In complex processes, various events can happen in different sequences. The prediction of the next event activity given an a-priori process state is of importance in such processes. Recent methods leverage deep learning techniques such as recurrent neural networks to predict event activities from raw process logs. However, deep learning techniques cannot efficiently model logical behaviors of complex processes. In this paper, we take advantage of Petri nets as a powerful tool in modeling logical behaviors of complex processes. We propose an approach which first discovers Petri nets from event logs utilizing a recent process mining algorithm. In a second step, we enhance the obtained model with time decay functions to create timed process state samples. Finally, we use these samples in combination with token movement counters and Petri net markings to train a deep learning model that predicts the next event activity. We demonstrate significant performance improvements and outperform the state-of-the-art methods on eight out of nine real-world benchmark event logs in accuracy.
The application of deep learning on predictive business process mining has grown enormously during recent years. Researchers have shown the applicability of machine and deep learning on several target variables such as the remaining time of running cases @cite_50 , forecasting time of events @cite_26 , and predicting upcoming events in running processes while utilizing a-priori knowledge @cite_51 . The prediction of event activities can be considered as a classification problem in which the probability of a next event activity @math given the state @math of the process at time @math , @math , is to be found.
@cite_26 @cite_51 @cite_50
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Predicting the final state of a running process, the remaining time to completion or the next activity of a running process are important aspects of runtime process management. Runtime management requires the ability to identify processes that are at risk of not meeting certain criteria in order to offer case managers decision information for timely intervention. This in turn requires accurate prediction models for process outcomes and for the next process event, based on runtime information available at the prediction and decision point. In this paper, we describe an initial application of deep learning with recurrent neural networks to the problem of predicting the next process event. This is both a novel method in process prediction, which has previously relied on explicit process models in the form of Hidden Markov Models (HMM) or annotated transition systems, and also a novel application for deep learning methods. Abstract Predictive business process monitoring methods use datasets of completed cases related to a process to predict the behaviour of running cases. To handle the large amounts of available event data, recent works have turned to deep learning techniques and have achieved fairly accurate results. However, results from these techniques are often difficult to interpret and explain. In the area of Recommender systems, factorization models have been an important class of predictive techniques due to its scalability and ability to infer latent features. Motivated by research in Recommender systems, this paper presents a predictive model that combines matrix factorization techniques from Recommender systems and knowledge from Business Process Management to learn interactions between latent features that can be used to predict the next event of an ongoing case. Evaluation on two real-life datasets from a Dutch Financial Institute and Volvo IT Belgium shows that the approach yields results that are comparable and at times superior to state-of-the-art techniques such as neural networks, yielding at most a precision of 0.87 for next event predictions. Abstract Predicting business process behaviour is an important aspect of business process management. Motivated by research in natural language processing, this paper describes an application of deep learning with recurrent neural networks to the problem of predicting the next event in a business process. This is both a novel method in process prediction, which has largely relied on explicit process models, and also a novel application of deep learning methods. The approach is evaluated on two real datasets and our results surpass the state-of-the-art in prediction precision. The enactment of business processes is generally supported by information systems that record data about each process execution (a.k.a. case). This data can be analyzed via a family of methods broadly known as process mining. Predictive process monitoring is a process mining technique concerned with predicting 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 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 (completed) cases are clustered according to control flow information. Secondly, a classifier is built for each cluster using event data attributes to discriminate between cases that lead to a fulfillment of the predicate under examination and cases that lead to a violation within the cluster. 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.