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.

Deep Learning for Predictive Business Process Monitoring: Review and Benchmark.

Business processes have to be enacted with increasing numbers of instances and under a growing pressure to reduce the cost of process enactment. Cloud computing provides scalable computing resources as utility at low cost. Therefore, this paper introduces service systems as new, cloud-based architecture for the scalable and flexible enactment of business processes.

Enterprise, Business-Process and Information Systems Modeling

Hospitals are becoming increasingly aware of the need to improve their processes and data-driven approaches, such as process mining, are gaining attention. When applying process mining techniques in reality, it is widely recognized that real-life data tends to suffer from data quality problems. Consequently, thorough data quality assessment and data cleaning is required. This paper proposes an interactive data cleaning approach for process mining. It encompasses both data-based and discovery-based data quality assessment, showing that both are complementary. To illustrate some key elements of the proposed approach, a case study of an outpatient clinic’s appointment system is considered.

Interactive Data Cleaning for Process Mining: A Case Study of an Outpatient Clinic’s Appointment System

We propose an approach to identify anomalies in business processes by building an anomaly detector using graph encodings of process event log data coupled with graph autoencoders. We evaluate the proposed approach with randomly mutated real event logs as well as synthetic data. The evaluation shows significant performance improvements (in terms of F1 score) over previous approaches, in particular with respect to other types of autoencoders that use flat encodings of the same data. The performance improvements are also stable under training and evaluation noise. Our approach is generic in that it requires no prior knowledge of the business process.

Graph Autoencoders for Business Process Anomaly Detection

Detecting anomalies in business process event logs using statistical leverage

Low quality of business process event logs, as determined by anomalous and missing values, is often unavoidable in practical contexts. The output of process analysis that uses event logs with missing and anomalous values is also likely to be of low quality, thus decreasing the quality of any decisions based on it. While previous work has focused on reconstructing missing events in an event log or removing anomalous traces, in this paper we focus on detecting anomalous values and reconstructing missing values at the level of attributes in event logs. We propose methods based on autoencoders, which are a class of neural networks that can reconstruct their own input and are particularly suitable to learn a model of the complex relationships among attribute values in an event log. These methods do not rely on any a-priori knowledge about the business process that generated an event log and are evaluated using real world and artificially-generated event logs. The paper also discusses a qualitative analysis of the impact of event log cleaning and reconstruction on the output of process discovery. The proposed approach shows remarkable performance regarding activity labels and timestamps in artificial event logs. The performance in the case of real world event logs, in particular timestamp anomaly detection, is lower, which may be due to high variability of attribute values in the chosen event logs. Process models discovered from reconstructed event logs are characterised by lower variability of allowed behaviour and, therefore, are more usable in practice.

Autoencoders for improving quality of process event logs

While several techniques for detecting trace-level anomalies in event logs in offline settings have appeared recently in the literature, such techniques are currently lacking for online settings. Event log anomaly detection in online settings can be crucial for discovering anomalies in process execution as soon as they occur and, consequently, allowing to promptly take early corrective actions. This paper describes a novel approach to event log anomaly detection on event streams that uses statistical leverage. Leverage has been used extensively in statistics to develop measures to identify outliers and it has been adapted in this paper to the specific scenario of event stream data. The proposed approach has been evaluated on both artificial and real event streams.

pdf/online-anomaly-detection-using-statistical-leverage-for-tmv19x6488.pdf

Online Anomaly Detection Using Statistical Leverage for Streaming Business Process Events.

/pdf/business-process-event-log-anomaly-detection-based-on-5f3g4sqf50.pdf

Business Process Event Log Anomaly Detection based on Statistical Leverage.

We describe an application of process predictive monitoring at an outpatient clinic in a large hospital. A model is created to predict which patients will wrongly refer to the outpatient clinic, instead of directly to other departments, when returning to get treatment after an initial visit. Four variables are identified to minimise the cost of handling these patients: the cost of giving appropriate guidance to them, the cost of handling patients taking a non-compliant flow by wrongly referring to the outpatient clinic, and the false positive/negative rates of the predictive model adopted. The latter determine the situations in which patients have not received guidance when they should have had or have been guided even though not necessary, respectively. Using these variables, a cost model is built to identify which combinations of process intervention/redesign options and predictive models are likely to minimise the cost overhead of handling the returning patients.

Predicting Outpatient Process Flows to Minimise the Cost of Handling Returning Patients: A Case Study

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