1. Proactive Knowledge Distribution for Agile Processes Dr. Rosina Weber College of Information Science & Technology Drexel University, Philadelphia, USA

2. 10/Jun/03Knowledge Management for Distributed Agile Processes Dr. R. Weber Outline Knowledge Distribution and Knowledge Management (KM) Knowledge Distribution and Knowledge Management (KM) Technological Process Oriented KM Technological Process Oriented KM Motivation for Monitored Distribution (MD) Motivation for Monitored Distribution (MD) MD is an approach for proactive distribution of knowledge artifacts MD is an approach for proactive distribution of knowledge artifacts Direct and Indirect MD Direct and Indirect MD MD and Agile Organizations MD and Agile Organizations Future Work Future Work

3. 10/Jun/03Knowledge Management for Distributed Agile Processes Dr. R. Weber Knowledge Distribution and KM Learning from the past Learning from the past Managing intellectual assets Managing intellectual assets Organizations manage through communication Organizations manage through communication Organizations attain their objectives by communication and coordination as a means of learning, exchanging and accumulating knowledge (Atwood, 2002) Organizations attain their objectives by communication and coordination as a means of learning, exchanging and accumulating knowledge (Atwood, 2002) Knowledge distribution is an enabler of knowledge sharing and thus of KM Knowledge distribution is an enabler of knowledge sharing and thus of KM

4. 10/Jun/03Knowledge Management for Distributed Agile Processes Dr. R. Weber Motivation for Process Oriented KM KM solutions should be integrated to existing processes (Aha et al., 1999) KM solutions should be integrated to existing processes (Aha et al., 1999) Role-based organization in agile methods Role-based organization in agile methods KM solutions for agile methods for software development should be incorporated in the programming language environment KM solutions for agile methods for software development should be incorporated in the programming language environment

5. 10/Jun/03Knowledge Management for Distributed Agile Processes Dr. R. Weber Motivation for Technological Process Oriented KM Large and distributed organizations Large and distributed organizations Highly automated organizations Highly automated organizations Whose processes are modeled in enterprise wide information systems Whose processes are modeled in enterprise wide information systems Real world problems require leveraging power Real world problems require leveraging power

6. 10/Jun/03Knowledge Management for Distributed Agile Processes Dr. R. Weber Motivation for Monitored Distribution KM solutions that are technologically supported and process-oriented KM solutions that are technologically supported and process-oriented KM solutions have to include people, technology and processes (Abecker, Decker, Maurer, 2000). KM solutions have to include people, technology and processes (Abecker, Decker, Maurer, 2000). The impact of knowledge in resulting processes has to be measured (Ahn & Chang, 2002) The impact of knowledge in resulting processes has to be measured (Ahn & Chang, 2002) Knowledge should be distributed when and where it is needed because this is when users reuse it Knowledge should be distributed when and where it is needed because this is when users reuse it Existing knowledge repositories are not being used Existing knowledge repositories are not being used

7. 10/Jun/03Knowledge Management for Distributed Agile Processes Dr. R. Weber Monitored Distribution (MD) is an organizational approach for the proactive distribution of knowledge artifacts

8. 10/Jun/03Knowledge Management for Distributed Agile Processes Dr. R. Weber MD: characteristics Focuses on distribution and reuse steps in a POKM approach Focuses on distribution and reuse steps in a POKM approach Distribution of knowledge artifacts Distribution of knowledge artifacts –Tightly integrated to targeted processes –Measurable knowledge –Measurable impact Proactive distribution Proactive distribution –Shifts burden from user to the system –Standalone tools place the distribution burden on the user discouraging sharing Distributes knowledge when and where it is needed with applicability-oriented retrieval Distributes knowledge when and where it is needed with applicability-oriented retrieval

9. Basic Knowledge Cycle Weber & Kaplan, 2003

10. 10/Jun/03Knowledge Management for Distributed Agile Processes Dr. R. Weber MD: distribution & reuse Focuses on distribution and reuse steps in a POKM approach Focuses on distribution and reuse steps in a POKM approach

11. 10/Jun/03Knowledge Management for Distributed Agile Processes Dr. R. Weber Organizational Processes MD: distribution & reuse Focuses on distribution and reuse steps in a POKM approach Focuses on distribution and reuse steps in a POKM approach

12. 10/Jun/03Knowledge Management for Distributed Agile Processes Dr. R. Weber Processes Monitored Distribution

13. 10/Jun/03Knowledge Management for Distributed Agile Processes Dr. R. Weber MD: distributes knowledge artifacts Distribution of knowledge artifacts Distribution of knowledge artifacts –Knowledge artifact most used today: lessons-learned

14. 10/Jun/03Knowledge Management for Distributed Agile Processes Dr. R. Weber Some organizations that adopt lessons-learned Weber, Aha, Becerra-Fernandez, 2001

15. 10/Jun/03Knowledge Management for Distributed Agile Processes Dr. R. Weber Lessons-learned: definition A lesson learned is knowledge gained by experience. The experience may be positive or negative. A lesson must have an impact on operations. A lesson must be applicable by identifying a specific design or decision that generates a real or assumed impact in its applicable task or process. (By Secchi et al., 1999 ) (By Secchi et al., 1999 )

16. 10/Jun/03Knowledge Management for Distributed Agile Processes Dr. R. Weber Regular Process Expected impact process i Expected decision

17. 10/Jun/03Knowledge Management for Distributed Agile Processes Dr. R. Weber Lesson, process, impact impact negative lesson 1inlesson 2nlesson 3nlesson nn neutral no lesson process i decision ndecision 3decision 1decision 2 positive lesson 1iplesson 2plesson 3plesson np

18. 10/Jun/03Knowledge Management for Distributed Agile Processes Dr. R. Weber Distribution of lessons-learned Measurable knowledge Measurable knowledge Measurable impact Measurable impact Tightly integrated to targeted processes Tightly integrated to targeted processes

19. 10/Jun/03Knowledge Management for Distributed Agile Processes Dr. R. Weber Lessons-learned Applicable task Applicable task –To which task/process is it applicable? Preconditions: Preconditions: –Do conditions really match to make lesson applicable? Lesson suggestion Lesson suggestion –What do repeat or avoid Rationale Rationale –How was it learned –What is the expected impact –Why should I reuse it?

20. 10/Jun/03Knowledge Management for Distributed Agile Processes Dr. R. Weber Proactive distribution Pushing lessons to the users shifts burden from user to the system Pushing lessons to the users shifts burden from user to the system Standalone tools place the distribution burden on the user discouraging sharing Standalone tools place the distribution burden on the user discouraging sharing –Know about system’s existence –Skills to use it –Believe its usefulness

21. 10/Jun/03Knowledge Management for Distributed Agile Processes Dr. R. Weber When and Where Needed Distributes knowledge when and where it is needed with applicability- oriented retrieval Distributes knowledge when and where it is needed with applicability- oriented retrieval Where: in the screen of the targeted system Where: in the screen of the targeted system When: when a lesson is applicable to the current process When: when a lesson is applicable to the current process 15 15

22. 10/Jun/03Knowledge Management for Distributed Agile Processes Dr. R. Weber Applicability-Oriented Retrieval MD keeps track of a user’s context to assess similarity between contexts and lessons in the LL base MD keeps track of a user’s context to assess similarity between contexts and lessons in the LL base Similarity-based retrieval that gives high weight to the applicable process to the extent that lessons are only retrieved if applicable to the current process Similarity-based retrieval that gives high weight to the applicable process to the extent that lessons are only retrieved if applicable to the current process

23. 10/Jun/03Knowledge Management for Distributed Agile Processes Dr. R. Weber Lessons as Cases Case retrieval retrieves cases with this structure: Case retrieval retrieves cases with this structure: Advantages associated with using CBR Advantages associated with using CBR indexing elements applicable task preconditions reuse elements lesson suggestion rationale

24. 10/Jun/03Knowledge Management for Distributed Agile Processes Dr. R. Weber Proactive Distribution Goal Improve Process Quality

25. 10/Jun/03Knowledge Management for Distributed Agile Processes Dr. R. Weber Process Quality Locally determined Locally determined Depends upon target organization Depends upon target organization Associated with organizational culture Associated with organizational culture Variable Variable Difficult (impossible?) to collect computationally Difficult (impossible?) to collect computationally Typically requires collection from humans Typically requires collection from humans

26. 10/Jun/03Knowledge Management for Distributed Agile Processes Dr. R. Weber Evaluation: Weber & Aha, 2003 NEO plan total duration* casualties among evacuees no lessons 39h50 11.48 with lessonsvariation 32h48 8.69 18 % 24 % duration until medical assistance* 29h37 24h13 18 % casualties among enemies 3.083.14 -2 % 9.41 6.57 30 % casualties among friendly forces

27. 10/Jun/03Knowledge Management for Distributed Agile Processes Dr. R. Weber Direct and Indirect MD MD can distribute knowledge artifacts directly to the user MD can distribute knowledge artifacts directly to the user MD can distribute knowledge artifacts to an intelligent system that performs decision making and thus distributing indirectly to the user MD can distribute knowledge artifacts to an intelligent system that performs decision making and thus distributing indirectly to the user 20

28. 10/Jun/03Knowledge Management for Distributed Agile Processes Dr. R. Weber Direct MD processes reuse capture understand distribute user

29. Indirect MD processes capture understand distribute user Intelligent decision- making system reuse

30. 10/Jun/03Knowledge Management for Distributed Agile Processes Dr. R. Weber Example Direct MD

31. 10/Jun/03Knowledge Management for Distributed Agile Processes Dr. R. Weber WHY : The enemy might be able to infer that SOF are involved, exposing And the user is notified of a lesson RATIONALE: TYPE: advice Clandestine SOF should not be used alone WHY : The enemy might be able to infer that SOF are involved, exposing them. RATIONALE: TYPE: advice Clandestine SOF should not be used alone WHY : The enemy might be able to infer that SOF are involved, exposing them. RATIONALE: TYPE: advice Clandestine SOF should not be used alone WHY : The enemy might be able to infer that SOF are involved, exposing

32. Example Indirect MD case base CI-tool NNGACS RETRIEVE RETAIN REUSE REVISE case base case base case base CBKMST MD

33. 10/Jun/03Knowledge Management for Distributed Agile Processes Dr. R. Weber MD and Agile Organizations 25

34. 10/Jun/03Knowledge Management for Distributed Agile Processes Dr. R. Weber Process PROCESS output decision data, information knowledge organization’s result impact

35. 10/Jun/03Knowledge Management for Distributed Agile Processes Dr. R. Weber Agile Process PROCESS output decision data, information knowledge NEW PROCESS organization’s result impact

36. 10/Jun/03Knowledge Management for Distributed Agile Processes Dr. R. Weber MD can support agility If evolving parameters are distributed through lessons-learned If evolving parameters are distributed through lessons-learned –Decisions -> lesson suggestion –Data/info -> preconditions –Impact -> rationale –Process -> applicable task

37. 10/Jun/03Knowledge Management for Distributed Agile Processes Dr. R. Weber AP are no obstacle for MD MD can be used as a knowledge distribution method for agile processes because if processes change, lessons will incorporate such changes when captured MD can be used as a knowledge distribution method for agile processes because if processes change, lessons will incorporate such changes when captured A lesson that does not find its applicable process is no longer distributed A lesson that does not find its applicable process is no longer distributed

38. 10/Jun/03Knowledge Management for Distributed Agile Processes Dr. R. Weber MD and Agile Org. If agile organizations (AO) are: If agile organizations (AO) are: –Highly automated –Virtually paperless Then AO are highly appropriate for MD Then AO are highly appropriate for MD Lessons as means to responding to change (evolution & adaptation) Lessons as means to responding to change (evolution & adaptation)

39. 10/Jun/03Knowledge Management for Distributed Agile Processes Dr. R. Weber Requirements/limitations for MD Processes delivered computationally Processes delivered computationally Processes modeled computationally Processes modeled computationally Flexible target systems that allow integration of MD Flexible target systems that allow integration of MD Knowledge capture that allows lessons- learned be represented in LL base Knowledge capture that allows lessons- learned be represented in LL base Capture of org. processes, impact Capture of org. processes, impact Maintenance Maintenance PRIME an extension for training PRIME an extension for training

40. 10/Jun/03Knowledge Management for Distributed Agile Processes Dr. R. Weber Future Work Investigate the extent of the difficulties and challenges of the actual integration of MD when processes are agile Investigate the extent of the difficulties and challenges of the actual integration of MD when processes are agile Integrate MD with methods that dynamic recognize agile processes Integrate MD with methods that dynamic recognize agile processes Develop knowledge capture for agile processes Develop knowledge capture for agile processes

41. References (i) Abecker, A., Decker, S., Maurer, F. (2000). Organizational Memory and Knowledge Management. Guest editorial. Information Systems Frontiers, 2, 3-4, 251-252. Abecker, A., Decker, S., Maurer, F. (2000). Organizational Memory and Knowledge Management. Guest editorial. Information Systems Frontiers, 2, 3-4, 251-252. Aha, D.W. Becerra-Fernandez, I. Maurer, F. and Muñoz-Avila, H. eds., Exploring Synergies of Knowledge Management and Case-Based Reasoning: Papers from the AAAI 1999 Workshop (Tech. Rep. WS- 99-10). Menlo Park, CA: AAAI Press, 1999. Aha, D.W. Becerra-Fernandez, I. Maurer, F. and Muñoz-Avila, H. eds., Exploring Synergies of Knowledge Management and Case-Based Reasoning: Papers from the AAAI 1999 Workshop (Tech. Rep. WS- 99-10). Menlo Park, CA: AAAI Press, 1999. Ahn, J.H & Chang, S. G. (2002). Valuation of Knowledge: A Business Performance-Oriented Methodology. Proc. Of the 35th Annual Hawaii International Conference on System Sciences. IEEE. Ahn, J.H & Chang, S. G. (2002). Valuation of Knowledge: A Business Performance-Oriented Methodology. Proc. Of the 35th Annual Hawaii International Conference on System Sciences. IEEE. Atwood, M. (2002). Organizational Memory Systems: Challenges For Information Technology Proceedings of the 35th Hawaii International Conference on System Sciences. Atwood, M. (2002). Organizational Memory Systems: Challenges For Information Technology Proceedings of the 35th Hawaii International Conference on System Sciences.

42. References (ii) Secchi, P. (Ed.) (1999). Proceedings of Alerts and LL: An Effective way to prevent failures and problems (Technical Report WPP-167). Noordwijk, The Netherlands: ESTEC. Secchi, P. (Ed.) (1999). Proceedings of Alerts and LL: An Effective way to prevent failures and problems (Technical Report WPP-167). Noordwijk, The Netherlands: ESTEC. SELLS (2003). Proceedings of the Society for Effective Lessons Learned Sharing (SELLS) Meetings. In U.S. Department of Energy Lessons Learned Information Services. [http://www.tis.eh.doe.gov/ll/proceedings/] Last visited 05-05-2003 SELLS (2003). Proceedings of the Society for Effective Lessons Learned Sharing (SELLS) Meetings. In U.S. Department of Energy Lessons Learned Information Services. [http://www.tis.eh.doe.gov/ll/proceedings/] Last visited 05-05-2003 Weber, R. & Aha, D.W. (2003) Intelligent Delivery of Military Lessons learned. Decision Support Systems, 34, 3, 287-304. Weber, R. & Aha, D.W. (2003) Intelligent Delivery of Military Lessons learned. Decision Support Systems, 34, 3, 287-304. Weber, R. & Kaplan, R. (2003). Knowledge-based knowledge management. Innovations in Knowledge Engineering, Editors: Colette Faucher, Lakhmi Jain, and Nikhil Ichalkaranje. Physica-Verlag, forthcoming. Weber, R. & Kaplan, R. (2003). Knowledge-based knowledge management. Innovations in Knowledge Engineering, Editors: Colette Faucher, Lakhmi Jain, and Nikhil Ichalkaranje. Physica-Verlag, forthcoming. Weber, R., Aha, D.W., Becerra-Fernandez, I. (2001). Intelligent lessons learned systems. Int. J. Expert Systems Research and Applications, 20, 1, 17–34. Weber, R., Aha, D.W., Becerra-Fernandez, I. (2001). Intelligent lessons learned systems. Int. J. Expert Systems Research and Applications, 20, 1, 17–34.

43. 10/Jun/03Knowledge Management for Distributed Agile Processes Dr. R. Weber Acknowledgements David W. Aha David W. Aha National Institute for Systems Test and Productivity at USF under the USA Space and Naval Warfare Systems Command grant no. N00039-02-C-3244 National Institute for Systems Test and Productivity at USF under the USA Space and Naval Warfare Systems Command grant no. N00039-02-C-3244