SKTR: Trace Recovery from Stochastically Known Logs

Eli Bogdanov, Izack Cohen, Avigdor Gal

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


Developments in machine learning together with the increasing usage of sensor data challenge the reliance on deterministic logs, requiring new process mining solutions for uncertain, and in particular stochastically known, logs. In this work we formulate trace recovery, the task of generating a deterministic log from stochastically known logs that is as faithful to reality as possible. An effective trace recovery algorithm would be a powerful aid for maintaining credible process mining tools for uncertain settings. We propose an algorithmic framework for this task that recovers the best alignment between a stochastically known log and a process model, with three innovative features. Our algorithm, SKT R, 1) handles both Markovian and non-Markovian processes; 2) offers a quality-based balance between a process model and a log, depending on the available process information, sensor quality, and machine learning predictiveness power; and 3) offers a novel use of a synchronous product multigraph to create the log. An empirical analysis using five publicly available datasets, three of which use predictive models over standard video capturing benchmarks, shows an average relative accuracy improvement of more than 10% over a common baseline.

Original languageEnglish
Title of host publicationProceedings - 2023 5th International Conference on Process Mining, ICPM 2023
EditorsJorge Munoz-Gama, Stefanie Rinderle-Ma, Arik Senderovich
PublisherInstitute of Electrical and Electronics Engineers Inc.
Number of pages8
ISBN (Electronic)9798350358391
StatePublished - 2023
Event5th International Conference on Process Mining, ICPM 2023 - Rome, Italy
Duration: 23 Oct 202327 Oct 2023

Publication series

NameProceedings - 2023 5th International Conference on Process Mining, ICPM 2023


Conference5th International Conference on Process Mining, ICPM 2023

All Science Journal Classification (ASJC) codes

  • Artificial Intelligence
  • Information Systems
  • Information Systems and Management
  • Modelling and Simulation


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