期刊
IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING
卷 31, 期 4, 页码 686-705出版社
IEEE COMPUTER SOC
DOI: 10.1109/TKDE.2018.2841877
关键词
Process mining; automated process discovery; survey; benchmark
类别
资金
- Australian Research Council [DP150103356]
- Estonian Research Council [IUT20-55]
- H2020-RISE EU project FIRST [734599]
- Sapienza grant DAKIP
- Italian project Social Museum and Smart Tourism [CTN01_00034_23154]
- Italian project NEPTIS [PON03PE_00214_3]
- Italian project RoMA - Resilience of Metropolitan Areas [SCN_00064]
Process mining allows analysts to exploit logs of historical executions of business processes to extract insights regarding the actual performance of these processes. One of the most widely studied process mining operations is automated process discovery. An automated process discovery method takes as input an event log, and produces as output a business process model that captures the control-flow relations between tasks that are observed in or implied by the event log. Various automated process discovery methods have been proposed in the past two decades, striking different tradeoffs between scalability, accuracy, and complexity of the resulting models. However, these methods have been evaluated in an ad-hoc manner, employing different datasets, experimental setups, evaluation measures, and baselines, often leading to incomparable conclusions and sometimes unreproducible results due to the use of closed datasets. This article provides a systematic review and comparative evaluation of automated process discovery methods, using an open-source benchmark and covering 12 publicly-available real-life event logs, 12 proprietary real-life event logs, and nine quality metrics. The results highlight gaps and unexplored tradeoffs in the field, including the lack of scalability of some methods and a strong divergence in their performance with respect to the different quality metrics used.
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