Predictive Business Process Monitoring Research Articles

LS-ICE: A Load State Intercase Encoding framework for improved predictive monitoring of business processes

Research on developing techniques for predictive process monitoring has generally relied on feature encoding schemes that extract intra-case features from events to make predictions. In doing so, the processing of cases is assumed to be solely influenced by the attributes of the cases themselves. However, cases are not processed in isolation and can be influenced by the processing of other cases or, more generally, the state of the process under investigation. In this work, we propose the LS-ICE (load state intercase encoding) framework for encoding intercase features that enriches events with a depiction of the state of relevant load points in a business process. To assess the benefits of the intercase features generated using the LS-ICE framework, we compare the performance of predictive process monitoring models constructed using the encoded features against baseline models without these features. The models are evaluated for remaining trace and runtime prediction using five real-life event logs. Across the board, a consistent improvement in performance is noted for models that integrate intercase features encoded through the proposed framework, as opposed to baseline models that lack these encoded features.

Embedding Graph Convolutional Networks in Recurrent Neural Networks for Predictive Monitoring

Predictive monitoring of business processes is a subfield of process mining that aims to predict, among other things, the characteristics of the next event or the sequence of the next events. Although multiple approaches based on deep learning have been proposed, mainly recurrent neural networks and convolutional neural networks, none of them really exploit the structural information available in process models. This paper proposes an approach that simultaneously learns spatio-temporal information from both the event log and the process model by combining recurrent neural networks with graph convolutional networks. Thus, common patterns from process models, such as loops or parallels, can be learned while avoiding overwriting information during the encoding phase. An experimental evaluation of real-life event logs shows that our approach is more consistent and outperforms the current state-of-the-art approaches.

Modelling and Predictive Monitoring of Business Processes under Uncertainty with Reinforcement Learning.

The analysis of business processes based on their observed behavior recorded in event logs can be performed with process mining. This method can discover, monitor, and improve processes in various application domains. However, the process models produced by typical process discovery methods are difficult for humans to understand due to their high complexity (the so-called "spaghetti-like" process models). Moreover, these methods cannot handle uncertainty or perform predictions because of their deterministic nature. Recently, researchers have been developing predictive approaches for running business cases of processes. This paper focuses on developing a predictive business process monitoring approach using reinforcement learning (RL), which has been successful in other contexts but not yet explored in this area. The proposed approach is evaluated in the banking sector through a use case.

When to Invoke a Prediction Service for Business Process Monitoring?

Predictive monitoring of business processes aims at forecasting the future information of a business process and has gained increasing attention in recent years. With the development of cloud computing, prediction models, including remaining time prediction models, can be provided as cloud services. Reducing the number of invocation times of a remaining time prediction service is necessary since a large quantity of business process instances may be initiated and monitored every day. However, most of the current research focuses on designing new algorithms to improve prediction accuracy but there is no approach available to decide when to invoke a prediction service for each business process instance. In this paper, we propose a deep reinforcement learning based strategy that can learn the policies of selecting prediction points for the remaining time prediction. Specifically, the learned policies can dynamically decide the next prediction point at which the remaining time prediction service will be invoked for a business process instance. We performed extensive experiments on five real-world datasets. The experiment results show this strategy can reduce the number of prediction points significantly and still maintain the high prediction accuracy.

Predictive Business Process Monitoring Approach Based on Hierarchical Transformer

Predictive business process monitoring is predicting the next stage of the business process based on the sequence of events that have occurred in the business process instance, which is positive for promoting the rational allocation of resources and the improvement of execution efficiency. There are drawbacks in modeling business process instances, such as conceptual drift phenomenon and long event sequences. Therefore, we propose a hierarchical Transformer-based business process prediction model to improve the performance of the Transformer-based predictive business process monitoring model. We encode the event features using two different encoding methods to obtain the relationship between activities and attributes. A drift detection algorithm is proposed to segment the business process and calculate the correlation between activities and segments by using cross-attention. Furthermore, learnable position encoding is designed to capture the relative position information of subsequences. Finally, the information of different granularity, such as event attributes, event subsequences, and complete instances, is fused by different weights. Experiments were run on seven real event logs for the next activity prediction and remaining time prediction, and the next activity prediction accuracy improved by 6.32% on average, and the mean absolute error of remaining time prediction reduces by 21% on average.

Beyond descriptive taxonomies in data analytics: a systematic evaluation approach for data-driven method pipelines

Taxonomies can serve as a valuable tool to capture dimensions and characteristics of data analytics solutions in a structured manner and thus create transparency about different design options of the technical solution space. However, previous taxonomic approaches often remain at a purely descriptive level without leveraging morphological structures to investigate the mechanisms between different combinatorial options given in data analytics pipelines. To this end, we propose a taxonomic evaluation approach to evaluate and construct the technical core of analytical information systems more systematically. Specifically, we present a rough guidance model consisting of four steps, which we subsequently instantiate with two application scenarios from the fields of industrial maintenance and predictive business process monitoring. In this way, we demonstrate how taxonomic frameworks can guide the creation of structured evaluation studies to consider the construction and assessment of data analytics pipelines in a multi-perspective and holistic manner. Our approach is sufficiently generic to be applied to various domains, scenarios, and decision support tasks.

Multi-task prediction method of business process based on BERT and Transfer Learning

Predictive Business Process Monitoring (PBPM) is one of the essential tasks in Business Process Management (BPM). It aims to predict the future behavior of an ongoing case using completed cases of a process stored in the event log, such as the prediction of the next activity and outcome of the case, etc. Although various deep learning methods have been proposed for PBPM, none of them consider the simultaneous application to multiple predictive tasks. This paper proposes a multi-task prediction method based on BERT and Transfer Learning. First, the method performs the Masked Activity Model (MAM) of a self-supervised pre-training task on many unlabeled traces using BERT (Bidirectional Encoder Representations from Transformers). The pre-training task MAM captures the bidirectional semantic information of the input traces using the bidirectional Transformer structure in BERT. It obtains the long-term dependencies between activities using the Attention mechanism in the Transformer. Then, the universal representation model of the traces is obtained. Finally, two different models are defined for two prediction tasks of the next activity and the outcome of the case, respectively, and the pre-trained model is transferred to the two prediction models for training using the fine-tuning strategy. Experiments evaluation on eleven real-world event logs shows that the performance of the prediction tasks is affected by different masking tactics and masking probabilities in the pre-training task MAM. This method performs well in the next activity prediction task and the case outcome prediction task. It can be applied to several different prediction tasks faster and with more outstanding performance than the direct training method.

Deep Learning for Predictive Business Process Monitoring: Review and Benchmark

Predictive monitoring of business processes is concerned with the prediction of ongoing cases on a business process. Lately, the popularity of deep learning techniques has propitiated an ever-growing set of approaches focused on predictive monitoring based on these techniques. However, the high disparity of process logs and experimental setups used to evaluate these approaches makes it especially difficult to make a fair comparison. Furthermore, it also difficults the selection of the most suitable approach to solve a specific problem. In this paper, we provide both a systematic literature review of approaches that use deep learning to tackle the predictive monitoring tasks. In addition, we performed an exhaustive experimental evaluation of 10 different approaches over 12 publicly available process logs.

HAM-Net: Predictive Business Process Monitoring with a hierarchical attention mechanism

One of the essential tasks in Business Process Management (BPM) is Predictive Business Process Monitoring. This task aims to predict the behavior of an ongoing process based on the historical data stored in event logs. Since feed-forward neural networks do not consider the order of events for the prediction, they may not be helpful in predictive process monitoring. Recent research shows that using Recurrent Neural Networks such as LSTM and GRU may not be also helpful in predictive process monitoring. Because these networks use only the last hidden state as the context vector, and may lose some of past information, especially in long sequences. In addition, many existing approaches just use the activity name of each event as the representative of that event. In this context, they may ignore other events’ attributes in generating the feature vector. Some works have utilized these attributes simply by concatenating all of them together. While we need to use all event attributes to predict the next activity, it should be noted that not all of them are equally important. In this paper, we use two layers of attention mechanism on top of LSTM: (i) at the attribute level, to determine which attributes have more importance; and (ii) at the event level, to identify important events in predicting the next activity. Experimental evaluation of the real-world event logs showed that the use of hierarchical attention mechanisms in the proposed approach could effectively predict the next activity of an ongoing process.

Parallel-Structure Deep Learning for Prediction of Remaining Time of Process Instances

Event logs generated by Process-Aware Information Systems (PAIS) provide many opportunities for analysis that are expected to help organizations optimize their business processes. The ability to monitor business processes proactively can allow an organization to achieve, maintain or enhance competitiveness in the market. Predictive Business Process Monitoring (PBPM) can provide measures such as the prediction of the remaining time of an ongoing process instance (case) by taking past activities in running process instances into account, as based on the event logs of previously completed process instances. With the prediction provided, we expect that organizations can respond quickly to deviations from the desired process. In the context of the growing popularity of deep learning and the need to utilize heterogeneous representation of data; in this study, we derived a new deep-learning approach that utilizes two types of data representation based on a parallel-structure model, which consists of a convolutional neural network (CNN) and a multi-layer perceptron (MLP) with an embedding layer, to predict the remaining time. Conducting experiments with real-world datasets, we compared our proposed method against the existing deep-learning approach to confirm its utility for the provision of more precise prediction (as indicated by error metrics) relative to the baseline method.

A Multi-View Deep Learning Approach for Predictive Business Process Monitoring

The predictive business process monitoring is a family of online approaches to predict the unfolding of running traces based on the knowledge learned from historical event logs. In this article, we address the task of predicting the next trace activity from the completed events in a running trace. This is an important business capability as counting on accurate predictions of the future activities may allow companies to guarantee the higher utilization by acting proactively in anticipation. We propose a novel predictive process approach that couples multi-view learning and deep learning, in order to gain predictive accuracy by accounting for the variety of information possibly recorded in event logs. Experiments with various benchmark event logs prove the effectiveness of the proposed approach compared to several recent state-of-the-art methods.

An Empirical Investigation of Different Classifiers, Encoding, and Ensemble Schemes for Next Event Prediction Using Business Process Event Logs

There is a growing need for empirical benchmarks that support researchers and practitioners in selecting the best machine learning technique for given prediction tasks. In this article, we consider the next event prediction task in business process predictive monitoring, and we extend our previously published benchmark by studying the impact on the performance of different encoding windows and of using ensemble schemes. The choice of whether to use ensembles and which scheme to use often depends on the type of data and classification task. While there is a general understanding that ensembles perform well in predictive monitoring of business processes, next event prediction is a task for which no other benchmarks involving ensembles are available. The proposed benchmark helps researchers to select a high-performing individual classifier or ensemble scheme given the variability at the case level of the event log under consideration. Experimental results show that choosing an optimal number of events for feature encoding is challenging, resulting in the need to consider each event log individually when selecting an optimal value. Ensemble schemes improve the performance of low-performing classifiers in this task, such as SVM, whereas high-performing classifiers, such as tree-based classifiers, are not better off when ensemble schemes are considered.

Explainable predictive business process monitoring using gated graph neural networks

ABSTRACT Predictive business process monitoring (PBPM) is a class of techniques designed to forecast future behaviour of a running process instance or the value of process-related metrics like times and frequencies. PBPM systems support process workers and process managers in making operational decisions. State-of-the-art PBPM systems apply deep-learning techniques with multiple hidden layers to infer from data which makes it difficult for system users to understand why a prediction was made. However, the user needs to see deeper causes to identify intervention mechanisms that secure process performance. The main contribution of this paper is a technique that makes a prediction more explainable by visualising how much the different activities included in a process impacted the prediction. This work is the first to use gated graph neural networks (GGNNs) to make decisions more explainable, and it’s also GGNN’s first application to PBPM. We use a process event data set to demonstrate our approach.

Prediction of Incompliance With Business Goals With Business-Related Data and Context Data

Proactive adaptation of business processes can prevent and mitigate future problems during the execution of business processes. For the proactive adaptation of business processes, adaptation decisions should be made based on whether a business process instance is heading to a business goal. Recently, much work struggles to improve predictive business process monitoring efficiency, i.e., accuracy and prediction earliness. As such, process designers can find more necessary adaptations and have more remaining time for the adaptations. Almost all of these previous efforts concentrate on business-related data generated by business processes. However, besides business-related data, context data has a huge impact on how a business process instance is unfolding to its completion. Context data should be noticed in the area of predictive business process monitoring. To remedy this shortage, in this paper, we propose a predictive business process monitoring framework based on a joint of business-related data and context data to predict incompliance with business goals. We evaluated the framework concerning the measures of accuracy, prediction earliness, and cost time based on a real-life online banana purchase process.

Context-Aware Process Performance Indicator Prediction

It is well-known that context impacts running instances of a process. Thus, defining and using contextual information may help to improve the predictive monitoring of business processes, which is one of the main challenges in process mining. However, identifying this contextual information is not an easy task because it might change depending on the target of the prediction. In this paper, we propose a novel methodology named CAP3 (Context-aware Process Performance indicator Prediction) which involves two phases. The first phase guides process analysts on identifying the context for the predictive monitoring of process performance indicators (PPIs), which are quantifiable metrics focused on measuring the progress of strategic objectives aimed to improve the process. The second phase involves a context-aware predictive monitoring technique that incorporates the relevant context information as input for the prediction. Our methodology leverages context-oriented domain knowledge and experts’ feedback to discover the contextual information useful to improve the quality of PPI prediction with a decrease of error rates in most cases, by adding this information as features to the datasets used as input of the predictive monitoring process. We experimentally evaluated our approach using two-real-life organizations. Process experts from both organizations applied CAP3 methodology and identified the contextual information to be used for prediction. The model learned using this information achieved lower error rates in most cases than the model learned without contextual information confirming the benefits of CAP3.

Predicting performances in business processes using deep neural networks

Online operational support is gaining increasing interest due to the availability of real-time data and sufficient computing power, such as predictive business process monitoring. Predictive business process monitoring aims at providing timely information that enables proactive and corrective actions to improve process enactments and mitigate risks. There are a handful of research works focusing on the predictions at the instance level. However, it is more practical to predict the performance of processes at the process model level and detect potential weaknesses in the process to facilitate the proactive actions that will improve the process execution. Thus, in this paper, we propose a novel method to predict the future performances of a business process at the process model level. More in detail, we construct an annotated transition system and generate a process representation matrix from it. Based on the process representation matrix, we build performance prediction models using deep neural networks that consider both spatial and temporal dependencies present in the underlying business process. To validate the proposed method, we performed case studies on three real-life logs.

Specification-driven predictive business process monitoring

Predictive analysis in business process monitoring aims at forecasting the future information of a running business process. The prediction is typically made based on the model extracted from historical process execution logs (event logs). In practice, different business domains might require different kinds of predictions. Hence, it is important to have a means for properly specifying the desired prediction tasks, and a mechanism to deal with these various prediction tasks. Although there have been many studies in this area, they mostly focus on a specific prediction task. This work introduces a language for specifying the desired prediction tasks, and this language allows us to express various kinds of prediction tasks. This work also presents a mechanism for automatically creating the corresponding prediction model based on the given specification. Differently from previous studies, instead of focusing on a particular prediction task, we present an approach to deal with various prediction tasks based on the given specification of the desired prediction tasks. We also provide an implementation of the approach which is used to conduct experiments using real-life event logs.

Predictive Monitoring of Business Processes: A Survey

Nowadays, process mining is becoming a growing area of interest in business process management (BPM). Process mining consists in the extraction of information from the event logs of a business process. From this information, we can discover process models, monitor and improve our processes. One of the applications of process mining, is the predictive monitoring of business process. The aim of these techniques is the prediction of quantifiable metrics of a running process instance with the generation of predictive models. The most representative approaches for the runtime prediction of business process are summarized in this paper. The different types of computational predictive methods, such as statistical techniques or machine learning approaches, and certain aspects as the type of predicted values and quality evaluation metrics, have been considered for the categorization of these methods. This paper also includes a summary of the basic concepts, as well as a global overview of the process predictive monitoring area, that can be used to support future efforts of researchers and practitioners in this research field.

Genetic algorithms for hyperparameter optimization in predictive business process monitoring

Predictive business process monitoring aims at predicting the outcome of ongoing cases of a business process based on past execution traces. A wide range of techniques for this predictive task have been proposed in the literature. It turns out that no single technique, under a default configuration, consistently achieves the best predictive accuracy across all datasets. Thus, the selection and configuration of a technique needs to be done for each dataset. This paper presents a framework for predictive process monitoring that brings together a range of techniques, each with an associated set of hyperparameters. The framework incorporates two automatic hyperparameter optimization algorithms, which, given a dataset, select suitable techniques for each step in the framework and configure these techniques with minimal user input. The proposed framework and hyperparameter optimization algorithms have been evaluated on two real-life datasets and compared with state-of-the-art approaches for predictive business process monitoring. The results demonstrate the scalability of the approach and its ability to identify accurate and reliable framework configurations.

Predictive Business Process Monitoring with Tree-based Classification Algorithms

Predictive business process monitoring is a current research area which purpose is to predict the outcome of a whole process (or an element of a process i.e. a single event or task) based on available data. In the article we explore the possibility of use of the machine learning classification algorithms based on trees (CART, C5.0, random forest and extreme gradient boosting) in order to anticipate the result of a process. We test the application of these algorithms on real world event-log data and compare it with the known approaches. Our results show that.