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Abstract
Business Process Management continues to gain much interest ever since the economic crisis that occurred in 2008. Many organizations are monitoring and auditing their own systems for detecting any violations, in order to avoid them. This has led many researches to address the issue of business processes monitoring at different business process life cycle phases specifically; design, configuration, enactment, and evaluation. However, monitoring business processes at enactment time has many challenges. Monitoring a large amount of event streams from different sources, in order to detect violations and maintain them, is one of the major challenges that need special event correlation techniques. Moreover, considering the human factor in performing unmanaged business processes, where tracing human behavior without a central execution engine for managing/orchestrating their tasks, can lead to the possibility of violations.Monitoring business processes plays a major role in conformance checking, compliance enforcement, risk management, and performance analysis. However, these techniques need a set of correlated events. Business process execution produces a set of events with basic information such as timestamp, activity name and case identifier. However, with the lack of a central orchestration execution engine, it is hard to correlate these events.In this research, we present two different approaches to fill the gap between the execution of uncorrelated events in acyclic and cyclic models, and the stack of techniques and approaches that need correlated events to conduct further analysis.These approaches induce a case identifier for each uncorrelated event with a high degree of trust at runtime. We provide two different approaches to fill this gap. One is by correlating events using a case decision tree and the other technique is by correlating events using the causal dependencies between activities and indexing techniques in a database management system