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Using a Deep Understanding of Network Activities for Workflow Mining

  • Mona Lange
  • Felix Kuhr
  • Ralf Möller
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9904)

Abstract

Workflow mining is the task of automatically detecting workflows from a set of event logs. We argue that network traffic can serve as a set of event logs and, thereby, as input for workflow mining. Networks produce large amounts of network traffic and we are able to extract sequences of workflow events by applying data mining techniques. We come to this conclusion due to the following observation: Network traffic consists of network packets, which are exchanged between network devices in order to share information to fulfill a common task. This common task corresponds to a workflow event and, when observed over time, we are able to record sequences of workflow events and model workflows as Hidden Markov models (HMM). Sequences of workflow events are caused by network dependencies, which force distributed network devices to interact. To automatically derive workflows based on network traffic, we propose a methodology based on network service dependency mining.

Keywords

Workflow mining Hidden Markov model Network dependency analysis 

Notes

Acknowledgments

This work was partly supported by the Seventh Framework Programme (FP7) of the European Commission as part of the PANOPTESEC integrated research project (GA 610416).

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Copyright information

© Springer International Publishing AG 2016

Authors and Affiliations

  1. 1.Universität zu LübeckLübeckGermany
  2. 2.Hamburg University of TechnologyHamburgGermany

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