Abstract
In organizations, process mining activities are typically performed in a recurrent fashion, e.g. once a week, an event log is extracted from the information systems and a process mining tool is used to analyze the process’ characteristics. Typically, process mining tools import the data from a file-based source in a pre-processing step, followed by an actual process discovery step over the pre-processed data in order to present results to the analyst. As the amount of event data grows over time, these tools take more and more time to do pre-processing and all this time, the business analyst has to wait for the tool to finish. In this paper, we consider the problem of recurrent process discovery in live environments, i.e. in environments where event data can be extracted from information systems near real time. We present a method that pre-processes each event when it is being generated, so that the business analyst has the pre-processed data at his/her disposal when starting the analysis. To this end, we define a notion of intermediate structure between the underlying data and the layer where the actual mining is performed. This intermediate structure is kept in a persistent storage and is kept live under updates. Using a state of the art process mining technique, we show the feasibility of our approach. Our work is implemented in the process mining tool ProM using a relational database system as our persistent storage. Experiments are presented on real-life event data to compare the performance of the proposed approach with the state of the art.
| Original language | English |
|---|---|
| Title of host publication | Business Process Management Workshops |
| Subtitle of host publication | BPM 2017 International Workshops, Barcelona, Spain, September 10-11, 2017, Revised Papers |
| Editors | E. Teniente, M. Weidlich |
| Place of Publication | Dordrecht |
| Publisher | Springer |
| Pages | 178-190 |
| Number of pages | 13 |
| ISBN (Electronic) | 978-3-319-74030-0 |
| ISBN (Print) | 978-3-319-74029-4 |
| DOIs | |
| Publication status | Published - 2018 |
| Event | 15th International Conference on Business Process Management (BPM 2017) - Barcelona, Spain Duration: 10 Sep 2017 → 15 Sep 2017 Conference number: 15 https://bpm2017.cs.upc.edu/ |
Publication series
| Name | Lecture Notes in Business Information Processing |
|---|---|
| Volume | 308 |
| ISSN (Print) | 1865-1348 |
Conference
| Conference | 15th International Conference on Business Process Management (BPM 2017) |
|---|---|
| Abbreviated title | BPM 2017 |
| Country | Spain |
| City | Barcelona |
| Period | 10/09/17 → 15/09/17 |
| Internet address |
Fingerprint
Keywords
- Incremental process discovery
- Live event data
- Recurrent process mining
Cite this
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Recurrent process mining with live event data. / Syamsiyah, A.; van Dongen, B.F.; van der Aalst, W.M.P.
Business Process Management Workshops: BPM 2017 International Workshops, Barcelona, Spain, September 10-11, 2017, Revised Papers. ed. / E. Teniente; M. Weidlich. Dordrecht : Springer, 2018. p. 178-190 (Lecture Notes in Business Information Processing; Vol. 308).Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Academic › peer-review
TY - GEN
T1 - Recurrent process mining with live event data
AU - Syamsiyah, A.
AU - van Dongen, B.F.
AU - van der Aalst, W.M.P.
PY - 2018
Y1 - 2018
N2 - In organizations, process mining activities are typically performed in a recurrent fashion, e.g. once a week, an event log is extracted from the information systems and a process mining tool is used to analyze the process’ characteristics. Typically, process mining tools import the data from a file-based source in a pre-processing step, followed by an actual process discovery step over the pre-processed data in order to present results to the analyst. As the amount of event data grows over time, these tools take more and more time to do pre-processing and all this time, the business analyst has to wait for the tool to finish. In this paper, we consider the problem of recurrent process discovery in live environments, i.e. in environments where event data can be extracted from information systems near real time. We present a method that pre-processes each event when it is being generated, so that the business analyst has the pre-processed data at his/her disposal when starting the analysis. To this end, we define a notion of intermediate structure between the underlying data and the layer where the actual mining is performed. This intermediate structure is kept in a persistent storage and is kept live under updates. Using a state of the art process mining technique, we show the feasibility of our approach. Our work is implemented in the process mining tool ProM using a relational database system as our persistent storage. Experiments are presented on real-life event data to compare the performance of the proposed approach with the state of the art.
AB - In organizations, process mining activities are typically performed in a recurrent fashion, e.g. once a week, an event log is extracted from the information systems and a process mining tool is used to analyze the process’ characteristics. Typically, process mining tools import the data from a file-based source in a pre-processing step, followed by an actual process discovery step over the pre-processed data in order to present results to the analyst. As the amount of event data grows over time, these tools take more and more time to do pre-processing and all this time, the business analyst has to wait for the tool to finish. In this paper, we consider the problem of recurrent process discovery in live environments, i.e. in environments where event data can be extracted from information systems near real time. We present a method that pre-processes each event when it is being generated, so that the business analyst has the pre-processed data at his/her disposal when starting the analysis. To this end, we define a notion of intermediate structure between the underlying data and the layer where the actual mining is performed. This intermediate structure is kept in a persistent storage and is kept live under updates. Using a state of the art process mining technique, we show the feasibility of our approach. Our work is implemented in the process mining tool ProM using a relational database system as our persistent storage. Experiments are presented on real-life event data to compare the performance of the proposed approach with the state of the art.
KW - Incremental process discovery
KW - Live event data
KW - Recurrent process mining
UR - http://www.scopus.com/inward/record.url?scp=85041708555&partnerID=8YFLogxK
U2 - 10.1007/978-3-319-74030-0_13
DO - 10.1007/978-3-319-74030-0_13
M3 - Conference contribution
AN - SCOPUS:85041708555
SN - 978-3-319-74029-4
T3 - Lecture Notes in Business Information Processing
SP - 178
EP - 190
BT - Business Process Management Workshops
A2 - Teniente, E.
A2 - Weidlich, M.
PB - Springer
CY - Dordrecht
ER -