systems • manufacturing information systems • financial information systems • human resources information systems

22. Concept of Organisational Information Subsystems (2) • Remember: Nothing physically separates these systems- rather these systems are logical distinct • databases used by one organisational subsystem can be used by others • programs may also be shared

23. Concept of Organisational Information Subsystems (3) • these systems are not an alternative to a firm-wide MIS • need these subsystems to have a complete organisation wide MIS • development strategy: MIS then implement organisational information subsystems

24. Organisational Information Systems EIS Marketing Financial Human Resource Manufacturing Information Systems

25. Knowledge SystemsArtificial Intelligence & Expert Systems

26. Knowledge SystemsAI vs ‘Non-Intelligent’ programs • Artificial Intelligence (AI) has emerged as one of the most significance technologies of this century • subfield of computing science that is concerned with symbolic reasoning and problem solving, by manipulation of knowledge rather than mere data • classical ‘non-intelligent’ computer programs: • are rigid, structured procedures that deal with specific problems • programs may be flexible and may be capable of dealing with complex situations • everything non-intelligent programs do is predictable or preordained

27. Knowledge SystemsIntelligent Programs • Intelligent Programs • can on occasion exhibit behaviours that were not programmed • consists of a complex set of rules on how to process data as well as having information stored in a database • thee programs are generally goal directed- they are designed to behave in order to reach their goals rather than being told how to achieve them

28. Conventional Programs Often primarily numeric Algorithmic- solution steps explicit Integrated information and control Difficult modification Correct answer required Best possible solution usually sought AI Programs Primarily symbolic processes Heuristic search- solution steps implicit Control structure usually separate from knowledge domain Usually easy to modify, update and enlarge Some incorrect answers are often tolerable Satisfactory answers usually acceptable Knowledge SystemsHeuritsics- Rules of Thumb A key characteristic of AI programs is heuristics or rules of thumb which guide the execution of the program:

29. Knowledge SystemsAI: A Broad Spectrum of Technologies • Expert systems • Natural language • Speech processing • Vision • Robotics • Cognitive modelling • Knowledge representation and utilization • Problem solving and inference • Learning- knowledge acquisition • Special purpose computer hardware

30. Knowledge SystemsExpert Systems • “An expert system is an intelligent computer program that uses knowledge and inference procedures to solve problems that are difficult enough to require significant human expertise for their solution. The knowledge necessary to perform at such a a level, plus the inference procedures used, can be thought of a s a model of the expertise of the practitioners of the field” • “the knowledge of an expert consists of facts and heuristics. The ‘facts’ constitute a body of information that is widely shared, publicly available, and generally agreed upon by experts in a field. The performance level of an expert system is primarily a function of the size and quality of the knowledge base that it processes”(Feigenbaum, E. A. & P. McCorduck 1983)

31. Knowledge Systems Expert Systems Applications • Typical expert systems applications (O’Brien, 355): • Decision Management (recommendations) • Diagnostic and troubleshooting • Maintainence and Scheduling (prioritizing, scheduling) • Intelligent text and documentation (legal documents) • Design/configuration • Selection/classification (suspect id) • process monitoring/control (chemical testing)

32. architecturally expert systems are considered as rule-based systems they provide a separation between the knowledge base, and the inference engine, and may contain a built-in explanation facility (reasoning) and a dialogue component- (based on computation linguistics) Knowledge SystemsExpert Systems-Architecture

33. Expert systems application areas are often divided into particular domains analysis problems which include: debugging, diagnosis, and interpretation synthesis problems which include: Configuration Planning, and scheduling ClassesApproach Diagnosis Pattern recognition Design Object Generation Planning Action Generation Simulation Modelling Knowledge SystemsES Application Areas & Problem Classes

34. developed by Stefik et al (1984), this table consists of the requirements and possible development prescriptions that might be used when developing expert systems three major development options include: when there is unreliable data or knowledge when the data are time varying, and when the problem to be solved involves a big solution space ES System Prescriptions

35. interestingly although we might think of expert systems as somehow privilege and special, in fact building them is similar to other systems notice the prototyping cycle in the lower half of the diagram this is what is referred to as Rapid Prototyping and is found in conventional systems development as well here it is rather well suited to the process of rule creation and improvement ( = knowledge acquisition) Knowledge SystemsExpert Systems Development

36. Expert System ClassificationKnowledge vs Technological Complexity Broad scope, great depth, information ambiguity, high database mgmt, integrity

37. Knowledge Systems Types of Expert Systems 1: Low Tech • Personal productivity systems- simplest system, eg. Personal budgeting systems running on PCs, built using an expert systems shell • Power Decision system- knowledge intensive incorporating skills of highly skilled decision makers, goal top improve decision making of a group eg. REVA- repairs and faults with electric pumps centrifuges etc

38. Knowledge Systems Types of Expert Systems 2: High Tech 3. Integrated production systems- limited amounts of domain complexity but involve advanced technology. Tend to target organisational productivity- improving throughput, reducing headcounts and lowering costs, eg. Telex Router receives all telexes coming into a banks headquarters, reformats them and routes them to appropriate employees 4. Strategic Impact Systems- are both broad and deep in their domains. The decision process is long and intricate, and often requires testing of numerous hypotheses. Example: Lincoln National Life Underwriting System which knows about medical, financial and insurance fields

39. Improve personal decision making Improve group decision making and enhance the quality of product service Improve organisational throughput and costs Create market barriers and improve organisational decision making and productivity Knowledge Systems Benefits of Expert Systems

40. Personal Productivity Systems: end user- supported by information systems Power Decision Systems: existing business unit- close to experts Integrated Production Systems: Data processing/MIS departments- driven by technical needs Strategic Impact Systems: new system business unit- full time, resource hungry, new business generating Knowledge Systems Benefits of Expert Systems

41. Bowerman & Glover (1988) identified five different types of integration between IS and ES based on production environment requirements: Standalone eg. Personal Consultant Plus Business eg. ACORN & Lotus 1-2-3 Scientific eg. Rulemaster & Informix Process Control: eg. PICON & GKS & TDC-3000 Decision Support: eg. TIMM & Comshare & Nichols & Versatec Integrating Expert Systems

42. a major area of research activity was how to construct standardised interfaces between numerous expert systems in the diagram on left- a hypothetical process control function- the inputs constitute data collected from sensors and condensed by pre-processing computer outputs consist of conventional computer for controlling processes Integrating Expert Systems

43. ConclusionsSome Critical Issues • we have concentrated on expert systems as new of the most useful ‘knowledge’ technologies in information systems • yet are expert systems knowledgeable- would we be thinking of these technologies as intelligent if the metaphor we used was not anthropomorphic (human-like or of human form) • if we though of these technologies as simply reproducing patterns where the patterns were in form of sets of rules- would we kid ourselves that there were intelligent • this is probably simply the same kind of discourse (way of speaking & way of thinking) as is illustrated in the cybernetic organisation metaphor we talked about earlier

44. ConclusionsSome Critical Issues • the facts and heuristics of expert systems for example constitute a particular ontological and epistemological position on the world • this definition of expertise ignores for example that with humans these facts are usually acquired through the senses and with the aid of a physical body rather than simply being a consequence of which rational cognition • this is not to deny the usefulness of these systems in the niche markets which they address- for many kind of computation these and related technologies are simply the only sensible way of solving certain class of computational problem

45. ConclusionsSome Critical Issues • it is to remind us that equating this kind of programmatic execution to intelligence is at best a trope • … a word or expression used in a figurative sense

46. References • Gottinger, H. W. and H. P. Weinmann (1990) Artificial Intelligence, A Tool for Industry and Management Chapters 1 and 3, pp. 9-12 & pp. 22-31 • Meyer, M. H. & K. F. Curley (1991) Putting Expert Systems Technology to Work Sloan Management Review 32 (2), pp.31 • Bowerman, R. G. and D. E. Glover (1988) Putting Expert Systems into Practice Van Nostrand Reinhold Chapter 8, pp. 287-297 & pp. 307-317 • Murray, J. T. & M. J. Murray (1988) Expert Systems in Data Processing: A Professional Guide, Pitman Publishing Co., Chapter 1

47. Feigenbaum, E. A. & P. McCorduck (1983) Fifth generation Reading, Mass.: Addison-Wesley • Stefik, M et al (1984) “The organization of expert systems: a perspective tutorial” in Hayes-Roth, F. et al (1984) Building expert systems Addison-Wesley: Redaing, Mass.