Impact-Driven Process Model Repair
The abundance of event data in today's information systems makes it possible to "confront" process models with the actual observed behavior. Process mining techniques use event logs to discover process models that describe the observed behavior, and to check conformance of process mod...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Polyvyanyy, Artem [verfasserIn] |
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| Format: |
Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2016 |
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| Schlagwörter: |
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| Übergeordnetes Werk: |
Enthalten in: ACM transactions on software engineering and methodology - New York, NY : ACM Press, 1992, 25(2016), 4, Seite 1-60 |
|---|---|
| Übergeordnetes Werk: |
volume:25 ; year:2016 ; number:4 ; pages:1-60 |
| Links: |
|---|
| DOI / URN: |
10.1145/2980764 |
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| 520 | |a The abundance of event data in today's information systems makes it possible to "confront" process models with the actual observed behavior. Process mining techniques use event logs to discover process models that describe the observed behavior, and to check conformance of process models by diagnosing deviations between models and reality. In many situations, it is desirable to mediate between a preexisting model and observed behavior. Hence, we would like to repair the model while improving the correspondence between model and log as much as possible. The approach presented in this article assigns predefined costs to repair actions (allowing inserting or skipping of activities). Given a maximum degree of change, we search for models that are optimal in terms of fitness-that is, the fraction of behavior in the log not possible according to the model is minimized. To compute fitness, we need to align the model and log, which can be time consuming. Hence, finding an optimal repair may be intractable. We propose different alternative approaches to speed up repair. The number of alignment computations can be reduced dramatically while still returning near-optimal repairs. The different approaches have been implemented using the process mining framework ProM and evaluated using real-life logs. | ||
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10.1145/2980764 doi PQ20161201 (DE-627)OLC1985871599 (DE-599)GBVOLC1985871599 (PRQ)a432-c4ea7867893aa226d2766b27bf1aa66ff1c361a2acbd2c66bd0a7430a16f3510 (KEY)0202023820160000025000400001impactdrivenprocessmodelrepair DE-627 ger DE-627 rakwb eng 004 DNB 54.00 bkl Polyvyanyy, Artem verfasserin aut Impact-Driven Process Model Repair 2016 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier The abundance of event data in today's information systems makes it possible to "confront" process models with the actual observed behavior. Process mining techniques use event logs to discover process models that describe the observed behavior, and to check conformance of process models by diagnosing deviations between models and reality. In many situations, it is desirable to mediate between a preexisting model and observed behavior. Hence, we would like to repair the model while improving the correspondence between model and log as much as possible. The approach presented in this article assigns predefined costs to repair actions (allowing inserting or skipping of activities). Given a maximum degree of change, we search for models that are optimal in terms of fitness-that is, the fraction of behavior in the log not possible according to the model is minimized. To compute fitness, we need to align the model and log, which can be time consuming. Hence, finding an optimal repair may be intractable. We propose different alternative approaches to speed up repair. The number of alignment computations can be reduced dramatically while still returning near-optimal repairs. The different approaches have been implemented using the process mining framework ProM and evaluated using real-life logs. process model event log Process mining process model repair repair recommendation Aalst, Wil oth Hofstede, Arthur oth Wynn, Moe oth Enthalten in ACM transactions on software engineering and methodology New York, NY : ACM Press, 1992 25(2016), 4, Seite 1-60 (DE-627)13105757X (DE-600)1105550-9 (DE-576)029156734 1049-331X nnns volume:25 year:2016 number:4 pages:1-60 http://dx.doi.org/10.1145/2980764 Volltext http://dl.acm.org/citation.cfm?id=2980764 GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT GBV_ILN_70 GBV_ILN_2021 GBV_ILN_2190 GBV_ILN_4125 54.00 AVZ AR 25 2016 4 1-60 |
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10.1145/2980764 doi PQ20161201 (DE-627)OLC1985871599 (DE-599)GBVOLC1985871599 (PRQ)a432-c4ea7867893aa226d2766b27bf1aa66ff1c361a2acbd2c66bd0a7430a16f3510 (KEY)0202023820160000025000400001impactdrivenprocessmodelrepair DE-627 ger DE-627 rakwb eng 004 DNB 54.00 bkl Polyvyanyy, Artem verfasserin aut Impact-Driven Process Model Repair 2016 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier The abundance of event data in today's information systems makes it possible to "confront" process models with the actual observed behavior. Process mining techniques use event logs to discover process models that describe the observed behavior, and to check conformance of process models by diagnosing deviations between models and reality. In many situations, it is desirable to mediate between a preexisting model and observed behavior. Hence, we would like to repair the model while improving the correspondence between model and log as much as possible. The approach presented in this article assigns predefined costs to repair actions (allowing inserting or skipping of activities). Given a maximum degree of change, we search for models that are optimal in terms of fitness-that is, the fraction of behavior in the log not possible according to the model is minimized. To compute fitness, we need to align the model and log, which can be time consuming. Hence, finding an optimal repair may be intractable. We propose different alternative approaches to speed up repair. The number of alignment computations can be reduced dramatically while still returning near-optimal repairs. The different approaches have been implemented using the process mining framework ProM and evaluated using real-life logs. process model event log Process mining process model repair repair recommendation Aalst, Wil oth Hofstede, Arthur oth Wynn, Moe oth Enthalten in ACM transactions on software engineering and methodology New York, NY : ACM Press, 1992 25(2016), 4, Seite 1-60 (DE-627)13105757X (DE-600)1105550-9 (DE-576)029156734 1049-331X nnns volume:25 year:2016 number:4 pages:1-60 http://dx.doi.org/10.1145/2980764 Volltext http://dl.acm.org/citation.cfm?id=2980764 GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT GBV_ILN_70 GBV_ILN_2021 GBV_ILN_2190 GBV_ILN_4125 54.00 AVZ AR 25 2016 4 1-60 |
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Polyvyanyy, Artem |
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Polyvyanyy, Artem |
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10.1145/2980764 |
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004 |
| title_sort |
impact-driven process model repair |
| title_auth |
Impact-Driven Process Model Repair |
| abstract |
The abundance of event data in today's information systems makes it possible to "confront" process models with the actual observed behavior. Process mining techniques use event logs to discover process models that describe the observed behavior, and to check conformance of process models by diagnosing deviations between models and reality. In many situations, it is desirable to mediate between a preexisting model and observed behavior. Hence, we would like to repair the model while improving the correspondence between model and log as much as possible. The approach presented in this article assigns predefined costs to repair actions (allowing inserting or skipping of activities). Given a maximum degree of change, we search for models that are optimal in terms of fitness-that is, the fraction of behavior in the log not possible according to the model is minimized. To compute fitness, we need to align the model and log, which can be time consuming. Hence, finding an optimal repair may be intractable. We propose different alternative approaches to speed up repair. The number of alignment computations can be reduced dramatically while still returning near-optimal repairs. The different approaches have been implemented using the process mining framework ProM and evaluated using real-life logs. |
| abstractGer |
The abundance of event data in today's information systems makes it possible to "confront" process models with the actual observed behavior. Process mining techniques use event logs to discover process models that describe the observed behavior, and to check conformance of process models by diagnosing deviations between models and reality. In many situations, it is desirable to mediate between a preexisting model and observed behavior. Hence, we would like to repair the model while improving the correspondence between model and log as much as possible. The approach presented in this article assigns predefined costs to repair actions (allowing inserting or skipping of activities). Given a maximum degree of change, we search for models that are optimal in terms of fitness-that is, the fraction of behavior in the log not possible according to the model is minimized. To compute fitness, we need to align the model and log, which can be time consuming. Hence, finding an optimal repair may be intractable. We propose different alternative approaches to speed up repair. The number of alignment computations can be reduced dramatically while still returning near-optimal repairs. The different approaches have been implemented using the process mining framework ProM and evaluated using real-life logs. |
| abstract_unstemmed |
The abundance of event data in today's information systems makes it possible to "confront" process models with the actual observed behavior. Process mining techniques use event logs to discover process models that describe the observed behavior, and to check conformance of process models by diagnosing deviations between models and reality. In many situations, it is desirable to mediate between a preexisting model and observed behavior. Hence, we would like to repair the model while improving the correspondence between model and log as much as possible. The approach presented in this article assigns predefined costs to repair actions (allowing inserting or skipping of activities). Given a maximum degree of change, we search for models that are optimal in terms of fitness-that is, the fraction of behavior in the log not possible according to the model is minimized. To compute fitness, we need to align the model and log, which can be time consuming. Hence, finding an optimal repair may be intractable. We propose different alternative approaches to speed up repair. The number of alignment computations can be reduced dramatically while still returning near-optimal repairs. The different approaches have been implemented using the process mining framework ProM and evaluated using real-life logs. |
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| title_short |
Impact-Driven Process Model Repair |
| url |
http://dx.doi.org/10.1145/2980764 http://dl.acm.org/citation.cfm?id=2980764 |
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false |
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Aalst, Wil Hofstede, Arthur Wynn, Moe |
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Aalst, Wil Hofstede, Arthur Wynn, Moe |
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2024-07-04T03:39:52.482Z |
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