Title: Mining resource service sequences based on similarity for collaborative tasks

Authors: Haibo Li; Yingchuan Sun; Tangquan Lin

Addresses: College of Computer Science and Technology, Huaqiao University, China; Xiamen Engineering Research Center of Enterprise Interoperability and Business Intelligence, China ' College of Computer Science and Technology, Huaqiao University, China; Xiamen Engineering Research Center of Enterprise Interoperability and Business Intelligence, China ' College of Computer Science and Technology, Huaqiao University, China; Xiamen Engineering Research Center of Enterprise Interoperability and Business Intelligence, China

Abstract: To improve the efficiency of selection of resource services and collaboration among them, the business link between resource services is essential for collaborative tasks. Service flow can better describe the business relationship between resource services. Although a workflow model can describe the relationship of resource service sequences (RSSs), it describes the static relationship between resource services and does not reflect the frequency of RSSs in real business. A method of mining frequent RSSs is proposed. First, by analysing the task-relevance that exists between resource services, an equation is presented to calculate the distances between resource services. Next, for an RSS, the similarities of resource services are measured by the equation to resolve the set of frequent resource service sub-sequences. Finally, an evaluation method of frequent subsequences is proposed. The result of the experiment proves the proposed approach is valid.

Keywords: collaborative task; resource service sequence; RSS; similarity; workflow.

DOI: 10.1504/IJSTM.2019.101899

International Journal of Services Technology and Management, 2019 Vol.25 No.5/6, pp.460 - 473

Received: 16 Jun 2017
Accepted: 29 Sep 2017
Published online: 30 Aug 2019 *

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