Real-time Prediction and Synchronization of Business Process Instances using Data and Control Perspective

Document Type: Research Paper

Authors

1 Faculty of Computer and IT, Shahrood University of Technology, Shahrood, Iran

2 Faculty of Computer and IT, Shahrood University of Technology, Shahrood, Iran

3 Faculty of Computer Engineering and IT, Shahrood University of Technology, Shahrood, Iran

10.22075/ijnaa.2019.4065

Abstract

Nowadays, in a competitive and dynamic environment of businesses, organizations need to moni-
tor, analyze and improve business processes with the use of Business Process Management Systems
(BPMSs). Management, prediction and time control of events in BPMS is one of the major chal-
lenges of this area of research that has attracted lots of researchers. In this paper, we present a
4-phase pipeline approach to the problem of synchronizing each pair of dependent process instances
to arrive at the corresponding pair of tasks simultaneous or near-simultaneous. In the rst phase,
the process model is mined from the event log and enriched by the probabilistic distributions of
time information. In the second phase, the hidden processing dependency between the each pair of
dependent process instances is formally de ned and is mined from the event log. In the third phase,
a process state prediction algorithm is presented to predict the future route of process instance and
then predict the remaining time of the process instance to a given point in a predicted route of the
business process. In the fourth phase, an iterative synchronization algorithm is presented based on
the presented process state prediction algorithm to make each pair of dependent process instances
arrive at the corresponding pair of tasks simultaneous or near-simultaneous. Experimental results
on a real-life event log of BPI challenge 2012 show that the proposed method leads to 39% reduction
in cycle time for dependent process instances.

Keywords

International Journal of Nonlinear Analysis and Applications

Volume 10, Issue 1
Summer and Autumn 2019
Pages 217-228

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