Abstract
Process models often do not adequately reect the behavior of real-life systems. In the general case, it is possible to construct a new adequate model by applying one of the discovery algorithms. At the same time, there are cases when the original model is of particular value. In such cases, it is better to apply model repair algorithms. Those algorithms construct a model which reects real behavior according to some criteria. Moreover, the repaired model remains as similar to the original one as possible. This paper proposes a modular approach which consists of three parts: (1) decomposing the process model and event log into model fragments and sub-logs, (2) selecting the fragments which need to be repaired, (3) repairing the selected fragments using a process discovery algorithm.
| Original language | English |
|---|---|
| Title of host publication | Supplementary Proceedings of the Sixth International Conference on Analysis of Images, Social Networks and Texts (AIST 2017) |
| Publisher | CEUR-WS.org |
| Pages | 301-313 |
| Number of pages | 13 |
| Publication status | Published - 1 Jan 2017 |
| Event | 6th International Conference on Analysis of Images, Social Networks and Texts (AIST 2017) - Polytechnic University Moscow, Moscow, Russian Federation Duration: 27 Jul 2017 → 29 Jul 2017 Conference number: 6 http://2017.aistconf.ru/ |
Publication series
| Name | CEUR Workshop Proceedings |
|---|---|
| Volume | 1975 |
| ISSN (Print) | 1613-0073 |
Conference
| Conference | 6th International Conference on Analysis of Images, Social Networks and Texts (AIST 2017) |
|---|---|
| Abbreviated title | AIST 2017 |
| Country | Russian Federation |
| City | Moscow |
| Period | 27/07/17 → 29/07/17 |
| Internet address |
Fingerprint
Keywords
- Divide and conquer
- Petri nets
- Process mining
- Process model decomposition
- Process model repair
Cite this
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Process model repair by detecting unfitting fragments? / Mitsyuk, Alexey A.; Lomazova, Irina A.; Shugurov, Ivan S.; van der Aalst, Wil M.P.
Supplementary Proceedings of the Sixth International Conference on Analysis of Images, Social Networks and Texts (AIST 2017). CEUR-WS.org, 2017. p. 301-313 (CEUR Workshop Proceedings; Vol. 1975).Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Academic › peer-review
TY - GEN
T1 - Process model repair by detecting unfitting fragments?
AU - Mitsyuk, Alexey A.
AU - Lomazova, Irina A.
AU - Shugurov, Ivan S.
AU - van der Aalst, Wil M.P.
PY - 2017/1/1
Y1 - 2017/1/1
N2 - Process models often do not adequately reect the behavior of real-life systems. In the general case, it is possible to construct a new adequate model by applying one of the discovery algorithms. At the same time, there are cases when the original model is of particular value. In such cases, it is better to apply model repair algorithms. Those algorithms construct a model which reects real behavior according to some criteria. Moreover, the repaired model remains as similar to the original one as possible. This paper proposes a modular approach which consists of three parts: (1) decomposing the process model and event log into model fragments and sub-logs, (2) selecting the fragments which need to be repaired, (3) repairing the selected fragments using a process discovery algorithm.
AB - Process models often do not adequately reect the behavior of real-life systems. In the general case, it is possible to construct a new adequate model by applying one of the discovery algorithms. At the same time, there are cases when the original model is of particular value. In such cases, it is better to apply model repair algorithms. Those algorithms construct a model which reects real behavior according to some criteria. Moreover, the repaired model remains as similar to the original one as possible. This paper proposes a modular approach which consists of three parts: (1) decomposing the process model and event log into model fragments and sub-logs, (2) selecting the fragments which need to be repaired, (3) repairing the selected fragments using a process discovery algorithm.
KW - Divide and conquer
KW - Petri nets
KW - Process mining
KW - Process model decomposition
KW - Process model repair
UR - http://www.scopus.com/inward/record.url?scp=85034959443&partnerID=8YFLogxK
UR - http://ceur-ws.org/Vol-1975/
M3 - Conference contribution
AN - SCOPUS:85034959443
T3 - CEUR Workshop Proceedings
SP - 301
EP - 313
BT - Supplementary Proceedings of the Sixth International Conference on Analysis of Images, Social Networks and Texts (AIST 2017)
PB - CEUR-WS.org
ER -