Efficiently querying large process model repositories in smart city cloud workflow systems based on quantitative ordering relations

Abstract

With the development of cloud computing and the rise of smart city, smart city cloud service platforms are widely accepted by more and more enterprises and individuals. The underlying cloud workflow systems accumulate large numbers of business process models. How to achieve efficiently querying large process model repositories in smart city cloud workflow systems is challenging. To this end, this paper proposes an improved two-phase retrieval approach for querying large process model repositories in smart city cloud workflow systems. In the filtering stage, the index based on quantitative ordering relation with time and probability constraints (namely ORTP_index) is adopted to greatly reduce the number of candidate models in large process model repositories. In the refining phase, a process behavior similarity computing algorithm based on quantitative ordering relations is proposed to refine the candidate model set. Experiments illustrate that our proposal can significantly improve the query efficiency of large process model repositories in smart city cloud workflow systems based on behavior.