Message Correlation for Conversation Reconstruction in Service Interaction Logs

The problem of understanding the behavior of business processes and of services is rapidly becoming a priority in medium and large companies. To this end, recently, analysis tools as well as variations of data mining techniques have been applied to process and service execution logs to perform OLAP-style analysis and to discover behavioral (process and protocol) models out of execution data. All these approaches are based on one key assumption: events describing executions and stored in process and service logs include identifiers that allow associating each event to the process or service execution they belong to (e.g., can correlate all events related to the processing of a certain purchase order or to the hiring of a given employee). In reality, however, such information rarely exists. In this paper, we present a framework for discovering correlations among messages in service logs. We characterize the problem of message correlation and propose novel algorithms and techniques based on heuristics on the characteristics of conversations and of message attributes that can act as identifier for such conversations. As we will show, there is no right or wrong way to correlate messages, and such correlation is necessarily subjective. To account for this subjectiveness, we propose an approach where algorithms suggest candidate correlators, provide measures that help users understand the implications of choosing a given correlators, and organize candidate correlators in such a way to facilitate visual exploration. The approach has been implemented and experimental results show its viability and scalability on large synthetic and real-world datasets. We believe that message correlation is a very important and challenging area of research that will witness many contributions in the near future due to the pressing industry needs for process and service execution analysis.