Clustering-Based Predictive Process Monitoring

TL;DR

This paper introduces a clustering-based framework for predictive process monitoring that estimates the likelihood of predicate fulfillment in ongoing cases by combining control flow clustering and event data classification.

Contribution

It presents a novel approach that integrates clustering of process prefixes with classifier training for improved prediction accuracy in process monitoring.

Findings

Framework effectively predicts predicate fulfillment in real-world logs.

Implementation in ProM demonstrates practical applicability.

Validated on hospital treatment data with promising results.

Abstract

Business process enactment is generally supported by information systems that record data about process executions, which can be extracted as event logs. Predictive process monitoring is concerned with exploiting such event logs to predict how running (uncompleted) cases will unfold up to their completion. In this paper, we propose a predictive process monitoring framework for estimating the probability that a given predicate will be fulfilled upon completion of a running case. The predicate can be, for example, a temporal logic constraint or a time constraint, or any predicate that can be evaluated over a completed trace. The framework takes into account both the sequence of events observed in the current trace, as well as data attributes associated to these events. The prediction problem is approached in two phases. First, prefixes of previous traces are clustered according to control flow information. Secondly, a classifier is built for each cluster using event data to discriminate between fulfillments and violations. At runtime, a prediction is made on a running case by mapping it to a cluster and applying the corresponding classifier. The framework has been implemented in the ProM toolset and validated on a log pertaining to the treatment of cancer patients in a large hospital.