Toward decisional DNA: developing holistic set of experience knowledge structure

• Autorzy: Sanin C. , Szczerbicki E.
• Języki publikacji: EN

Abstrakty

EN

Set of Experience Knowledge Structure (SOEKS) is a structure able to collect and manage explicit knowledge of formal decision events on different forms. It was built as part of a platform for transforming information into knowledge named Knowledge Supply Chain System (KSCS). In brief, the KSCS takes information from different technologies that make formal decision events, integrates them and transforms them into knowledge represented by Sets of Experience. SOEKS is a structure that can be source and target of multiple technologies. Moreover, it comprises variables, functions, constraints and rules associated in a DNA shape allowing the construction of Decisional DNA. However, when having various dissimilar Sets of Experience as output of the same formal decision event, a renegotiation and unification of the decision has to be performed. The purpose of this paper is to show the process of renegotiating various dissimilar Sets of Experience collected from the same formal decision event.

Słowa kluczowe

EN

knowledge discovery; knowledge management; knowledge representation

• Wydawca: Wydawnictwo Politechniki Poznańskiej
• Czasopismo: Foundations of Control and Management Sciences
• Rocznik: 2008
• Tom: No. 9
• Strony: 109--122
• Opis fizyczny: Bibliogr. 26 poz.

Twórcy

autor

Sanin C.

autor

Szczerbicki E.

• Faculty of Engineering and Built Environment, University of Newcastle, University Drive, Callaghan NSW 2308, Australia,

Bibliografia

• [1]Althoff K.-D., Decker B., Hartkopf S., Jedlitschka A., Nick M. and Rech J., Experience Management: The Fraunhofer IESE Experience Factory In Industrial Conference Data Mining, Ed. Perner P., Berlin, 2001.
• [2]Awad E.M. and Ghaziri H.M., Knowledge Management, New Jersey, Prentice Hall, 2004.
• [3]Becerra-Fernandez I., Gonzalez A. and Sabherwal R., Knowledge Management, Upper Saddle River, New Jersey, Pearson Education, Inc., 2004.
• [4]Bergmann R., Experience Management, New York, Springer, 2004.
• [5]Boahene M. and Ditsa G., Conceptual Confusions in Knowledge Management and Knowledge Management Systems: Clarifications for Better KMS Development, Knowledge Management: Current Issues and Challenges, London, England, IRM Press, 2003.
• [6]Collette Y. and Siarry P., Multiobjective optimization: Principles and case studies,Berlin, Springer, 2003.
• [7]Davis R., Shrobe H. and Szolovitz P., What is a Knowledge Representation? AI Magazine, 1993, 14 (1), 17-33.
• [8]Drucker P., The Post-Capitalist Executive: Managing in a Time of Great Change, New York, Penguin, 1995.
• [9]Goldratt E.M. and Cox J., The goal, Aldershot, Hants, UK, Grower, 1986.
• [10]Hakanson and Hartung, Using Reengineering for Knowledge Based Systems, Cybernetics and Systems, 2007, 38, 799-824.
• [11]Hasan H. and Handzic M., Australian Studies in Knowledge Management,Wollongong, University of Wollongong Press, 2004.
• [12]Universal Knowledge (http://www.kpsol.com/products/universal/index.html/) (accessed 01.12.07).
• [13]Malhotra Y., From information management to knowledge management: Beyond the 'hi-tech hidebound' systems, in: Knowledge management for the information professional, ed. by K. Srikantaiah and M.E.D. Koening, Melford, NJ, Information Toady Inc., 2000, 37-61.
• [14]Minsky M., A Framework for Representing Knowledge, Memorandum 306, AI Laboratory, Massachusetts, Institute of Technology, 1974.
• [15]Mitchell H., Technology and Knowledge Management: Is Technology Just an Enabler or Does it also Add Value?, Knowledge Management: Current Issues and Challenges. London, England, IRM Press, 2003.
• [16]Sanin C. and Szczerbicki E., Knowledge supply chain system: A conceptual model, in: Knowledge management: Selected issues, ed. by A. Szuwarzynski, Gdansk, Gdansk University Press, 2004, 79-97.
• [17]Sanin C. and Szczerbicki E., Knowledge supply system: A modelling platfom for management decision-making, APIEMS’2004, Gold Coast, 2004, 17.11.1-17.11.15.
• [18]Sanin C. and Szczerbicki E., Set of experience: A knowledge structure for formal decision events, Foundations of Control and Management Sciences, 2005, 3, 95-113.
• [19]Sanin C. and Szczerbicki E., Using set of experience in the process of transforming information into knowledge, I. J. Enterprise Information Systems, 2006, 2, 45-62.
• [20]Sanin C. and Szczerbicki E., Genetic Algorithms for Decisional DNA: Solving Sets of Experience Knowledge Structure, Cybernetics and Systems: An International Journal,2007, Vol. 38,475-494.
• [21]C Sanin, C Toro, J Vaquero, E Szczerbicki, J Posada (2007) Implementing Decisional DNA in Industrial Maintenance by a Knowledge SOUP A Extension, Systems Science, 33,2,60-68.
• [22]Sanin C. and Szczerbicki E., Towards the Construction of Decisional DNA: A Set of Experience Knowledge Structure Java Class within an Ontology System, Cybernetics and Systems, 2007, 38, 859-878.
• [23]Sun Z. and Finnie G., Experience Management in Knowledge Management, KES 2005, Vol. 3681, Part 1, Eds. R. Khosla, R. Howlett and L. Jain, LNAI, Springer Verlag, Melbourne, 2005, 979-986.
• [24]Wood S. and Logan G., LIMS: A commodity item or strategic business system?, Scientific Computing, 2006, 87.
• [25]Yang and Reidsema, Information handling in a knowledge based intelligent design system, Cybernetics and Systems, 2007, 30, 549-574.
• [26]http://www.atlab.com/ (17.12.07).