Complex Event Processing (CEP) systems aim at processing large flows of events to discover situations of interest (SOI ). Primarily, CEP uses predefined pattern templates to detect occurrences of complex events in an event stream. Extracting complex event is achieved by employing techniques such as filtering and aggregation to detect complex patterns of many simple events. In general, CEP systems rely on domain experts to define complex pattern rules to recognize SOI. However, the task of fine tuning complex pattern rules in the event streaming environment faces two main challenges: the issue of increased pattern complexity and the event streaming constraints where such rules must be acquired and processed in near real-time.Therefore, to fine tune the CEP pattern to identify SOI, the following requirements must be met: first, a minimum number of rules must be used to refine the CEP pattern to avoid increased pattern complexity, and second, domain knowledge must be incorporated in the refinement process to improve awareness about the emerging situations. Furthermore, the event data must be processed upon arrival to cope with the continuous arrival of events in the stream and to respond in near real-time.In this dissertation , we present a Situation Refinement Model (SRM) that considers these requirements. In particular, by developing a Single-Scan Frequent Item Mining algorithm to acquire the minimal number of CEP rules with the ability to adjust the level of refinement to fit the applied scenario. In addition, a cost-gain evaluation measure to determine the best tradeoff to identify a particular SOI is presented.

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