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• W127804522 abstract "The volume of data related to business process execution is increasing significantly in the enterprise. Many of data sources include events related to the execution of the same processes in various systems or applications. Event correlation is the task of analyzing a repository of event logs in order to find out the set of events that belong to the same business process execution instance. This is a key step in the discovery of business processes from event execution logs. Event correlation is a computationally-intensive task in the sense that it requires a deep analysis of very large and growing repositories of event logs, and exploration of various possible relationships among the events. In this dissertation, we present a scalable data analysis technique to support efficient event correlation for mining business processes. We propose a two-stages approach to compute correlation conditions and their entailed process instances from event logs using MapReduce framework. The experimental results show that the algorithm scales well to large datasets." @default.
• W127804522 created "2016-06-24" @default.
• W127804522 creator A5081682907 @default.
• W127804522 date "2014-02-19" @default.
• W127804522 modified "2023-09-27" @default.
• W127804522 title "Using MapReduce to scale event correlation discovery for process mining" @default.
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• W127804522 hasPublicationYear "2014" @default.
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