NDLI: Data-aware remaining time prediction of business process instances

Content Provider	IEEE Xplore Digital Library
Author	Polato, M. Sperduti, A. Burattin, A. de Leoni, M.
Copyright Year	2014
Description	Author affiliation: Dept. of Math., Univ. of Padua, Padua, Italy (Polato, M.; Sperduti, A.; Burattin, A.) \|\| Dept. of Math. & Comput. Sci., Eindhoven Univ. of Technol., Eindhoven, Netherlands (de Leoni, M.)
Abstract	Accurate prediction of the completion time of a business process instance would constitute a valuable tool when managing processes under service level agreement constraints. Such prediction, however, is a very challenging task. A wide variety of factors could influence the trend of a process instance, and hence just using time statistics of historical cases cannot be sufficient to get accurate predictions. Here we propose a new approach where, in order to improve the prediction quality, both the control and the data flow perspectives are jointly used. To achieve this goal, our approach builds a process model which is augmented by time and data information in order to enable remaining time prediction. The remaining time prediction of a running case is calculated combining two factors: (a) the likelihood of all the following activities, given the data collected so far; and (b) the remaining time estimation given by a regression model built upon the data.
Starting Page	816
Ending Page	823
File Size	541752
Page Count	8
File Format	PDF
ISSN	21614407
e-ISBN	9781479914845
DOI	10.1109/IJCNN.2014.6889360
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2014-07-06
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Vectors Predictive models Data mining Business Gold Data models Indexes Support Vector Regression Data-aware Prediction Process mining Naive Bayes
Content Type	Text
Resource Type	Article

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