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Efficient Event Correlation over Distributed Systems

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Efficient Event Correlation over Distributed Systems

Content Provider	ACM Digital Library
Author	Cheng, Long van der Aalst, Wil M.P. van Dongen, Boudewijn F.
Abstract	Event correlation is a cornerstone for process discovery over event logs crossing multiple data sources. The computed correlation rules and process instances will greatly help us to unleash the power of process mining. However, exploring all possible event correlations over a log could be time consuming, especially when the log is large. State-of-the-art methods based on MapReduce designed to handle this challenge have offered significant performance improvements over standalone implementations. However, all existing techniques are still based on a conventional generating-and-pruning scheme. Therefore, event partitioning across multiple machines is often inefficient. In this paper, following the principle of filtering-and-verification, we propose a new algorithm, called RF-GraP, which provides a more efficient correlation over distributed systems. We present the detailed implementation of our approach and conduct a quantitative evaluation using the Spark platform. Experimental results demonstrate that the proposed method is indeed efficient. Compared to the state-of-the-art, we are able to achieve significant performance speedups with obviously less network communication.
Starting Page	1
Ending Page	10
Page Count	10
File Format	PDF
ISBN	9781509066100
DOI	10.1109/CCGRID.2017.94
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2017-05-14
Access Restriction	Subscribed
Subject Keyword	Event correlation Process mining Data partitioning Service computing Big data Data-intensive computing
Content Type	Text
Resource Type	Article

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