Event Processing (EP) is a paradigm which analyzes streams of events to extract useful insights of real world events. As shown in Figure 1, we can divide EP into two main areas called Event Stream processing and Complex Event Processing (CEP). The first area, stream (i.e., event) processing supports many kinds of continuous analytics such as filter, aggregation, enrichment, classification, joining, etc. The second area, CEP uses patterns over sequences of simple events to detect and report composite events.

Complex event processing (CEP) is the art of detecting patterns (or predefined sequences of data) over continuous streams of data. Use cases can range from searching for DNA sequences and detecting suspicious activity in transaction logs to tracking shipments with specific characteristics (e.g., contaminated goods) and analyzing user activity on websites, and financial institutions, network security companies, retailers, IoT-based services

Complex Event Processing (CEP) is a novel and promising methodology that enables the real-time analysis of stream event data. The main purpose of CEP is detection of the complex event patterns from the atomic and semantically low-level events such as sensor, log, or RFID data. Complex event processing is useful for detecting patterns in streaming data and sending alerts or notifications based on these patterns. Anyone working with time critical data that needs to be monitored should know how to use CEP.