Identifying duplicate events are essential to various business process applications such as provenance querying or process mining. Distinct features of heterogeneous events including opaque names, dislocated traces and composite events, prevent existing data integration from techniques performing well.In the existing system logs are kept separately in the system and there will be n number of events. The search option will not be enabled though want to see the log with the time. The existing structure-based matching techniques for relational data also fail to perform owing to the poor discriminative power of dependency relationships between events.All events are separated with different patterns. Easily accessible for the user which kept as per the events. Proposed with a graph to monitor how much events occurs in an application. A generic pattern based matching framework is proposed, which is compatible with the existing structure based techniques. To improve the matching efficiency, we devise several bounds of matching scores for pruning. Recognizing the NP-hardness of the optimal event matching problem with patterns, we propose efficient heuristic.