Encoding High-Level Control-Flow Construct Information for Process Outcome Prediction

Outcome-oriented predictive process monitoring aims at classifying a running process execution according to a given set of categorical outcomes, leveraging data on past process executions. Most previous studies employ Recurrent Neural Networks to encode the sequence of events, without taking the structure of the process into account. However, process executions typically involve complex control-flow constructs, like parallelism and loops. Different executions of these constructs can be recorded as different event sequences in the event log. This makes it challenging for a recurrent classifier to detect potential relations between a high-level control-flow construct and the prediction target. This is especially true in the presence of high variability in process executions and lack of data. In this paper, we propose a novel approach which encodes the control-flow construct each event belongs to. First, we exploit Local Process Model mining techniques to extract frequently occurring control-flow patterns from the event log. Then, we employ different encoding techniques to enrich an on-going process execution with information related to the extracted control-flow patterns. We tested the proposed method on nine real-life event logs. The obtained results show consistent improvements in the prediction performance

Event Log Sampling for Predictive Monitoring

Predictive process monitoring is a subfield of process mining that aims to estimate case or event features for running process instances. Such predictions are of significant interest to the process stakeholders. However, state-of-the-art methods for predictive monitoring require the training of complex machine learning models, which is often inefficient. This paper proposes an instance selection procedure that allows sampling training process instances for prediction models. We show that our sampling method allows for a significant increase of training speed for next activity prediction methods while maintaining reliable levels of prediction accuracy.Comment: 7 pages, 1 figure, 4 tables, 34 reference

Performance-preserving event log sampling for predictive monitoring

Predictive process monitoring is a subfield of process mining that aims to estimate case or event features for running process instances. Such predictions are of significant interest to the process stakeholders. However, most of the state-of-the-art methods for predictive monitoring require the training of complex machine learning models, which is often inefficient. Moreover, most of these methods require a hyper-parameter optimization that requires several repetitions of the training process which is not feasible in many real-life applications. In this paper, we propose an instance selection procedure that allows sampling training process instances for prediction models. We show that our instance selection procedure allows for a significant increase of training speed for next activity and remaining time prediction methods while maintaining reliable levels of prediction accuracy

Interactive Multi-interest Process Pattern Discovery

Process pattern discovery methods (PPDMs) aim at identifying patterns of interest to users. Existing PPDMs typically are unsupervised and focus on a single dimension of interest, such as discovering frequent patterns. We present an interactive multi-interest-driven framework for process pattern discovery aimed at identifying patterns that are optimal according to a multi-dimensional analysis goal. The proposed approach is iterative and interactive, thus taking experts’ knowledge into account during the discovery process. The paper focuses on a concrete analysis goal, i.e., deriving process patterns that affect the process outcome. We evaluate the approach on real-world event logs in both interactive and fully automated settings. The approach extracted meaningful patterns validated by expert knowledge in the interactive setting. Patterns extracted in the automated settings consistently led to prediction performance comparable to or better than patterns derived considering single-interest dimensions without requiring user-defined thresholds

Encoding High-Level Control-Flow Construct Information for Process Outcome Prediction

Outcome-oriented predictive process monitoring aims at classifying a running process execution according to a given set of categorical outcomes, leveraging data on past process executions. Most previous studies employ Recurrent Neural Networks to encode the sequence of events, without taking the structure of the process into account. However, process executions typically involve complex control-flow constructs, like parallelism and loops. Different executions of these constructs can be recorded as different event sequences in the event log. This makes it challenging for a recurrent classifier to detect potential relations between a high-level control-flow construct and the prediction target. This is especially true in the presence of high variability in process executions and lack of data. In this paper, we propose a novel approach which encodes the control-flow construct each event belongs to. First, we exploit Local Process Model mining techniques to extract frequently occurring control-flow patterns from the event log. Then, we employ different encoding techniques to enrich an on-going process execution with information related to the extracted control-flow patterns. We tested the proposed method on nine real-life event logs. The obtained results show consistent improvements in the prediction performance

Interactive Multi-interest Process Pattern Discovery

Process pattern discovery methods (PPDMs) aim at identifying patterns of interest to users. Existing PPDMs typically are unsupervised and focus on a single dimension of interest, such as discovering frequent patterns. We present an interactive multi-interest-driven framework for process pattern discovery aimed at identifying patterns that are optimal according to a multi-dimensional analysis goal. The proposed approach is iterative and interactive, thus taking experts’ knowledge into account during the discovery process. The paper focuses on a concrete analysis goal, i.e., deriving process patterns that affect the process outcome. We evaluate the approach on real-world event logs in both interactive and fully automated settings. The approach extracted meaningful patterns validated by expert knowledge in the interactive setting. Patterns extracted in the automated settings consistently led to prediction performance comparable to or better than patterns derived considering single-interest dimensions without requiring user-defined thresholds

Event Log Sampling for Predictive Monitoring

Predictive process monitoring is a subfield of process mining that aims to estimate case or event features for running process instances. Such predictions are of significant interest to the process stakeholders. However, state-of-the-art methods for predictive monitoring require the training of complex machine learning models, which is often inefficient. This paper proposes an instance selection procedure that allows sampling training process instances for prediction models. We show that our sampling method allows for a significant increase of training speed for next activity prediction methods while maintaining reliable levels of prediction accuracy