Abstract: As the amount of data becomes bigger and bigger,it is increasingly meaningful to combine different business system data to mine potential values.The complex event processing technology abstracts the business data as an event sequence,and describes the potentially valuable composite data as a specific event matching structure through the event description method.Then the event detection engine detects the event sequence meeting the matching structure from a large number of event flows,and finally outputs the data fusion results.However,in the traditional event description,the input event flow of the event engine is a single atomic event type,the event predicate constraint contains a simple attribute value comparison operation or simple aggregation operation,and the time constraint between events is simple.This makes the traditional detection method cannot be suitable for some application fields in which the time is required to be more accurate and the event predicate constraint is required to be more complex,such as medicine and finance.In light of this,this paper designed a multivariate event input supported quantitative timing constraint representation model based on TCN and predicate constraint representation model based on time-interval feature constraint,and proposed a parallel detection algorithm for complex events(PARALLEL-TCSEQ-DETECTION).The method makes the complex event detection more efficient.The analysis results based on 200 million records of 2045 stocks demonstrate the validity and high efficiency of the proposed processing technology for the complex events.

Key words: CEP, Event detection model, Parallel, TCN, Timing feature