Accurate and Transparent Path Prediction Using Process Mining

Anticipating the next events of an ongoing series of activities has many compelling applications in various industries. It can be used to improve customer satisfaction, to enhance operational efficiency, and to streamline health-care services, to name a few. In this work, we propose an algorithm that predicts the next events by leveraging business process models obtained using process mining techniques. Because we are using business process models to build the predictions, it allows business analysts to interpret and alter the predictions. We tested our approach with more than 30 synthetic datasets as well as 6 real datasets. The results have superior accuracy compared to using neural networks while being orders of magnitude faster

Predictive Process Monitoring Methods: Which One Suits Me Best?

Predictive process monitoring has recently gained traction in academia and is maturing also in companies. However, with the growing body of research, it might be daunting for companies to navigate in this domain in order to find, provided certain data, what can be predicted and what methods to use. The main objective of this paper is developing a value-driven framework for classifying existing work on predictive process monitoring. This objective is achieved by systematically identifying, categorizing, and analyzing existing approaches for predictive process monitoring. The review is then used to develop a value-driven framework that can support organizations to navigate in the predictive process monitoring field and help them to find value and exploit the opportunities enabled by these analysis techniques

Predicting critical behaviors in business process executions: when evidence counts

Organizations need to monitor the execution of their processes to ensure they comply with a set of constraints derived, e.g., by internal managerial choices or by external legal requirements. However, preventive systems that enforce users to adhere to the prescribed behavior are often too rigid for real-world processes, where users might need to deviate to react to unpredictable circumstances. An effective strategy for reducing the risks associated with those deviations is to predict whether undesired behaviors will occur in running process executions, thus allowing a process analyst to promptly respond to such violations. In this work, we present a predictive process monitoring technique based on Subjective Logic. Compared to previous work on predictive monitoring, our approach allows to easily customize both the reliability and sensitivity of the predictive system. We evaluate our approach on synthetic data, also comparing it with previous work

Predicting Critical Behaviors in Business Process Executions: When Evidence Counts

Organizations need to monitor the execution of their processes to ensure they comply with a set of constraints derived, e.g., by internal managerial choices or by external legal requirements. However, preventive systems that enforce users to adhere to the prescribed behavior are often too rigid for real-world processes, where users might need to deviate to react to unpredictable circumstances. An effective strategy for reducing the risks associated with those deviations is to predict whether undesired behaviors will occur in running process executions, thus allowing a process analyst to promptly respond to such violations. In this work, we present a predictive process monitoring technique based on Subjective Logic. Compared to previous work on predictive monitoring, our approach allows to easily customize both the reliability and sensitivity of the predictive system. We evaluate our approach on synthetic data, also comparing it with previous work

Predictive business process monitoring with LSTM neural networks

Predictive business process monitoring methods exploit logs of completed cases of a process in order to make predictions about running cases thereof. Existing methods in this space are tailor-made for specific prediction tasks. Moreover, their relative accuracy is highly sensitive to the dataset at hand, thus requiring users to engage in trial-and-error and tuning when applying them in a specific setting. This paper investigates Long Short-Term Memory (LSTM) neural networks as an approach to build consistently accurate models for a wide range of predictive process monitoring tasks. First, we show that LSTMs outperform existing techniques to predict the next event of a running case and its timestamp. Next, we show how to use models for predicting the next task in order to predict the full continuation of a running case. Finally, we apply the same approach to predict the remaining time, and show that this approach outperforms existing tailor-made methods

Optimal paths in business processes: Framework and applications

We present an innovative framework for calculating optimal execution paths in business processes using the abstraction of workflow hypergraphs. We assume that information about the utility associated with the execution of activities in a process is available. Using the workflow hypergraph abstraction, finding a utility maximising path in a process becomes a generalised shortest hyperpath problem, which is NP-hard. We propose a solution that uses ant-colony optimisation customised to the case of hypergraph traversal. We discuss three possible applications of the proposed framework: process navigation, process simulation, and process analysis. We also present a brief performance evaluation of our solution and an example application

An experimental evaluation of the generalizing capabilities of process discovery techniques and black-box sequence models

A plethora of automated process discovery techniques have been developed which aim to discover a process model based on event data originating from the execution of business processes. The aim of the discovered process models is to describe the control-flow of the underlying business process. At the same time, a variety of sequence modeling techniques have been developed in the machine learning domain, which aim at finding an accurate, not necessarily interpretable, model describing sequence data. Both approaches ultimately aim to find a model that generalizes the behavior observed, i.e., they describe behavior that is likely to be part of the underlying distribution, whilst disallowing unlikely behavior. While the generalizing capabilities of process discovery algorithms have been studied before, a comparison, in terms of generalization, w.r.t. sequence models is not yet explored. In this paper we present an experimental evaluation of the generalizing capabilities of automated process discovery techniques and black-box sequence models, on the basis of next activity prediction. We compare a range of process discovery and sequence modeling techniques on a range of real-life datasets from the business process management domain. Our results indicate that LSTM neural networks more accurately describe previously unseen traces (i.e., test traces) than existing process discovery methods