Modeling, Discovering, and Visualizing Workflow Performer-Role Affiliation Networking Knowledge

• Published in: KSII transactions on Internet and information systems 2014, 8(2), , pp.691-708
• Main Authors: 김학성, 안현, Kwanghoon Pio Kim
• Format: Article
• Language: English
• Subjects: 컴퓨터학
• ISSN: 1976-7277; 1976-7277
• DOI: 10.3837/tiis.2014.02.0022

This paper formalizes a special type of social networking knowledge, which is called “workflow performer-role affiliation networking knowledge.” A workflow model specifies execution sequences of the associated activities and their affiliated relationships with roles, performers, invoked-applications, and relevant data. In Particular, these affiliated relationships exhibit a stream of organizational work-sharing knowledge and utilize business process intelligence to explore resources allotting and planning knowledge concealed in the corresponding workflow model. In this paper, we particularly focus on the performer-role affiliation relationships and their implications as organizational and business process intelligence in workflow-driven organizations. We elaborate a series of theoretical formalisms and practical implementation for modeling, discovering, and visualizing workflow performer-role affiliation networking knowledge, and practical details as workflow performer-role affiliation knowledge representation, discovery, and visualization techniques. These theoretical concepts and practical algorithms are based upon information control net methodology for formally describing workflow models, and the affiliated knowledge eventually represents the various degrees of involvements and participations between a group of performers and a group of roles in a corresponding workflow model. Finally, we summarily describe the implications of the proposed affiliation networking knowledge as business process intelligence, and how worthwhile it is in discovering and visualizing the knowledge in workflow-driven organizations and enterprises that produce massively parallel interactions and large-scaled operational data collections through deploying and enacting massively parallel and large-scale workflow models. KCI Citation Count: 6