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

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 a4-phase pipeline approach to the problem of synchronizing each pair of dependent process instancesto 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 oftime information. In the second phase, the hidden processing dependency between the each pair ofdependent process instances is formally dened 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 andthen predict the remaining time of the process instance to a given point in a predicted route of thebusiness process. In the fourth phase, an iterative synchronization algorithm is presented based onthe presented process state prediction algorithm to make each pair of dependent process instancesarrive at the corresponding pair of tasks simultaneous or near-simultaneous. Experimental resultson a real-life event log of BPI challenge 2012 show that the proposed method leads to 39% reductionin cycle time for dependent process instances.