Performance Analysis: Discovering Semi-Markov Models From Event Logs
Process mining is a well-established discipline of data analysis focused on the discovery of process models from information systems’ event logs. Recently, an emerging subarea of process mining, known as stochastic process discovery, has started to evolve. Stochastic process discovery con...
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IEEE
2025-01-01
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| Online Access: | https://ieeexplore.ieee.org/document/10904251/ |
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| author | Anna Kalenkova Lewis Mitchell Matthew Roughan |
| author_facet | Anna Kalenkova Lewis Mitchell Matthew Roughan |
| author_sort | Anna Kalenkova |
| collection | DOAJ |
| description | Process mining is a well-established discipline of data analysis focused on the discovery of process models from information systems’ event logs. Recently, an emerging subarea of process mining, known as stochastic process discovery, has started to evolve. Stochastic process discovery considers frequencies of events in the event data and allows for a more comprehensive analysis. In particular, when the durations of activities are presented in the event log, performance characteristics of the discovered stochastic models can be analyzed, e.g., the overall process execution time can be estimated. Existing performance analysis techniques usually discover stochastic process models from event data, and then simulate these models to evaluate their execution times. These methods rely on empirical approaches. This paper proposes analytical techniques for performance analysis that allow for the derivation of statistical characteristics of the overall processes’ execution times in the presence of arbitrary time distributions of events modeled by semi-Markov processes. The proposed methods include express analysis, focused on the mean execution time estimation, and full analysis techniques that build probability density functions (PDFs) of process execution times in both continuous and discrete forms. These methods are implemented and tested on real-world event data, demonstrating their potential for what-if analysis by providing solutions without resorting to simulation. Specifically, we demonstrated that the discrete approach is more time-efficient for small duration support sizes compared to the simulation technique. Furthermore, we showed that the continuous approach, with PDFs represented as Mixtures of Gaussian Models (GMMs), facilitates the discovery of more compact and interpretable models. |
| format | Article |
| id | doaj-art-4bafc9f7715440abbf1c7dc9998cebd9 |
| institution | DOAJ |
| issn | 2169-3536 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-4bafc9f7715440abbf1c7dc9998cebd92025-08-20T03:15:38ZengIEEEIEEE Access2169-35362025-01-0113380353805310.1109/ACCESS.2025.354603310904251Performance Analysis: Discovering Semi-Markov Models From Event LogsAnna Kalenkova0https://orcid.org/0000-0002-5088-7602Lewis Mitchell1https://orcid.org/0000-0001-8191-1997Matthew Roughan2https://orcid.org/0000-0002-7882-7329Adelaide Data Science Centre (ADSC), School of Computer and Mathematical Sciences, The University of Adelaide, North Terrace Campus, Adelaide, SA, AustraliaAdelaide Data Science Centre (ADSC), School of Computer and Mathematical Sciences, The University of Adelaide, North Terrace Campus, Adelaide, SA, AustraliaAdelaide Data Science Centre (ADSC), School of Computer and Mathematical Sciences, The University of Adelaide, North Terrace Campus, Adelaide, SA, AustraliaProcess mining is a well-established discipline of data analysis focused on the discovery of process models from information systems’ event logs. Recently, an emerging subarea of process mining, known as stochastic process discovery, has started to evolve. Stochastic process discovery considers frequencies of events in the event data and allows for a more comprehensive analysis. In particular, when the durations of activities are presented in the event log, performance characteristics of the discovered stochastic models can be analyzed, e.g., the overall process execution time can be estimated. Existing performance analysis techniques usually discover stochastic process models from event data, and then simulate these models to evaluate their execution times. These methods rely on empirical approaches. This paper proposes analytical techniques for performance analysis that allow for the derivation of statistical characteristics of the overall processes’ execution times in the presence of arbitrary time distributions of events modeled by semi-Markov processes. The proposed methods include express analysis, focused on the mean execution time estimation, and full analysis techniques that build probability density functions (PDFs) of process execution times in both continuous and discrete forms. These methods are implemented and tested on real-world event data, demonstrating their potential for what-if analysis by providing solutions without resorting to simulation. Specifically, we demonstrated that the discrete approach is more time-efficient for small duration support sizes compared to the simulation technique. Furthermore, we showed that the continuous approach, with PDFs represented as Mixtures of Gaussian Models (GMMs), facilitates the discovery of more compact and interpretable models.https://ieeexplore.ieee.org/document/10904251/Event logsGaussian mixture modelsperformance analysisprocess miningsemi-Markov processestime distributions |
| spellingShingle | Anna Kalenkova Lewis Mitchell Matthew Roughan Performance Analysis: Discovering Semi-Markov Models From Event Logs IEEE Access Event logs Gaussian mixture models performance analysis process mining semi-Markov processes time distributions |
| title | Performance Analysis: Discovering Semi-Markov Models From Event Logs |
| title_full | Performance Analysis: Discovering Semi-Markov Models From Event Logs |
| title_fullStr | Performance Analysis: Discovering Semi-Markov Models From Event Logs |
| title_full_unstemmed | Performance Analysis: Discovering Semi-Markov Models From Event Logs |
| title_short | Performance Analysis: Discovering Semi-Markov Models From Event Logs |
| title_sort | performance analysis discovering semi markov models from event logs |
| topic | Event logs Gaussian mixture models performance analysis process mining semi-Markov processes time distributions |
| url | https://ieeexplore.ieee.org/document/10904251/ |
| work_keys_str_mv | AT annakalenkova performanceanalysisdiscoveringsemimarkovmodelsfromeventlogs AT lewismitchell performanceanalysisdiscoveringsemimarkovmodelsfromeventlogs AT matthewroughan performanceanalysisdiscoveringsemimarkovmodelsfromeventlogs |