Machine Learning for Predictive Business Process Monitoring

Lecture notes in networks and systems 715, 2022

The use of immense amounts of data on the execution of applications based on business processes can make it possible, thanks to Process Mining, to detect trends. Indeed, human intelligence in decisionmaking is enriched by Machine Learning in order to avoid bottlenecks, improve efficiency and highlight potential process improvements. In this research article, we present a method (BPETPM) for predictive monitoring of business processes. This method allows to predict the execution time of a business process according to the path followed by the process instance. It predicts whether a process instance will run in time or late. We follow the CRISP-DM approach, known in Data Science, to carry out our method. The input data for learning is extracted from the event logs saving the execution traces of the workflow engine of a BPMS. We start by cleaning data, adding additional attributes, and encoding categorical variables. Then, at the modelling level, we test six classification algorithms : KNN, SVM(kernel=linear), SVM(kernel=rbf), Decision Tree, Random Forest and Logestic Regression. Then, using the BPETPM method, we create an intelligent process management system (iBPMS4PET). This system is applied to a process for managing incoming mail in the mutual health sector.

2011

Business process prediction technologies are being increasingly used by organisations to provide timely feedback to their customers and improve their overall productivity. In order to provide valuable information to both customers and system managers, the timings of business processes need to be forecast with high accuracy and efficiency. In particular, organisations require to predict the process and event flows, recognize their patterns, and forecast the total time it would take for a workflow to complete, in order to meet the Service Level Agreements (SLAs) signed with the customers. In this paper, we focus on the prediction models that could be used for forecasting time to completion of business processes by analysing historical event logs. First, we propose a service oriented architecture that provides a test-bed for carrying out predictions on business processes. Second, we propose a Hidden Markov Model (HMM) based prediction technique that produces a model based on event logs, and compare it against existing prediction models. Finally, we describe an implementation of the system, where we simulate the execution of a business process and obtain predictions using both the proposed and existing prediction techniques.

The real-time prediction of business processes using historical event data is an important capability of modern business process monitoring systems. Existing process prediction methods are able to also exploit the data perspective of recorded events, in addition to the control-flow perspective. However, while well-structured numerical or categorical attributes are considered in many prediction techniques, almost no technique is able to utilize text documents written in natural language, which can hold information critical to the prediction task. In this paper, we illustrate the design, implementation, and evaluation of a novel text-aware process prediction model based on Long Short-Term Memory (LSTM) neural networks and natural language models. The proposed model can take categorical, numerical and textual attributes in event data into account to predict the activity and timestamp of the next event, the outcome, and the cycle time of a running process instance. Experiments show that the text-aware model is able to outperform state-of-the-art process prediction methods on simulated and real-world event logs containing textual data.

IEEE Transactions on Services Computing, 2021

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.

Context-Aware Predictive Process Monitoring: The Impact of News Sentiment: Confederated International Conferences: CoopIS, C&TC, and ODBASE 2018, Valletta, Malta, October 22-26, 2018, Proceedings, Part I, 2018

Predictive business process monitoring is concerned with forecasting how a process is likely to proceed, covering questions such as what is the next activity to expect and what is the remaining time until case completion. Process prediction typically builds on machine learning techniques that leverage past process execution data. A fundamental problem of a process prediction methods is the data acquisition. So far, research on predictive monitoring utilize data, which is internal to the process. In this paper, we present a novel approach of integrating the external context of the business processes into prediction methods. More specifically, we develop a technique that leverages the sentiments of online news for the task of remaining time prediction. Using our pro-totypical implementation, we carried out experiments that demonstrate the usefulness of this approach and allowing us to draw conclusions about circumstances in which it works best.

Lecture Notes in Computer Science, 2019

The prime concern for a business organization is to supply quality services to the customers without any delay or interruption so to establish a good reputation among the customer's and competitors. Ontime delivery of a customers order not only builds trust in the business organization but is also cost effective. Therefore, there is a need is to monitor complex business processes though automated systems which should be capable during execution to predict delay in processes so as to provide a better customer experience. This online problem has led us to develop an automated solution using machine learning algorithms so as to predict possible delay in business processes. The core characteristic of the proposed system is the extraction of generic process event log, graphical and sequence features, using the log generated by the process as it executes up to a given point in time where a prediction need to be made (referred to here as cut-off time); in an executing process this would generally be current time. These generic features are then used with Support Vector Machines, Logistic Regression, Naive Bayes and Decision trees to predict the data into on-time or delayed processes. The experimental results are presented based on real business processes evaluated using various metric performance measures such as accuracy, precision, sensitivity, specificity, F-measure and AUC for prediction as to whether the order will complete on-time when it has already been executing for a given period.

This introduction to the special issue on Business Process Intelligence (BPI) discusses the relation between data and processes. The recent attention for Big Data illustrates that organizations are aware of the potential of the torrents of data generated by today's information systems. However, at the same time, organizations are struggling to extract value from this overload of data. Clearly, there is a need for data scientists able to transform event data into actionable information. To do this, it is crucial to take a process perspective. The ultimate goal of BPI is not to improve information systems or the recording of data; instead the focus should be in improving the process. For example, we may want to aim at reducing costs, minimizing response times, and ensuring compliance. This requires a "confrontation" between process models and event data. Recent advances in process mining allow us to automatically learn process models showing the bottlenecks from "raw" event data. Moreover, given a normative model, we can use conformance checking to quantify and understand deviations. Automatically learned models may also be used for prediction and recommendation. BPI is rapidly developing as a field linking data science to business process management. This article aims to provide an overview thereby paving the way for the other contributions in this special issue.

WI2020 Zentrale Tracks

Predictive business process monitoring (PBPM) deals with predicting a process's future behavior based on historical event logs to support a process's execution. Many of the recent techniques utilize a machine-learned model to predict which event type is the next most likely. Beyond PBPM, prescriptive BPM aims at finding optimal actions based on considering relevant key performance indicators. Existing techniques are geared towards the outcome prediction and deal with alarms for interventions or interventions that do not represent process events. In this paper, we argue that the next event prediction is insufficient for practitioners. Accordingly, this research-in-progress paper proposes a technique for determining next best actions that represent process events. We conducted an intermediate evaluation to test the usefulness and the quality of our technique compared to the most frequently cited technique for predicting next events. The results show a higher usefulness for process participants than a next most likely event.

2013

Large service companies like telecommunication businesses run complex customer service processes in order to provide communication services to their customers. The flawless execution of these processes is essential since customer service is an important differentiator for these companies. They must also be able to predict if processes will complete successfully or run into exceptions in order to intervene at the right time, pre-empt problems and maintain customer service. Business process data is sequential in nature and can be very diverse. Thus, there is a need for an efficient sequential forecasting methodology that can cope with the diversity of the business data. In response to these requirements, in this paper we propose an approach which is a combination of KNN (K nearest neighbour) and sequence alignment for predicting process outcome. The proposed approach exploits temporal categorical features of the extracted data to predict the process outcomes using sequence alignment t...