Using a Deep Understanding of Network Activities for Workflow Mining

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.
Original languageEnglish
Title of host publicationKI 2016: Advances in Artificial Intelligence
EditorsGerhard Friedrich, Malte Helmert, Franz Wotawa
Number of pages8
Volume9904
Place of PublicationCham
PublisherSpringer International Publishing
Publication date08.09.2016
Pages177-184
ISBN (Print)978-3-319-46072-7
ISBN (Electronic)978-3-319-46073-4
DOIs
Publication statusPublished - 08.09.2016
Event39th German Conference on Artificial Intelligence - Klagenfurt, Austria
Duration: 26.09.201630.09.2016
Conference number: 181639

DFG Research Classification Scheme

  • 409-06 Information Systems, Process and Knowledge Management

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