Semantic Process Fragments Matching to Assist the Development of Process Variants

With the increasing adoption of Cloud Computing, Business Process as a Service (BPaaS) has recently emerged as the delivery of business process outsourcing services that are sourced from the cloud and constructed for multitenancy. BPaaS providers may look for available business processes from other cloud providers to improve their processes. However, today's BPaaS solutions lack an explicit and formal semantics which prevent an easy and dynamic interoperability between different cloud providers. In this paper, we propose firstly to semantically populate a shared knowledge base of process models deployed by BPaaS providers. Therefore, using this knowledge base we aim at finding for selected positions in a business process suitable process fragments for recommendation to assist process variant modeling. To do so, we define a process fragment as a neighborhood context graph, which captures order constraints between tasks and their neighbors. Thus, we compute similarity between fragments to select the most similar ones for recommendation. As a proof of concept, we provide a tool that allows process designers to retrieve similar process fragments that can be used to design new process variants. We also performed experiments on large public datasets and experimental results show that our approach is feasible and efficient.