In the era of IoT, large volumes of event data from different sources are collected in the form of streams. As these logs need to be online processed to extract further knowledge about the underlying business process, it is becoming more and more important to give support to run-time monitoring. In particular, increasing attention has been turned to conformance checking as a way to identify when a sequence of events deviates from the expected behavior. Albeit rather straightforward on a small log file, conformance verification techniques may show poor performance when dealing with big data, making increasingly attractive the possibility to improve scalability through distributed computation. In this paper, we adopt a previously implemented framework for compliance verification (which provides a high-level logic-based notation for the monitoring specification) and we show how it can be efficiently distributed on a set of computing nodes to support scalable run-time monitoring when dealing with large volumes of event logs.

Distributed Compliance Monitoring of Business Processes over MapReduce Architectures

LORETI, DANIELA;CHESANI, FEDERICO;CIAMPOLINI, ANNA;MELLO, PAOLA
2017

Abstract

In the era of IoT, large volumes of event data from different sources are collected in the form of streams. As these logs need to be online processed to extract further knowledge about the underlying business process, it is becoming more and more important to give support to run-time monitoring. In particular, increasing attention has been turned to conformance checking as a way to identify when a sequence of events deviates from the expected behavior. Albeit rather straightforward on a small log file, conformance verification techniques may show poor performance when dealing with big data, making increasingly attractive the possibility to improve scalability through distributed computation. In this paper, we adopt a previously implemented framework for compliance verification (which provides a high-level logic-based notation for the monitoring specification) and we show how it can be efficiently distributed on a set of computing nodes to support scalable run-time monitoring when dealing with large volumes of event logs.
2017
ICPE '17 Companion Proceedings of the 8th ACM/SPEC on International Conference on Performance Engineering Companion
79
84
Loreti, Daniela; Chesani, Federico; Ciampolini, Anna; Mello, Paola
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11585/584916
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