Complex event processing 

Forrester defines a CEP platform as, a software infrastructure that can detect patterns of events (and expected events that didn’t occur) by filtering, correlating, contextualizing, and analyzing data captured from disparate live data sources to respond as defined using the platform’s development tools.

Complex event processing (CEP) is a subset of event stream processing. CEP enables you to gain insights from large volumes of data in near real-time by monitoring, analyzing, and acting on data while it is in motion. Data is typically generated by business or system events such as placing an order or adding a message to a queue. CEP is the continuous monitoring and processing of events from multiple sources on a near real-time basis. Since CEP enables the analysis of data in real-time, it lends itself to predictive scenarios to enable more proactive decisions. 

Typical scenarios may include:

• Monitoring the effectiveness of key performance indicators (KPIs) by using data from event streams
• Monitoring the health and availability of servers, networks and service level threshold compliance
• Fraud detection
• Stock ticker analysis—taking action when certain events occur or price points are achieved
• Performance history—predicting spikes
• Buying patterns (what product/pricing combinations are most popular)

The concept behind CEP is the aggregation of information over a time window or looking for a pattern and generating a notification when the aggregation of data or pattern breaches a defined condition. The emphasis is placed on detection of the event.

CEP has its origins in the stock market and, because of this fact, it is tuned for low latency and often responds in a few milliseconds or sub-milliseconds. Some of the events can be ignored without impact.

Internet of things (IoT) applications are very good to use cases for CEP since they are time series data, auto-correlated. IoT use cases are usually complex and they go beyond aggregation and calculation of data. These types of cases need complex operations such as time windows and temporal query patterns. Due to the availability of temporal operators, it's easy to process time series data efficiently. The following figure illustrations showcase CEP Flow: