Title: OBWD: an ontology and Bayesian network-based workflow design platform

Authors: Chao Dong; Chongchong Zhao

Addresses: Hebei University of Science and Technology, 26 Yuxiang St, Yuhua, Shijiazhuang, Hebei, China ' Hebei University of Science and Technology, 26 Yuxiang St, Yuhua, Shijiazhuang, Hebei, China

Abstract: Workflow management provides a great convenience for the cooperation between different roles in modern industry and business. The task reuse and design automation are challenges of workflow management currently. In this paper, an ontology SDWMO is constructed for workflow resources integration and task request release. An algorithm DOMDM is proposed to achieve the conversion of the data from traditional workflow data base to SDWMO ontology. In order to provide workflow templates for designers, we extract statistic-oriented cases from the workflow database. Based on these cases a Bayesian network is established for workflow template recommendation. We have designed OBWD platform to implement the above methods. The experimental data indicates that OBWD is statistically effective and saves a lot of time for workflow designers. Currently, OBWD has been used in space debris mitigation domain for workflow management. Moreover, our methodology can also be applied in many other domains in the future.

Keywords: workflow; ontology; Bayesian network; JBPM.

DOI: 10.1504/IJITM.2020.10027421

International Journal of Information Technology and Management, 2020 Vol.19 No.2/3, pp.240 - 258

Received: 29 May 2017
Accepted: 12 Feb 2018

Published online: 06 Mar 2020 *

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