EXPERTISE SEARCH IN UNSTRUCTURED DATA IN ECM USING S-BPM APPROACH

This article describes the application of currently most promising methods of (1) network (graph) theory, (2) content analysis and (3) subject-oriented approach to business process modeling for creating and automation of innovative process and therefore for maximization of ROI (return on investments) in intellectual and social capital of enterprises. Described approach delivers opportunities for unstructured information utilization in order to increase efficiency of innovation activity in organizations. As a result, virtual community with a multiple content centers is created presenting a prototype of intellectual neural network with distributed association nodes. In a course of development, instant full-text indexation takes place and taxonomic picture of different branches for such community is formed. In due course system gathers the statistics and builds-up maps of intercommunication with priority allocation of most discussed topics. A group of predetermined experts begins discussion on development prospects of this or that subject afterwards. The strategic map of investments into innovative development that can be offered to group of investors for competitive investments eventually turns out. In this process all steps except final (gathering of experts) are human nondependant, what increase efficiency of this process in general.

[1]  Yulia Stavenko,et al.  An Approach to Agility in Enterprise Innovation , 2011, S-BPM ONE.

[2]  Roberto M. Fernandez,et al.  Social Capital at Work: Networks and Employment at a Phone Center , 2000, American Journal of Sociology.

[3]  Jyun-Cheng Wang,et al.  An Automated Tool for Managing Interactions in Virtual Communities-Using Social Network Analysis Approach , 2004, J. Organ. Comput. Electron. Commer..

[4]  Juan-Zi Li,et al.  Expert Finding in a Social Network , 2007, DASFAA.

[5]  Katherine J. Stewart,et al.  Friends in High Places: The Effects of Social Networks on Discrimination in Salary Negotiations , 2000 .

[6]  Dennis F. Galletta,et al.  Individual Centrality and Performance in Virtual R&D Groups: An Empirical Study , 2003, Manag. Sci..

[7]  Stanley Wasserman,et al.  Social Network Analysis: Methods and Applications , 1994, Structural analysis in the social sciences.

[8]  Noah E. Friedkin,et al.  Social Influence Network Theory: A Sociological Examination of Small Group Dynamics , 2011 .

[9]  L. A. Streeter,et al.  An expert/expert-locating system based on automatic representation of semantic structure , 1988, [1988] Proceedings. The Fourth Conference on Artificial Intelligence Applications.

[10]  Steffen Staab,et al.  Human Language Technologies for Knowledge Management , 2001, IEEE Intell. Syst..

[11]  P. Adler,et al.  Social Capital: Prospects for a New Concept , 2002 .

[12]  Ching-Yung Lin,et al.  ExpertiseNet: Relational and Evolutionary Expert Modeling , 2005, User Modeling.

[13]  Martin G. Everett,et al.  A Graph-theoretic perspective on centrality , 2006, Soc. Networks.

[14]  P. Bonacich Factoring and weighting approaches to status scores and clique identification , 1972 .

[15]  Paul Thompson,et al.  An Inductive Search System: Theory, Design, and Implementation , 1986, IEEE Transactions on Systems, Man, and Cybernetics.

[16]  Mark S. Granovetter,et al.  Getting a Job: A Study of Contacts and Careers. , 1976 .

[17]  Gordon I. McCalla,et al.  User Modelling in I-Help: What, Why, When and How , 2001, User Modeling.