Paper Info
Title
Efficient Event Correlation over Distributed Systems.
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.
Year
DOI
Venue
2017
10.1109/CCGRID.2017.94
CCGrid
Keywords
Field
DocType
event correlation, process mining, service computing, data partitioning, big data, data-intensive computing
Spark (mathematics),Algorithm design,Data-intensive computing,Computer science,Event correlation,Event partitioning,Business process discovery,Big data,Process mining,Distributed computing
Conference
ISSN
ISBN
Citations 
2376-4414
978-1-5090-5980-5
2
PageRank 
References 
Authors
0.37
21
3
Name
Order
Citations
PageRank
Long Cheng19116.99
Boudewijn F. van Dongen2187597.84
Wil Van Der Aalst3208941418.27