Memetic Algorithms for Mining Change Logs in Process Choreographies

Abstract

The propagation and management of changes in process choreographies has been recently addressed as crucial challenge by several approaches. A change rarely confines itself to a single change, but triggers other changes in different partner processes. Specifically, it has been stated that with an increasing number of partner processes, the risk for transitive propagations and costly negotiations increases as well. In this context, utilizing past change events to learn and analyze the propagation behavior over process choreographies will help avoiding significant costs related to unsuccessful propagations and negotiation failures, of further change requests. This paper aims at the posteriori analysis of change requests in process choreographies by the provision of mining algorithms based on change logs. In particular, a novel implementation of the memetic mining algorithm for change logs, with the appropriate heuristics is presented. The results of the memetic mining algorithm are compared with the results of the actual propagation of the analyzed change events.

Impact analysis and change propagation in service-oriented enterprises: A systematic review

“…In this context, MSR techniques have been applied for process model repositories [12], Web service ecosystems [28] and process choreographies [16]. Several process model repositories utilize traditional version control systems (Subversion, CVS) for versioning.…”

“…Inter-process change management has received significant attention from the research community. Existing research provides a sound basis for change propagation [13,16,36,43,51,81], dependency analysis [7,8,10,12,30,31,36,45] and traceability analysis [22,25,71] for business process level changes. Most CIA approaches for inter-process impact analysis utilize graphbased or formal specification approaches for dependency analysis.…”

“…In business process execution logs, each trace provides a sequence of events related to a specific execution case [16]. Service execution data is crucial as it can reveal message exchange correlations among services.…”