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Automating Knowledge Flows by Integrating Workflow and Knowledge Discovery

The 2007 Winter Conference on Business Intelligence University of Utah, Salt Lake City February 22-24, 2007. Automating Knowledge Flows by Integrating Workflow and Knowledge Discovery. J. Leon Zhao, University of Arizona Surendra Sarnikar, Dakota State University. Outline.

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Automating Knowledge Flows by Integrating Workflow and Knowledge Discovery

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  1. The 2007 Winter Conference on Business Intelligence University of Utah, Salt Lake City February 22-24, 2007 Automating Knowledge Flowsby Integrating Workflow and Knowledge Discovery J. Leon Zhao, University of Arizona Surendra Sarnikar, Dakota State University

  2. Outline • What is Knowledge Flow? • What is Knowledge Flow Automation? • Dealing with the Process Uncertainty Problem • Integrating Workflow and Knowledge Discovery • Components of Knowledge Workflow Management System • Conclusions J. Leon Zhao, University of Arizona

  3. What is Knowledge Flow? • Knowledge flow is the process of knowledge spillovers among firms through sharing technical papers (Appleyard, 1996) • Knowledge flow is critical to organizational efficacy and performance under a knowledge-based view of the firm (Nissen and Levitt, 2002) • Scientific articles are the major medium that carries knowledge between scientists (Hai, 2006) For practical purposes, knowledge flow is the coordinated movement of knowledge objects among collaborators in an enterprise context. J. Leon Zhao, University of Arizona

  4. What is Knowledge Flow Automation? • Knowledge flow automation refers to system-enabled sharing of knowledge among individuals and organizations • Search engines and document recommenders provide basic mechanisms of knowledge management, but they do not constitute knowledge flow automation alone • Automation of knowledge flows should support the discovery, recommendation, and sharing of knowledge about expertise and documents in a specific enterprise environment J. Leon Zhao, University of Arizona

  5. An Individual Document Search Process Determine a search objective Choose a repository Yes New repository? No No Perform a search or combine searches Complete? Yes • Some search engines keep track of search histories and • allow combining previous searches, but some do not! • Little known support on inter-repository buffering! J. Leon Zhao, University of Arizona

  6. Accenture Knowledge Management Portal Source: http://www.microsoft.com/technet J. Leon Zhao, University of Arizona

  7. A Knowledge Flow Process Determine a knowledge request Consult experts? Yes Consult experts Yes New experts? No No No Conduct searches Complete? Yes • Each activity above is a sub-process • Many points of uncertainty exist in the process • Need to store and reuse the results from each cycle J. Leon Zhao, University of Arizona

  8. Challenges in Knowledge Flow Automation • Much uncertainty exists in the knowledge flow process above such as where to find experts, how to trust the experts, what repositories to use first, how to reuse previous search results, and how to balance knowledge sharing promotion and knowledge overload • Many knowledge flow patterns exist such as system-driven document sharing, enterprise sharing of search results, automatic search updates • There are currently no mature framework of knowledge flow automation that deal with these challenges head on Research is needed on new techniques for knowledge flow automaton! J. Leon Zhao, University of Arizona

  9. Research Ideas for Knowledge Flow Automation • Need to extend the boundary of system support beyond what typical search engines can do • Need to extend knowledge management systems to knowledge flow management systems • Need to understand knowledge flow patterns and their features in a typical enterprise environment • Need to develop new tools that will deal with knowledge flows with significant uncertainty How about integrate workflow and knowledge discovery? J. Leon Zhao, University of Arizona

  10. Simple Knowledge Flow Patterns Entity A Entity B Entity A Entity B Single-link Flow Pattern Request-Response Flow Pattern Entity B Entity A Entity A Entity B Matching Pattern Sharing Flow Pattern Document Flow Control Flow J. Leon Zhao, University of Arizona

  11. Assembled Knowledge Flow Patterns Repository Domain Expert Team Leader Retrieval Pattern Team Members Request-Response Pattern Relationship-based Sharing Document Flow Control Flow J. Leon Zhao, University of Arizona

  12. Request-Response Pattern System User Send Request Generate Documents Receive Document Send Documents J. Leon Zhao, University of Arizona

  13. Knowledge Flow Pattern for Document Sharing System Entity B Entity A Does Entity B has it already? Yes Receive Document No Send Document Receive Document J. Leon Zhao, University of Arizona

  14. Expert-Generated Knowledge Flow Pattern Identify Experts Select Experts Send Requests Summarize Filter & Aggregate Receive Responses J. Leon Zhao, University of Arizona

  15. Message Flow Sequence Diagram User Workflow Engine IR Engine Expert A Expert B sendRequest() findExperts() sendExperts() sendRequest() sendResponse() sendResponse() sendAllMsgs() aggregate() summarize() sendResponse() IR = Intelligent Recommendation J. Leon Zhao, University of Arizona

  16. Partial BPEL Code <process name=“request-response”> <sequence> <receive partnerLink = “User” operation = “requestForExpertKnowledge” variable = “knowledgeRequirementQuery, senderInfo”> </receive> <invoke partnerLink=“IR-Services” operation=“requestExperts” inputVariable=“knowledgeRequirementQuery” outputVariable=“ExpertList” </invoke> <invoke partnerLink=“IR-Services” operation=“requestExpertAvailability” inputVariable=“ExpertList, senderInfo” outputVariable=“AvailableExpertList” </invoke> <invoke partnerLink=“MailServer” operation=“sendRequests” inputVariable=“knowledgeRequirementQuery, AvailableExpertList” </invoke> J. Leon Zhao, University of Arizona

  17. Partial BPEL Code (Cont.) <flow> <receive partnerLink = “Expert” operation = “receiveExpertKnowledge” variable = “knowledgeDocument”> </receive> <sequence> <wait until=“deadline-expression”> <invoke partnerLink=“IR-Services” operation=“requestAggregationSummarization” inputVariable=“knowledgeDocuments” outputVariable=“summaryResponses” </invoke> <reply partnerLink = “User” operation = “sendExpertKnowledge” variable = “summaryResponses”> </reply> </sequence> </flow> </process> J. Leon Zhao, University of Arizona

  18. Conventional Workflow Architecture J. Leon Zhao, University of Arizona

  19. Limitations of Conventional Workflows Systems • In traditional workflows, control flow and data flow are predetermined while they are ad hoc in knowledge flows • In knowledge workflow, routing is based on retrieval based matching criteria as opposed to role-resolution • Knowledge workflows are instance based and their models can change during execution J. Leon Zhao, University of Arizona

  20. A New Paradigm for Knowledge Flow Knowledge Workflow Modeler Intelligent Workflow Engine Knowledge Workflow Management System Intelligent Expertise Locator Intelligent Doc Recommender Users and Groups J. Leon Zhao, University of Arizona

  21. Knowledge Workflow • Knowledge flow can be automated using a new type of workflow called knowledge workflow • Knowledge workflows integrate functions from retrieval systems and workflow systems for knowledge flow automation • Knowledge workflow models can be built by assembling knowledge workflow patterns J. Leon Zhao, University of Arizona

  22. Extended State Space for Knowledge Workflow J. Leon Zhao, University of Arizona

  23. Related Work J. Leon Zhao, University of Arizona

  24. Contributions • Proposed a new paradigm for automating flow of knowledge • Proposed an architecture for knowledge workflow management system that integrates workflow and retrieval technologies • Developed mathematical representations for all components of the architecture J. Leon Zhao, University of Arizona

  25. Issues for Future Research • Verification of the completeness and correctness of the state-machine workflow representations that are generated from a knowledge workflow model • Investigation of conflicts, exceptions, and other issues related to the dynamic extension of knowledge workflow models J. Leon Zhao, University of Arizona

  26. Issues for Future Research (Cont.) • Identification of knowledge workflow patterns including basic patterns and their assembly towards more complex knowledge workflows • Investigation of access control issues to ensure security of the organizational knowledge, and • Prevention of information overload by controlling the intensity of knowledge flows at the user and system levels. J. Leon Zhao, University of Arizona

  27. Thank you! Any questions? J. Leon Zhao, University of Arizona

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