Abstract
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.
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Acknowledgements
The authors acknowledge the National Key Research and Development Program of China (Grant No. 2017YFB1402400), National High Technology Research and Development Program of China (Grant: 2015AA015308), Social Undertakings and Livelihood Security Science and Technology Innovation Funds of CQ CSTC (Grant: cstc2017shmsA0641), the National Nature Science Foundation of China (Grant: 61762025).
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Li, Q., Zhong, J., Tao, Y. et al. Research of the processing technology for time complex event based on LSTM. Cluster Comput 22 (Suppl 4), 9571–9579 (2019). https://doi.org/10.1007/s10586-018-2765-z
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DOI: https://doi.org/10.1007/s10586-018-2765-z