Abstract
Process discovery is crucial for understanding how business operations are performed and how to improve them. The opportunity to discover process models exists given that many systems underlying the execution of process steps log their execution times. However, there are many challenges to discover the actual processes particularly complex ones and without making unrealistic assumptions. In this paper we present a novel probabilistic-based approach to discover high quality process models of any complexity. The approach has a series of steps to discover links between nodes corresponding to execution dependencies between tasks and at the end it ranks these links according to their probabilities of actually existing and classifies them according to their type. In this paper we formulate the process discovery problem, describe the challenges and describe our solution.
| Original language | English |
|---|---|
| Title of host publication | ICDE Workshops 2011 - 2011 IEEE 27th International Conference on Data Engineering Workshops |
| Pages | 232-237 |
| Number of pages | 6 |
| DOIs | |
| Publication status | Published - 2011 |
| Externally published | Yes |
| Event | 2011 IEEE 27th International Conference on Data Engineering Workshops, ICDE 2011 - Hannover, Germany Duration: 11 Apr 2011 → 16 Apr 2011 |
Conference
| Conference | 2011 IEEE 27th International Conference on Data Engineering Workshops, ICDE 2011 |
|---|---|
| Country | Germany |
| City | Hannover |
| Period | 11.4.11 → 16.4.11 |
Fingerprint
ASJC Scopus subject areas
- Software
- Signal Processing
- Information Systems
Cite this
A probabilistic-based approach to process model discovery. / Castellanos, Malu; Casati, Fabio; Dayal, Umeshwar.
ICDE Workshops 2011 - 2011 IEEE 27th International Conference on Data Engineering Workshops. 2011. p. 232-237 5767637.Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
}
TY - GEN
T1 - A probabilistic-based approach to process model discovery
AU - Castellanos, Malu
AU - Casati, Fabio
AU - Dayal, Umeshwar
PY - 2011
Y1 - 2011
N2 - Process discovery is crucial for understanding how business operations are performed and how to improve them. The opportunity to discover process models exists given that many systems underlying the execution of process steps log their execution times. However, there are many challenges to discover the actual processes particularly complex ones and without making unrealistic assumptions. In this paper we present a novel probabilistic-based approach to discover high quality process models of any complexity. The approach has a series of steps to discover links between nodes corresponding to execution dependencies between tasks and at the end it ranks these links according to their probabilities of actually existing and classifies them according to their type. In this paper we formulate the process discovery problem, describe the challenges and describe our solution.
AB - Process discovery is crucial for understanding how business operations are performed and how to improve them. The opportunity to discover process models exists given that many systems underlying the execution of process steps log their execution times. However, there are many challenges to discover the actual processes particularly complex ones and without making unrealistic assumptions. In this paper we present a novel probabilistic-based approach to discover high quality process models of any complexity. The approach has a series of steps to discover links between nodes corresponding to execution dependencies between tasks and at the end it ranks these links according to their probabilities of actually existing and classifies them according to their type. In this paper we formulate the process discovery problem, describe the challenges and describe our solution.
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U2 - 10.1109/ICDEW.2011.5767637
DO - 10.1109/ICDEW.2011.5767637
M3 - Conference contribution
SN - 9781424491940
SP - 232
EP - 237
BT - ICDE Workshops 2011 - 2011 IEEE 27th International Conference on Data Engineering Workshops
ER -