Distributed Compliance Monitoring of Business Processes over MapReduce Architectures

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.

[1]  Ernesto Damiani,et al.  Processes Meet Big Data: Connecting Data Science with Process Science , 2015, IEEE Transactions on Services Computing.

[2]  Boualem Benatallah,et al.  Using Mapreduce to Scale Events Correlation Discovery for Business Processes Mining , 2012, BPM.

[3]  Marco Montali,et al.  Runtime Verification of LTL-Based Declarative Process Models , 2011, RV.

[4]  Robert A. Kowalski,et al.  The Iff Proof Procedure for Abductive Logic Programming , 1997, J. Log. Program..

[5]  Joerg Evermann,et al.  Scalable Process Discovery Using Map-Reduce , 2016, IEEE Transactions on Services Computing.

[6]  Wil M. P. van der Aalst,et al.  Declarative Workflow , 2010, Modern Business Process Automation.

[7]  Wil M. P. van der Aalst,et al.  Handling Big(ger) Logs: Connecting ProM 6 to Apache Hadoop , 2015, BPM.

[8]  Joerg Evermann,et al.  Big data meets process mining: implementing the alpha algorithm with map-reduce , 2014, SAC.

[9]  Evelina Lamma,et al.  Evaluating compliance: from LTL to abductive logic programming , 2015, CILC.

[10]  Alexander Artikis,et al.  Logic-based event recognition , 2012, The Knowledge Engineering Review.

[11]  David Luckham,et al.  The power of events - an introduction to complex event processing in distributed enterprise systems , 2002, RuleML.

[12]  Paola Mello,et al.  Abducing Compliance of Incomplete Event Logs , 2016, AI*IA.

[13]  Paola Mello,et al.  On the integration of declarative choreographies and Commitment-based agent societies into the SCIFF logic programming framework , 2010, Multiagent Grid Syst..

[14]  Wil M. P. van der Aalst,et al.  Distributed Process Discovery and Conformance Checking , 2012, FASE.

[15]  van der Wmp Wil Aalst,et al.  Declarative workflow (Chapter 6) , 2010 .

[16]  Wil M. P. van der Aalst,et al.  The Application of Petri Nets to Workflow Management , 1998, J. Circuits Syst. Comput..

[17]  Sanjay Ghemawat,et al.  MapReduce: Simplified Data Processing on Large Clusters , 2004, OSDI.

[18]  Marco Montali,et al.  Compliance monitoring in business processes: Functionalities, application, and tool-support , 2015, Inf. Syst..

[19]  Wil M. P. van der Aalst,et al.  Workflow mining: discovering process models from event logs , 2004, IEEE Transactions on Knowledge and Data Engineering.

[20]  Wil M. P. van der Aalst,et al.  Verification of Workflow Nets , 1997, ICATPN.

[21]  Carlo Ghezzi,et al.  Trace Checking of Metric Temporal Logic with Aggregating Modalities Using MapReduce , 2014, SEFM.

[22]  Felix Klaedtke,et al.  Scalable offline monitoring of temporal specifications , 2016, Formal Methods in System Design.

[23]  Ernesto Damiani,et al.  Process Mining in Big Data Scenario , 2015, SIMPDA.

[24]  Paola Mello,et al.  Process Mining Monitoring for Map Reduce Applications in the Cloud , 2016, CLOSER.

[25]  Evelina Lamma,et al.  Verifiable agent interaction in abductive logic programming: The SCIFF framework , 2008, TOCL.

[26]  Wil M. P. van der Aalst,et al.  Declarative workflows: Balancing between flexibility and support , 2009, Computer Science - Research and Development.

[27]  Martin Leucker,et al.  A brief account of runtime verification , 2009, J. Log. Algebraic Methods Program..

[28]  Akhil Kumar,et al.  Managing Controlled Violation of Temporal Process Constraints , 2015, BPM.

[29]  Sylvain Hallé,et al.  MapReduce for parallel trace validation of LTL properties , 2015, Journal of Cloud Computing.

[30]  Ricardo Seguel,et al.  Process Mining Manifesto , 2011, Business Process Management Workshops.

[31]  Serge Haddad,et al.  Application and Theory of Petri Nets , 2012, Lecture Notes in Computer Science.