Introduction to CEP

CEP is the complex event processing abbreviation, which translates to complex event processing (composite events may be more accurate).

1. Why do we need CEP? CEP is a function of real-time analysis and quick response. Let's consider the significance of CEP in one case. Classic case: Real-time statistics of the past red vehicles, traditional relational database practice, each car opened, then record a data in the database, to statistics, then take out the data to calculate the value, we put the process of access to the image into real life, have to this time through the car all in the parking lot, Wait until the time is up, and then find out all the red vehicles with the specified times or numbers. This practice is unreasonable, because the overhead of access is too large to achieve the real-time effect, and if there is a continuous query, then take too many times, the overhead is too large, if there is too much data, the overhead will increase. So unable to

2.CEP is to solve this kind of problem, and the traditional way to query the data before storage. CEP pre-sets the query criteria, and then lets the real-time data through these query conditions, the engine fetches the matching data, this query is continuous.

3.CEP is an event-driven architecture that enables sensing (real-time event detection), analysis (aggregation of various events), response (update expectations)

4. Event-driven applications vs. database Applications 5. Event events can be seen as observable state changes in a system. Example: See Lightning, Hear thunder, credit card consumption. For example, if something happens, it's always a phenomenon, so how do you know something happened?Simple event: Single event. Complex events: Complex events, compounded by multiple simple events, can also be understood as composite events. 　　Composite Event Example: Hear Thunder (Simple event) + See Lightning (simple) and will rain (compound), through two simple events we analyze a complex event.　　　　Role of 6.CEP: anomaly detection, trend analysis, discovery opportunities, pattern matching. 7. Industry Applications: Algorithmic Trading (algorithmic Trading), quantitative investment (quantitative investment), risk management (Risk Management), sensor data management and pattern analysis, business activity monitoring, crowd intelligence, 　　Network monitoring, System dynamic calibration and so on. The next chapter will introduce a brief introduction and comparative analysis of the two engines that implement CEP technology in Donet StreamInsight and Nesper.

Introduction to CEP