With the huge amount of data, it is increasingly meaningful to combine different business system data with potential values. In the traditional event description, the input event ﬂow of the event engine is a single atomic event type. The event predicate constraint contains simple attribute value, comparison operation and simple aggregation operation. The time constraint between events always simply. This makes the traditional detection method cannot meet the requirements such as ﬁnancial, medical and other relatively accurate time requirements, event predicate constraints require more complex applications. Thus, this paper introduces the long short-term memory network model (LSTM), designs a multivariate event input to process these data based on TCN quantitative timing constraint representation model and predicate constraint representation model. In this paper, an innovative method makes the complex event processing technology more high efﬁcient. By the analysis 200 million records of 2045 stocks, the results show that the processing technology of the complex events is more effective, more efﬁcient. Keywords Long short-term memory Complex event processing Temporal constraint network Timing feature 1 Introduction technologies . But at present, users can only intuitively perceive single system data behavioral event in most dis- In recent years, interactive heterogeneous
Cluster Computing – Springer Journals
Published: May 29, 2018
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