Making complex event processing simple

Posted by Emma Thompson on September 20th, 2017

Complex event processing is yet another great stream that has been revolutionized by spark streaming. Though complex event processing has been around for more than two decades, but never before it had the potential it has today thanks to streaming analytics. Simply put complex event processing is joining up of seemingly different events into one thread and inferring a conclusion out of that.

For instance, heavy rain, falling of a tree and a car accident may be three separate events but when stitched together they become one and make sense – due to heavy rain a tree is uprooted and a car rams into it. Thus the system joins three separate events to understand the larger perspective as we humans do all the time. Taking the cue from the past we make patterns and juxtapose the present set of events to the previous ones to understand the larger picture.

This has of course great importance in real life. From politics to business to sports to health care it can be applied effectively to read the events with the past patterns and draw logical conclusions to assess the situation and carry out predictive analysis and prepare to take appropriate action. Event processing is a method of tracking and analyzing (processing) streams of information (data) about things that happen (events), and deriving a conclusion from them. The CEP area has roots in discrete event simulation, the active database area, and some programming languages. The activity in the industry was preceded by a wave of research projects in the 1990s.

Complex event processing, or CEP, is event processing that combines data from multiple sources to infer events or patterns that suggest more complicated circumstances. The goal of complex event processing is to identify meaningful events (such as opportunities or threats) and respond to them as quickly as possible.

These events may be happening across the various layers of an organization as sales leads, orders or customer service calls. Or, they may be news items, text messages, social media posts, stock market feeds, traffic reports, weather reports, or other kinds of data. An event may also be defined as a "change of state," when a measurement exceeds a predefined threshold of time, temperature, or other value. Analysts suggest that CEP will give organizations a new way to analyze patterns in real-time and help the business side communicate better with IT and service departments. The vast amount of information available about events is sometimes referred to as the event cloud.

CEP as a technique helps discover complex events by analyzing and correlating other events: CEP relies on a number of techniques, including:

1. Event-pattern detection
2. Event abstraction
3. Event filtering
4. Event aggregation and transformation
5. Modeling event hierarchies
6. Detecting relationships (such as causality, membership or timing) between events
7. Abstracting event-driven processes
8. Commercial applications of CEP exist in the variety of industries and include algorithmic stock-trading, the detection of credit-card fraud, etc.