Efficient process discovery from event streams using sequential pattern mining

M. Hassani, S. Siccha, F. Richter, T. Seidl

Research output: Chapter in Book/Report/Conference proceedingConference contributionAcademicpeer-review

36 Citations (Scopus)
4 Downloads (Pure)


Process mining is an emerging research area that brings the well-established data mining solutions to the chal- lenging business process modeling problems. Mining streams of business processes in the real time as they are generated is a necessity to obtain an instant knowledge from big process data. In this paper, we introduce an efficient approach for exploring and counting process fragments from a stream of events to infer a process model using the Heuristics Miner algorithm. Our novel approach, called StrProM, builds prefix-trees to extract sequential patterns of events from the stream. StrProM uses a batch-based approach to continuously update and prune these prefix-trees. The models are generated from those trees after applying a decaying mechanism over their statistics. The extensive experimental evaluation demonstrates the superiority of our approach over a state-of-the-art technique in terms of execution time using a real dataset, while delivering models of a comparable quality. I.
Original languageEnglish
Title of host publication2015 IEEE Symposium Series on Computational Intelligence, SSCI 2015 : 8-10 December 2015, Cape Town, South Africa
Place of PublicationPiscataway
PublisherInstitute of Electrical and Electronics Engineers
Number of pages8
ISBN (Print)9781479975600
Publication statusPublished - 2015
Externally publishedYes
Event6th IEEE Symposium on Computational Intelligence and Data Mining (CIDM2015) - Cape Town, South Africa
Duration: 7 Dec 201510 Dec 2015
Conference number: 6


Conference6th IEEE Symposium on Computational Intelligence and Data Mining (CIDM2015)
Abbreviated titleCIDM2015
CountrySouth Africa
CityCape Town
Internet address

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