IS300 Chap11 Note

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tttran1
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IS300 Chap11 Note
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2011-12-12 18:55:27
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IS300Chap11Note
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Note for Chap 11
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  1. knowledge
    concepts, experience, insights that provide a framework for creating, evaluating, and using info
  2. wisdom
    • Collective and individual experience of applying knowledge to solve problems
    • Involves where, when, and how to apply knowledge
  3. Organizational learning
    Process in which organizations learn
  4. Knowledge management
    Set of business processes developed in an organization to create, store, transfer, and apply knowledge
  5. Knowledge management value chain stage:
    • 1.Knowledge acquisition
    • 2.Knowledge storage
    • 3.Knowledge dissemination
    • 4.Knowledge application
  6. Knowledge acquisition
    • •Documenting tacit and explicit knowledge
    • –Storing documents, reports, presentations, best practices
    • –Unstructured documents (e.g., e-mails)
    • –Developing online expert networks
    • •Creating knowledge
    • •Tracking data from TPS and external sources
  7. Knowledge storage
    • •Databases
    • •Document management systems
    • •Role of management:
    • –Support development of planned knowledge storage systems
    • –Encourage development of corporate
    • -wide schemas for indexing documents
    • –Reward employees for taking time to update and store documents properly
  8. Knowledge dissemination
    • •Portals
    • •Push e-mail reports
    • •Search engines
    • •Collaboration tools
    • •A deluge of information?
    • –Training programs, informal networks, and shared management experience help managers focus attention on important information
  9. Knowledge application
    • •To provide return on investment, organizational knowledge must become systematic part of management decision making and become situated in decision-support systems
    • –New business practices
    • –New products and services
    • –New markets
  10. Communities of practice (COPs)
    • Informal social networks of professionals and employees within and outside firm who have similar work-related activities and interests
    • Activities include education, online newsletters, sharing experiences and techniques
    • Facilitate reuse of knowledge, discussion
    • Reduce learning curves of new employees
  11. 3 major types of knowledge management systems
    • 1.Enterprise-wide knowledge management systems
    • 2.Knowledge work systems (KWS)
    • 3.Intelligent techniques
  12. Enterprise-wide knowledge management systems
    General-purpose firm-wide efforts to collect, store, distribute, and apply digital content and knowledge
  13. Knowledge work systems (KWS)
    Specialized systems built for engineers, scientists, other knowledge workers charged with discovering and creating new knowledge
  14. Intelligent techniques
    Diverse group of techniques such as data mining used for various goals: discovering knowledge, distilling knowledge, discovering optimal solutions
  15. Three major types of knowledge in enterprise
    • 1.Structured documents
    • 2.Semistructured documents
    • 3.Unstructured, tacit knowledge
  16. Enterprise content management systems
    Help capture, store, retrieve, distribute, preserve
  17. Enterprise content management systems Key problem
    Developing taxonomy
  18. Digital asset management systems
    Specialized content management systems for classifying, storing, managing unstructured digital data
  19. Knowledge network systems
    • Provide online directory of corporate experts in well-defined knowledge domains
    • Use communication technologies to make it easy for employees to find appropriate expert in a company
    • May systematize solutions developed by experts and store them in knowledge database
  20. Learning management systems
    • Provide tools for management, delivery, tracking, and assessment of various types of employee learning and training
    • Support multiple modes of learning
    • Automates selection and administration of courses
    • Assembles and delivers learning content
    • Measures learning effectiveness
  21. Knowledge work systems
    Systems for knowledge workers to help create new knowledge and integrate that knowledge into business
  22. Knowledge workers
    Researchers, designers, architects, scientists, engineers who create knowledge for the organization
  23. Three key roles of knowledge workers:
    • 1.Keeping organization current in knowledge
    • 2.Serving as internal consultants regarding their areas of expertise
    • 3.Acting as change agents, evaluating, initiating, and promoting change projects
  24. Requirements of knowledge work systems
    • Substantial computing power for graphics, complex calculations
    • Powerful graphics and analytical tools
    • Communications and document management
    • Access to external databases
    • User-friendly interfaces
    • Optimized for tasks to be performed (design engineering, financial analysis)
  25. CAD (computer-aided design)
    Creation of engineering or architectural designs
  26. Virtual reality systems
    Simulate real-life environments
  27. Augmented reality (AR) systems
    • a tech for enhancing visualization
    • provides a live direct or indirect view of a physical real-world envir whose elements are augmented by virtual comp generated imagery
  28. Virtual Reality Modeling Language (VRML)
    • a set of specifications for interactive, 3-D modelng on the World Wide Web that can organize multiple media types to put users in a simluated real-world envir
    • platform independent, operates over a desktop comp, and requires little bandwidth
  29. Investment workstations
    Streamline investment process and consolidate internal, external data for brokers, traders, portfolio managers
  30. Intelligent techniques
    Used to capture individual and collective knowledge and to extend knowledge base
  31. Knowledge discovery
    Neural networks and data mining
  32. Artificial intelligence (AI) technology
    Computer-based systems that emulate human behavior
  33. Expert systems:
    • Capture tacit knowledge in very specific and limited domain of human expertise
    • Capture knowledge of skilled employees as set of rules in software system that can be used by others in organization
    • Typically perform limited tasks that may take a few minutes or hours
  34. Knowledge base
    Set of hundreds or thousands of rules
  35. Inference engine
    Strategy used to search knowledge base
  36. Forward chaining
    Inference engine begins with information entered by user and searches knowledge base to arrive at conclusion
  37. Backward chaining
    Begins with hypothesis and asks user questions until hypothesis is confirmed or disproved
  38. Case-based reasoning (CBR)
    • Descriptions of past experiences of human specialists (cases), stored in knowledge base
    • System searches for cases with problem characteristics similar to new one, finds closest fit, and applies solutions of old case to new case
  39. Fuzzy logic systems
    • Rule-based technology that represents imprecision used in linguistic categories (e.g., “cold,” “cool”) that represent range of values
    • Describe a particular phenomenon or process linguistically and then represent that description in a small number of flexible rules
    • Provides solutions to problems requiring expertise that is difficult to represent with IF-THEN rules
  40. Neural networks
    Find patterns and relationships in massive amounts of data too complicated for humans to analyze
  41. Machine learning
    Related AI technology allowing computers to learn by extracting information using computation and statistical methods
  42. Genetic algorithms
    • Useful for finding optimal solution for specific problem by examining very large number of possible solutions for that problem
    • Conceptually based on process of evolution
  43. Hybrid AI systems
    Genetic algorithms, fuzzy logic, neural networks, and expert systems integrated into single application to take advantage of best features of each
  44. Intelligent agents
    • Work in background to carry out specific, repetitive, and predictable tasks for user, process, or application
    • Use limited built-in or learned knowledge base to accomplish tasks or make decisions on user’s behalf
  45. Agent-based modeling applications:
    • Systems of autonomous agents
    • Model behavior of consumers, stock markets, and supply chains; used to predict spread of epidemics

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