discovering event correlation rules for semi-structured business processes
In this paper we describe an algorithm to discover event correlation rules from arbitrary data sources. Correlation rules can be useful for determining relationships between events in order to isolate instances of a running business process for the purposes of monitoring, discovery and other applications. We have implemented our algorithm and validate our approach on events generated by a simulator that implements a real-world inspired export compliance regulations scenario consisting of 24 activities and corresponding event types. This simulated scenario involves a wide range of heterogeneous systems (e.g. Order Management, Document Management, E-Mail, and Export Violation Detection Services) as well as workflow-supported human-driven interactions (Process Management System). Experimental results demonstrate that our algorithm achieves a high level of accuracy in the detection of correlation rules. This paper confirms that our algorithm is a step towards semi-automating the task of detecting correlations. We also demonstrate how correlation rules discovered by our algorithm can be used to create aggregation nodes that allow more efficient querying, filtering and analytics. The results in this paper encourage future directions such as distributed statistics calculation, and scalability in terms of handling massive data sets.
complex event processing event analysis business process discovery correlation discovery data mining