proactive knowledge distribution for agile processes dr. rosina weber college of information science...

Proactive Knowledge Proactive Knowledge Distribution forDistribution forAgile ProcessesAgile Processes

Dr. Rosina WeberDr. Rosina WeberCollege of Information Science & Technology College of Information Science & Technology

Drexel University, Philadelphia, USADrexel University, Philadelphia, USA

10/Jun/03 Knowledge Management for Distributed Agile Processes

Dr. R. Weber

OutlineOutline

Knowledge Distribution and Knowledge Knowledge Distribution and Knowledge Management (KM)Management (KM)

Technological Process Oriented KMTechnological Process Oriented KM Motivation for Monitored Distribution (MD) Motivation for Monitored Distribution (MD) MD is an approach for proactive distribution MD is an approach for proactive distribution

of knowledge artifactsof knowledge artifacts Direct and Indirect MD Direct and Indirect MD MD and Agile OrganizationsMD and Agile Organizations Future WorkFuture Work

http://www.dwm.uni-hildesheim.de/homes/schaaf/WETICE03/



Dr. R. Weber

Knowledge Distribution and KMKnowledge Distribution and KM Learning from the pastLearning from the past Managing intellectual assetsManaging intellectual assets Organizations manage through Organizations manage through

communicationcommunication Organizations attain their objectives by Organizations attain their objectives by

communication and coordination as a communication and coordination as a means of learning, exchanging and means of learning, exchanging and accumulating knowledge (Atwood, 2002)accumulating knowledge (Atwood, 2002)

Knowledge distribution is an enabler of Knowledge distribution is an enabler of knowledge sharing and thus of KMknowledge sharing and thus of KM




Dr. R. Weber

Motivation for Motivation for Process Oriented KMProcess Oriented KM

KM solutions should be integrated to KM solutions should be integrated to existing processes (Aha et al., 1999)existing processes (Aha et al., 1999)

Role-based organization in agile Role-based organization in agile methodsmethods

KM solutions for agile methods for KM solutions for agile methods for software development should be software development should be incorporated in the programming incorporated in the programming language environmentlanguage environment




Dr. R. Weber

Motivation for TechnologicalMotivation for TechnologicalProcess Oriented KMProcess Oriented KM

Large and distributed organizationsLarge and distributed organizations Highly automated organizationsHighly automated organizations Whose processes are modeled in Whose processes are modeled in

enterprise wide information systemsenterprise wide information systems Real world problems require Real world problems require

leveraging powerleveraging power




Dr. R. Weber

Motivation for Monitored DistributionMotivation for Monitored Distribution

KM solutions that are technologically supported KM solutions that are technologically supported and process-orientedand process-oriented

KM solutions have to include people, KM solutions have to include people, technology and processes (Abecker, Decker, technology and processes (Abecker, Decker, Maurer, 2000). Maurer, 2000).

The impact of knowledge in resulting processes The impact of knowledge in resulting processes has to be measured (Ahn & Chang, 2002) has to be measured (Ahn & Chang, 2002)

Knowledge should be distributed Knowledge should be distributed when and when and wherewhere it is needed because this is when users it is needed because this is when users reuse itreuse it

Existing knowledge repositories are not being Existing knowledge repositories are not being usedused




Dr. R. Weber

Monitored Distribution (MD) Monitored Distribution (MD) is an organizational is an organizational

approach for the proactive approach for the proactive distribution of knowledge distribution of knowledge

artifactsartifacts




Dr. R. Weber

MD: characteristicsMD: characteristics Focuses on distribution and reuse steps in a Focuses on distribution and reuse steps in a

POKM approachPOKM approach Distribution of knowledge artifactsDistribution of knowledge artifacts

– Tightly integrated to targeted processesTightly integrated to targeted processes– Measurable knowledgeMeasurable knowledge– Measurable impactMeasurable impact

Proactive distributionProactive distribution– Shifts burden from user to the systemShifts burden from user to the system– Standalone tools place the distribution burden Standalone tools place the distribution burden

on the user discouraging sharingon the user discouraging sharing Distributes knowledge Distributes knowledge when and where when and where it is it is

needed with applicability-oriented retrievalneeded with applicability-oriented retrieval



Basic Knowledge CycleBasic Knowledge Cycle

Weber & Kaplan, 2003Weber & Kaplan, 2003


Dr. R. Weber

MD: distribution & reuseMD: distribution & reuse

Focuses on distribution and reuse Focuses on distribution and reuse steps in a POKM approachsteps in a POKM approach




Dr. R. Weber

CREATE

DISTRIBUTE

UNDERSTAND

REUSE

Organizational Processes

MD: distribution & reuseMD: distribution & reuse

Focuses on Focuses on distribution distribution and reuse and reuse steps in a steps in a POKM POKM approachapproach




Dr. R. Weber

CREATE

DISTRIBUTE

UNDERSTAND

REUSE

Processes

Monitored DistributionMonitored Distribution




Dr. R. Weber

MD: distributes knowledge artifactsMD: distributes knowledge artifacts

Distribution of knowledge artifactsDistribution of knowledge artifacts– Knowledge artifact most used today:Knowledge artifact most used today:

lessons-learnedlessons-learned




Dr. R. Weber

Some organizations that adopt Some organizations that adopt lessons-learnedlessons-learned

Weber, Aha, Becerra-Fernandez, 2001Weber, Aha, Becerra-Fernandez, 2001




Dr. R. Weber

Lessons-learned: definitionLessons-learned: definitionA lesson learned is A lesson learned is knowledgeknowledge gained by experience. gained by experience.

The experience may be positive or negative. The experience may be positive or negative.

A lesson must have an impact on operationsA lesson must have an impact on operations..

A lesson must be A lesson must be applicableapplicable by identifying a specific by identifying a specific design or decision that generates a real or assumed design or decision that generates a real or assumed

impact impact in its in its applicable taskapplicable task or process. or process.

(By Secchi et al., 1999 )(By Secchi et al., 1999 )




Dr. R. Weber

Regular ProcessRegular Process

Expectedimpact

process i

Expecteddecision




Dr. R. Weber

Lesson, process, impactLesson, process, impactim

pac

t

negative lesson 1in lesson 2n lesson 3n lesson nn

neutral no lesson no lesson no lesson no lesson

process i process i process i process i

decision ndecision 3decision 1 decision 2

positive lesson 1ip lesson 2p lesson 3p lesson np




Dr. R. Weber

Distribution of lessons-learnedDistribution of lessons-learned

Measurable knowledgeMeasurable knowledge Measurable impactMeasurable impact Tightly integrated to targeted Tightly integrated to targeted

processesprocesses




Dr. R. Weber

Lessons-learnedLessons-learned

Applicable taskApplicable task– To which task/process is it applicable?To which task/process is it applicable?

Preconditions:Preconditions:– Do conditions really match to make lesson Do conditions really match to make lesson

applicable?applicable? Lesson suggestionLesson suggestion

– What do repeat or avoidWhat do repeat or avoid RationaleRationale

– How was it learnedHow was it learned– What is the expected impactWhat is the expected impact– Why should I reuse it?Why should I reuse it?




Dr. R. Weber

Proactive distributionProactive distribution

Pushing lessons to the users shifts Pushing lessons to the users shifts burden from user to the system burden from user to the system

Standalone tools place the Standalone tools place the distribution burden on the user distribution burden on the user discouraging sharingdiscouraging sharing– Know about system’s existenceKnow about system’s existence– Skills to use itSkills to use it– Believe its usefulnessBelieve its usefulness




Dr. R. Weber

When and Where NeededWhen and Where Needed

Distributes knowledge Distributes knowledge when and when and where where it is needed with applicability-it is needed with applicability-oriented retrievaloriented retrieval

Where: in the screen of the targeted Where: in the screen of the targeted systemsystem

When: when a lesson is applicable to When: when a lesson is applicable to the current processthe current process

1515




Dr. R. Weber

Applicability-Oriented RetrievalApplicability-Oriented Retrieval

MD keeps track of a user’s context to MD keeps track of a user’s context to assess similarity between contexts assess similarity between contexts and lessons in the LL baseand lessons in the LL base

Similarity-based retrieval that gives Similarity-based retrieval that gives high weight to the applicable process high weight to the applicable process to the extent that lessons are only to the extent that lessons are only retrieved if applicable to the current retrieved if applicable to the current process process




Dr. R. Weber

Lessons as CasesLessons as Cases

Case retrieval retrieves cases with this Case retrieval retrieves cases with this structure:structure:

Advantages associated with using CBRAdvantages associated with using CBR

indexing elements

applicable task

preconditions

reuse elements

lesson suggestion

rationale




Dr. R. Weber

Proactive Distribution GoalProactive Distribution Goal

Improve Process QualityImprove Process Quality




Dr. R. Weber

Process QualityProcess Quality

Locally determinedLocally determined Depends upon target organizationDepends upon target organization Associated with organizational Associated with organizational

cultureculture VariableVariable Difficult (impossible?) to collect Difficult (impossible?) to collect

computationallycomputationally Typically requires collection from Typically requires collection from

humanshumans




Dr. R. Weber

Evaluation: Evaluation: Weber & Aha, 2003Weber & Aha, 2003

NEO plantotal duration*

casualties among evacuees

no lessons

39h50

11.48

with lessons variation

32h48

8.69

18 %

24 %

duration until medical assistance*

29h37 24h13 18 %

casualties among enemies

3.08 3.14 -2 %

9.41 6.57 30 % casualties among friendly forces




Dr. R. Weber

Direct and Indirect MDDirect and Indirect MD

MD can distribute knowledge MD can distribute knowledge artifacts directly to the userartifacts directly to the user

MD can distribute knowledge MD can distribute knowledge artifacts to an intelligent system that artifacts to an intelligent system that performs decision making and thus performs decision making and thus distributing indirectly to the userdistributing indirectly to the user

20




Dr. R. Weber

Direct MDDirect MD

processes

reu

se

capture

un

ders

tan

d

distribute

user



Indirect MDIndirect MD

processes

capture

un

ders

tan

d

distribute

user

Intelligentdecision-makingsystem

reuse


Dr. R. Weber

Example Direct MDExample Direct MD




Dr. R. Weber

WHY: The enemy might be able to infer that SOF are involved, exposing

And the user is notified of a And the user is notified of a lessonlesson

RATIONALE:

TYPE: adviceClandestine SOF should not be used aloneWHY: The enemy might be able to infer that SOF are involved, exposing them.

RATIONALE:

TYPE: adviceClandestine SOF should not be used aloneWHY: The enemy might be able to infer that SOF are involved, exposing them.

RATIONALE:

TYPE: adviceClandestine SOF should not be used aloneWHY: The enemy might be able to infer that SOF are involved, exposing



Example Indirect MDExample Indirect MD

case base

CI-tool

NN GA CS

RETR

IEV

E

RETA

IN

REUSE

REVISE

case base case

base

case base

CBKMST

MD


Dr. R. Weber

MD and Agile OrganizationsMD and Agile Organizations

Direct Monitored Distribution

Indirect Monitored DistributionAgile Organizations

25




Dr. R. Weber

ProcessProcess

PROCESS output

decision

data, information

knowledge

organization’s result

imp

act




Dr. R. Weber

Agile ProcessAgile Process

PROCESS output

decision

data, information

knowledge

NEW PROCESS

organization’s result

imp

act




Dr. R. Weber

MD can support agilityMD can support agility

If evolving parameters are If evolving parameters are distributed through lessons-learneddistributed through lessons-learned– Decisions -> lesson suggestionDecisions -> lesson suggestion– Data/info -> preconditionsData/info -> preconditions– Impact -> rationaleImpact -> rationale– Process -> applicable taskProcess -> applicable task




Dr. R. Weber

AP are no obstacle for MDAP are no obstacle for MD MD can be used as a knowledge MD can be used as a knowledge

distribution method for agile processes distribution method for agile processes because if processes change, lessons because if processes change, lessons will incorporate such changes when will incorporate such changes when captured captured

A lesson that does not find its A lesson that does not find its applicable process is no longer applicable process is no longer distributeddistributed




Dr. R. Weber

MD and Agile Org.MD and Agile Org.

If agile organizations (AO) are:If agile organizations (AO) are:– Highly automatedHighly automated– Virtually paperlessVirtually paperless

Then AO are highly appropriate for Then AO are highly appropriate for MDMD

Lessons as means to responding to Lessons as means to responding to change (evolution & adaptation)change (evolution & adaptation)




Dr. R. Weber

Requirements/limitations for MDRequirements/limitations for MD

Processes delivered computationallyProcesses delivered computationally Processes modeled computationallyProcesses modeled computationally Flexible target systems that allow integration Flexible target systems that allow integration

of MDof MD Knowledge capture that allows lessons-Knowledge capture that allows lessons-

learned be represented in LL baselearned be represented in LL base Capture of org. processes, impactCapture of org. processes, impact MaintenanceMaintenance PRIME an extension for trainingPRIME an extension for training




Dr. R. Weber

Future WorkFuture Work

Investigate the extent of the Investigate the extent of the difficulties and challenges of the difficulties and challenges of the actual integration of MD when actual integration of MD when processes are agile processes are agile

Integrate MD with methods that Integrate MD with methods that dynamic recognize agile processesdynamic recognize agile processes

Develop knowledge capture for agile Develop knowledge capture for agile processesprocesses



References (i)References (i) Abecker, A., Decker, S., Maurer, F. (2000). Abecker, A., Decker, S., Maurer, F. (2000).

Organizational Memory and Knowledge Management. Organizational Memory and Knowledge Management. Guest editorial. Information Systems Frontiers, 2, 3-4, Guest editorial. Information Systems Frontiers, 2, 3-4, 251-252.251-252.

Aha, D.W. Becerra-Fernandez, I. Maurer, F. and Muñoz-Aha, D.W. Becerra-Fernandez, I. Maurer, F. and Muñoz-Avila, H. eds., Exploring Synergies of Knowledge Avila, H. eds., Exploring Synergies of Knowledge Management and Case-Based Reasoning: Papers from Management and Case-Based Reasoning: Papers from the AAAI 1999 Workshop (Tech. Rep. WS-99-10). Menlo the AAAI 1999 Workshop (Tech. Rep. WS-99-10). Menlo Park, CA: AAAI Press, 1999.Park, CA: AAAI Press, 1999.

Ahn, J.H & Chang, S. G. (2002). Valuation of Knowledge: Ahn, J.H & Chang, S. G. (2002). Valuation of Knowledge: A Business Performance-Oriented Methodology. Proc. Of A Business Performance-Oriented Methodology. Proc. Of the 35th Annual Hawaii International Conference on the 35th Annual Hawaii International Conference on System Sciences. IEEE.System Sciences. IEEE.

Atwood, M. (2002). Organizational Memory Systems: Atwood, M. (2002). Organizational Memory Systems: Challenges For Information Technology Proceedings of Challenges For Information Technology Proceedings of the 35th Hawaii International Conference on System the 35th Hawaii International Conference on System Sciences.Sciences.

References (ii)References (ii) Secchi, P. (Ed.) (1999). Proceedings of Alerts and LL: An Secchi, P. (Ed.) (1999). Proceedings of Alerts and LL: An

Effective way to prevent failures and problems Effective way to prevent failures and problems (Technical Report WPP-167). Noordwijk, The (Technical Report WPP-167). Noordwijk, The Netherlands: ESTEC.Netherlands: ESTEC.

SELLS (2003). Proceedings of the Society for Effective SELLS (2003). Proceedings of the Society for Effective Lessons Learned Sharing (SELLS) Meetings. In U.S. Lessons Learned Sharing (SELLS) Meetings. In U.S. Department of Energy Lessons Learned Information Department of Energy Lessons Learned Information Services. [http://www.tis.eh.doe.gov/ll/proceedings/] Services. [http://www.tis.eh.doe.gov/ll/proceedings/] Last visited 05-05-2003Last visited 05-05-2003

Weber, R. & Aha, D.W. (2003) Intelligent Delivery of Weber, R. & Aha, D.W. (2003) Intelligent Delivery of Military Lessons learned. Decision Support Systems, 34, Military Lessons learned. Decision Support Systems, 34, 3, 287-304. 3, 287-304.

Weber, R. & Kaplan, R. (2003). Knowledge-based Weber, R. & Kaplan, R. (2003). Knowledge-based knowledge management. Innovations in Knowledge knowledge management. Innovations in Knowledge Engineering, Editors: Colette Faucher, Lakhmi Jain, and Engineering, Editors: Colette Faucher, Lakhmi Jain, and Nikhil Ichalkaranje. Physica-Verlag, forthcoming.Nikhil Ichalkaranje. Physica-Verlag, forthcoming.

Weber, R., Aha, D.W., Becerra-Fernandez, I. (2001). Weber, R., Aha, D.W., Becerra-Fernandez, I. (2001). Intelligent lessons learned systems. Int. J. Expert Intelligent lessons learned systems. Int. J. Expert Systems Research and Applications, 20, 1, 17–34.Systems Research and Applications, 20, 1, 17–34.


Dr. R. Weber

AcknowledgementsAcknowledgements

David W. AhaDavid W. Aha National Institute for Systems Test National Institute for Systems Test

and Productivity at USF under the and Productivity at USF under the USA Space and Naval Warfare USA Space and Naval Warfare Systems Command grant no. Systems Command grant no. N00039-02-C-3244 N00039-02-C-3244

proactive knowledge distribution for agile processes dr. rosina weber college of information science...

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