Integrating Predictive Analytics into Complex Event Processing by Using Conditional Density Estimations

Abstract

The monitoring of real-time objects such as steel billets during their casting process creates myriads of events. Complex Event Processing (CEP) is the technology to analyze resulting event streams as fast as possible. But classic CEP is not able to consider events that did not happen yet. It is not clear how to transform CEP from a technology, which reacts on past events, to one, which anticipates near future events. Conditional density estimation allows to combine both estimation and expected uncertainty about the next occurrence of a given event in one mathematical object. Moreover, it allows to calculate the probability of event patterns, which are the basis for CEP. Hence, we are introducing the concept of Conditional Event Occurrence Density Estimation (CEODE) to CEP. We present a reference architecture for combining CEP engines with predictive analytics using CEODEs. On basis of concrete guidelines for transforming classical event processing rules to proactive ones, we are demonstrating how CEP evolves from being reactive to becoming both predictive and prescriptive.