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 monitor, 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 challenges 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 first 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 defined 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.