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Split miner: automated discovery of accurate and simple business process models from event logs

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Abstract

The problem of automated discovery of process models from event logs has been intensively researched in the past two decades. Despite a rich field of proposals, state-of-the-art automated process discovery methods suffer from two recurrent deficiencies when applied to real-life logs: (i) they produce large and spaghetti-like models; and (ii) they produce models that either poorly fit the event log (low fitness) or over-generalize it (low precision). Striking a trade-off between these quality dimensions in a robust and scalable manner has proved elusive. This paper presents an automated process discovery method, namely Split Miner, which produces simple process models with low branching complexity and consistently high and balanced fitness and precision, while achieving considerably faster execution times than state-of-the-art methods, measured on a benchmark covering twelve real-life event logs. Split Miner combines a novel approach to filter the directly-follows graph induced by an event log, with an approach to identify combinations of split gateways that accurately capture the concurrency, conflict and causal relations between neighbors in the directly-follows graph. Split Miner is also the first automated process discovery method that is guaranteed to produce deadlock-free process models with concurrency, while not being restricted to producing block-structured process models.

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Notes

  1. http://www.bpmn.org/.

  2. In the empirical evaluation, we use \(k=3\) because existing measures of fitness and precision are slow to compute, making high k values impractical.

  3. BPMN allows process models to have multiple start and multiple end events, but such process models can be rewritten as process models with a single start and a single end event; hence, we can restrict ourselves to process models with a single start and a single end event without loss of generality.

  4. Each node of the graph represents a task.

  5. We favor self-loops over concurrency.

  6. First-in first-out.

  7. A split-task represents a syntactical error in the BPMN model.

  8. This happens because by construction a task cannot belong to its own future.

  9. An OR-join is said trivial when its semantic is equivalent to the semantic of an XOR or AND join.

  10. By \(\mathbb {N}_0\), we denote the set of all natural numbers including zero.

  11. Available at http://apromore.org/platform/tools.

  12. https://data.4tu.nl/repository/collection:event_logs_real.

  13. We did not hyperparameter-optimize ETM due to the prohibitively high execution times of this method.

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Acknowledgements

This research is partly funded by the Australian Research Council (Grant DP180102839) and the Estonian Research Council (Grant IUT20-55).

Reproducibility. Links to all tools and datasets required to reproduce the experiments are given in Sections IV.B-IV.C.

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Authors and Affiliations

  1. The University of Melbourne, Melbourne, Australia

    Adriano Augusto, Raffaele Conforti, Marcello La Rosa & Artem Polyvyanyy

  2. University of Tartu, Tartu, Estonia

    Adriano Augusto & Marlon Dumas

Authors
  1. Adriano Augusto
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  2. Raffaele Conforti
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  3. Marlon Dumas
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  4. Marcello La Rosa
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  5. Artem Polyvyanyy
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Correspondence to Adriano Augusto.

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Augusto, A., Conforti, R., Dumas, M. et al. Split miner: automated discovery of accurate and simple business process models from event logs. Knowl Inf Syst 59, 251–284 (2019). https://doi.org/10.1007/s10115-018-1214-x

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