Time and activity sequence prediction of business process instances

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 all of them assume that the underling 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 outperforms the state-of-the-art and two other methods which are able to make predictions even with nonstationary 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 two real case studies.

Specification-driven predictive business process monitoring

“…In the literature, there have been several works focusing on predicting time-related properties of running processes. The works by [3,4,54,55,52,53] focus on predicting the remaining processing time. In [3,4], the authors present an approach for predicting the remaining processing time based on annotated transition system that contains time information extracted from event logs.…”

“…Another major stream of works tackle the problem of predicting the future activities/events of a running process (cf. [63,26,27,23,40,15,53]). The works by [63,26,27,23,40] use deep learning approach for predicting the future events, e.g., the next event of the current running process.…”

“…Through predictive analysis, potential future problems can be detected and preventive actions can be taken in order to avoid unexpected situation, e.g., processing delay and Service-Level Agreement (SLA) violations. Many studies have been conducted in order to deal with various prediction tasks such as predicting the remaining processing time [4,63,54,52,53], predicting the outcomes of a process [37,22,67,50], predicting future events [23,63,27], etc (cf. [43,42,58,49,15,19]).…”

Event Entry Time Prediction in Financial Business Processes Using Machine Learning - A Use Case From Loan Applications

“…The majority of research concerning this process prediction focuses on predicting the result of a process or the next step in a process ( fig. 1), where there are multiple possibilities available [23,24,25]. Evermann et al [26] transferred the applicability of neural networks from Natural Language Processing to the domain of Next…”

Cross-domain graph based similarity measurement of workflows

“…The extraction of useful information out of large amount of data is the main challenge of the data mining field. To tackle this challenge, we propose a process mining [27] method based on transfer learning [28]. Process mining aims at discovering, analyzing, and extending formal models of process revealing the real process in a system, using the event log containing the footprints of real process executions [29].…”