Using Graph Aggregation for Service Interaction Message Correlation

Discovering the behavior of services and their interactions in an enterprise requires the ability to correlate service interaction messages into process instances. The service interaction logic (or process model) is then discovered from the set of process instances that are the result of a given way of correlating messages. However, sometimes, the Correlation Conditions (CC) allowing to identify correlations of messages from a service interaction log are not known. In such cases, and with a large number of message's correlator attributes, we are facing a large space of possible ways messages may be correlated which makes identifying process instances difficult. In this paper, we propose an approach based on message indexation and aggregation to generate a size-efficient Aggregated Correlation Graph (ACG) that exhibits all the ways messages correlate in a service interaction log not only for disparate pairs of messages but also for sequences of messages corresponding to process instances. Adapted filtering techniques based on user defined heuristics are then applied on such a graph to help the analysts efficiently identify the most frequently executed processes from their sequences of CCs. The approach has been implemented and experiments show its effectiveness to identify relevant sequences of CCs from large service interaction logs.