Abstract
The ability to know in advance the trend of running process instances, with respect to different features, such as the expected completion time, would allow business managers to timely counteract to undesired situations, in order to prevent losses. Therefore, the ability to accurately predict future features of running business process instances would be a very helpful aid when managing processes, especially under service level agreement constraints. However, making such accurate forecasts is not easy: many factors may influence the predicted features. Many approaches have been proposed to cope with this problem but, generally, they assume that the underlying process is stationary. However, in real cases this assumption is not always true. In this work we present new methods for predicting the remaining time of running cases. In particular we propose a method, assuming process stationarity, which achieves state-of-the-art performances and two other methods which are able to make predictions even with non-stationary processes. We also describe an approach able to predict the full sequence of activities that a running case is going to take. All these methods are extensively evaluated on different real case studies.
| Original language | English |
|---|---|
| Pages (from-to) | 1005–1031 |
| Number of pages | 27 |
| Journal | Computing |
| Volume | 100 |
| Issue number | 9 |
| DOIs | |
| Publication status | Published - 1 Sep 2018 |
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Keywords
- Machine learning
- Prediction
- Process mining
- Remaining time
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Time and activity sequence prediction of business process instances. / Polato, Mirko; Sperduti, Alessandro; Burattin, Andrea; de Leoni, Massimiliano .
In: Computing, Vol. 100, No. 9, 01.09.2018, p. 1005–1031.Research output: Contribution to journal › Article › Academic › peer-review
TY - JOUR
T1 - Time and activity sequence prediction of business process instances
AU - Polato, Mirko
AU - Sperduti, Alessandro
AU - Burattin, Andrea
AU - de Leoni, Massimiliano
PY - 2018/9/1
Y1 - 2018/9/1
N2 - The ability to know in advance the trend of running process instances, with respect to different features, such as the expected completion time, would allow business managers to timely counteract to undesired situations, in order to prevent losses. Therefore, the ability to accurately predict future features of running business process instances would be a very helpful aid when managing processes, especially under service level agreement constraints. However, making such accurate forecasts is not easy: many factors may influence the predicted features. Many approaches have been proposed to cope with this problem but, generally, they assume that the underlying process is stationary. However, in real cases this assumption is not always true. In this work we present new methods for predicting the remaining time of running cases. In particular we propose a method, assuming process stationarity, which achieves state-of-the-art performances and two other methods which are able to make predictions even with non-stationary processes. We also describe an approach able to predict the full sequence of activities that a running case is going to take. All these methods are extensively evaluated on different real case studies.
AB - The ability to know in advance the trend of running process instances, with respect to different features, such as the expected completion time, would allow business managers to timely counteract to undesired situations, in order to prevent losses. Therefore, the ability to accurately predict future features of running business process instances would be a very helpful aid when managing processes, especially under service level agreement constraints. However, making such accurate forecasts is not easy: many factors may influence the predicted features. Many approaches have been proposed to cope with this problem but, generally, they assume that the underlying process is stationary. However, in real cases this assumption is not always true. In this work we present new methods for predicting the remaining time of running cases. In particular we propose a method, assuming process stationarity, which achieves state-of-the-art performances and two other methods which are able to make predictions even with non-stationary processes. We also describe an approach able to predict the full sequence of activities that a running case is going to take. All these methods are extensively evaluated on different real case studies.
KW - Machine learning
KW - Prediction
KW - Process mining
KW - Remaining time
UR - http://www.scopus.com/inward/record.url?scp=85042069635&partnerID=8YFLogxK
U2 - 10.1007/s00607-018-0593-x
DO - 10.1007/s00607-018-0593-x
M3 - Article
AN - SCOPUS:85042069635
VL - 100
SP - 1005
EP - 1031
JO - Computing
JF - Computing
SN - 0010-485X
IS - 9
ER -