Complex Event Processing (CEP) combines streams of simple events from multiple sources and infer high-level knowledge by correlating the simple events. The correlation of events is done according to the requirement of the application. This requirement is specified to the CEP system in the form of rules. State of the art implementations of CEP system deploy a distributed network of operators to execute a rule. For low latency information retrieval under high event rate of input streams, scalability of operators is required. This is done by the parallelization of operator functionality. The existing PACE system provides a framework for operator parallelization. Parameter context in a rule specify which events in the input stream can be used for correlation and are essential for application of CEP systems to many real world situations. In this thesis operator parallelization techniques, for adopting the existing PACE system to execute rules with various parameter contexts, are proposed. In cases where the existing PACE system cannot be adopted, other approaches for operator parallelization are proposed. Finally, the proposed techniques are analysed by implementing the techniques and evaluating their performance.