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Process analytics is a collection of data-driven techniques for, among others, making predictions for individual process instances or overall process models. At the instance level, various novel techniques have been recently devised, tackling analytical tasks such as next activity, remaining time, or outcome prediction. However, there is a notable void regarding predictions at the process model level. It is the ambition of this article to fill this gap. More specifically, we develop a technique to forecast the entire process model from historical event data. A forecasted model is a will-be process model representing a probable description of the overall process for a given period in the future. Such a forecast helps, for instance, to anticipate and prepare for the consequences of upcoming process drifts and emerging bottlenecks. Our technique builds on a representation of event data as multiple time series, each capturing the evolution of a behavioural aspect of the process model, such that corresponding time series forecasting techniques can be applied. Our implementation demonstrates the feasibility of process model forecasting using real-world event data. A user study using our Process Change Exploration tool confirms the usefulness and ease of use of the produced process model forecasts.

Event sequence data is increasingly available in various application domains, such as business process management, software engineering, or medical pathways. Processes in these domains are typically represented as process diagrams or flow charts. So far, various techniques have been developed for automatically generating such diagrams from event sequence data. An open challenge is the visual analysis of drift phenomena when processes change over time. In this paper, we address this research gap. Our contribution is a system for fine-granular process drift detection and corresponding visualizations for event logs of executed business processes. We evaluated our system both on synthetic and real-world data. On synthetic logs, we achieved an average F-score of 0.96 and outperformed all the state-of-the-art methods. On real-world logs, we identified all types of process drifts in a comprehensive manner. Finally, we conducted a user study highlighting that our visualizations are easy to use and useful as perceived by process mining experts. In this way, our work contributes to research on process mining, event sequence analysis, and visualization of temporal data.

Business Process Management is a boundary-spanning discipline that aligns operational capabilities and technology to design and manage business processes. The Digital Transformation has enabled human actors, information systems, and smart products to interact with each other via multiple digital channels. The emergence of this hyper-connected world greatly leverages the prospects of business processes – but also boosts their complexity to a new level. We need to discuss how the BPM discipline can find new ways for identifying, analyzing, designing, implementing, executing, and monitoring business processes. In this research note, selected transformative trends are explored and their impact on current theories and IT artifacts in the BPM discipline is discussed to stimulate transformative thinking and prospective research in this field.

Process analytics is an umbrella of data-driven techniques which includes making predictions for individual process instances or overall process models. At the instance level, various novel techniques have been recently devised, tackling next activity, remaining time, and outcome prediction. At the model level, there is a notable void. It is the ambition of this paper to fill this gap. To this end, we develop a technique to forecast the entire process model from historical event data. A forecasted model is a will-be process model representing a probable future state of the overall process. Such a forecast helps to investigate the consequences of drift and emerging bottlenecks. Our technique builds on a representation of event data as multiple time series, each capturing the evolution of a behavioural aspect of the process model, such that corresponding forecasting techniques can be applied. Our implementation demonstrates the accuracy of our technique on real-world event log data.

Research on concept drift detection has inspired recent advancements of process mining and expanding the growing arsenal of process analysis tools. What has so far been missing in this new research stream are techniques that support comprehensive process drift analysis in terms of localizing, drillingdown, quantifying, and visualizing process drifts. In our research, we built on ideas from concept drift, process mining, and visualization research and present a novel web-based software tool to analyze process drifts, called Visual Drift Detection (VDD). Addressing the comprehensive analysis requirements, our tool is of benefit to researchers and practitioners in the business intelligence and process analytics area. It constitutes a valuable aid to those who are involved in business process redesign projects.

This paper presents a command-line tool, called Entropia, that implements a family of conformance checking measures for process mining founded on the notion of entropy from information theory. The measures allow quantifying classical non-deterministic and stochastic precision and recall quality criteria for process models automatically discovered from traces executed by IT-systems and recorded in their event logs. A process model has "good" precision with respect to the log it was discovered from if it does not encode many traces that are not part of the log, and has "good" recall if it encodes most of the traces from the log. By definition, the measures possess useful properties and can often be computed quickly.

The problem of automated discovery of process models from event logs has been intensely investigated in the past two decades, leading to a range of approaches that strike various trade-offs between accuracy, model complexity, and execution time. A few studies have suggested that the accuracy of automated process discovery approaches can be enhanced by using metaheuristic optimization. However, these studies have remained at the level of proposals without validation on real-life logs or they have only considered one metaheuristics in isolation. In this setting, this paper studies the following question: To what extent can the accuracy of automated process discovery approaches be improved by applying different optimization metaheuristics? To address this question, the paper proposes an approach to enhance automated process discovery approaches with metaheuristic optimization. The approach is instantiated to define an extension of a state-of-the-art automated process discovery approach, namely Split Miner. The paper compares the accuracy gains yielded by four optimization metaheuristics relative to each other and relative to state-of-the-art baselines, on a benchmark comprising 20 real-life logs. The results show that metaheuristic optimization improves the accuracy of Split Miner in a majority of cases, at the cost of execution times in the order of minutes, versus seconds for the base algorithm.

Recent research has introduced ideas from concept drift into process mining to enable the analysis of changes in business processes over time. This stream of research, however, has not yet addressed the challenges of drift categorization, drilling-down, and quantification. In this paper, we propose a novel technique for managing process drifts, called Visual Drift Detection (VDD), which fulfills these requirements. The technique starts by clustering declarative process constraints discovered from recorded logs of executed business processes based on their similarity and then applies change point detection on the identified clusters to detect drifts. VDD complements these features with detailed visualizations and explanations of drifts. Our evaluation, both on synthetic and real-world logs, demonstrates all the aforementioned capabilities of the technique.

Recent research has introduced ideas from concept drift into process mining to enable the analysis of changes in business processes over time. This stream of research, however, has not yet addressed the challenges of drift categorization, drilling-down, and quantification. In this tool demonstration paper, we present a novel software tool to analyze process drifts, called Visual Drift Detection (VDD), which fulfills these requirements. The tool is of benefit to the researchers and practitioners in the business intelligence and process analytics area, and can constitute a valuable aid to those who are involved in business process redesign endeavors.

Real-life event logs are typically much less structured and more complex than the predefined business activities they refer to. Most of the existing process mining techniques assume that there is a one-to-one mapping between process model activities and events recorded during process execution. Unfortunately, event logs and process model activities are defined at different levels of granularity. The challenges posed by this discrepancy can be addressed by means of log-lifting. In this work we develop a machine-learning-based framework aimed at bridging the abstraction level gap between logs and process models. The proposed framework operates of two main phases: log segmentation and machine-learning-based classification. The purpose of the segmentation phase is to identify the potential segment separators in a flow of low-level events, in which each segment corresponds to an unknown high-level activity. For this, we propose a segmentation algorithm based on maximum likelihood with n-gram analysis. In the second phase, event segments are mapped into their corresponding high-level activities using a supervised machine learning technique. Several machine learning classification methods are explored including ANNs, SVMs, and random forest. We demonstrate the applicability of our framework using a real-life event log provided by the SAP company. The results obtained show that a machine learning approach based on the random forest algorithm outperforms the other methods with an accuracy of 96.4%. The testing time was found to be around 0.01s, which makes the algorithm a good candidate for real-time deployment scenarios.

The higher education sector faces like most information-intensive industries an opportunity-rich, digital future. Nowadays, students demand contemporary, multi-channel learning experiences and fast evolving digital affordances provide universities with a growing design space for their future processes. Legislative changes, a globalizing market of learners and educational providers, and the emergence of new technology-based business models (EduTech) and legislative changes are further features of the current situation in this sector. In order to prepare for and to capitalize on this changing environment the Queensland University of Technology (QUT), like any university, needs to ensure operational inefficiencies are addressed as part of the required organisational transformation. However, traditional BPM approaches are often time-consuming and not tailored to immediate process transformation, meaning a new, dedicated and agile approach for QUT was needed.

(a) Situation faced: This case study is a unique example of a people-centric ICT-enabled BPM effort that overcame many challenges through steady championship fuelled by a multi-sectorial support network (local community, government agencies, private sector and institutes of higher education). Driven by a desire to make a difference, a weekly reputed regional hospital in Sri Lanka with chaotic, mundane, manual processes became a landmark success in its service efficiency and effectiveness via staged-continuous improvements, collaborative ideation, creative resource utilisation, and effective management of its “people” aspects. (b) Action taken: The project took a multi-staged people-centric approach. Major attitudinal change efforts with staff helped to build a unified internal workforce that was empowered to understand the patients’ needs. The hospital’s physical environment was transformed into a peaceful, pleasant atmosphere that was free of chaos. The entire patient-care-process was mapped, analysed, and transformed with IT enabled process improvements. A new patient records management system and a mobile-channeling system was implemented to eliminate long queues and increase the quality of patient care. Continued reviews and improvements are key in this case, as the vision to make a difference does not end with a single initiative. (c) Results achieved: The case illustrates how an ordinary government regional hospital’s patient-care pro-cess was transformed with the collective efforts of multi-stakeholder power. The reforms have enabled the hospital to increase the quality of patient care, enhance staff satisfaction, gain deep support, and get buy-in from higher authorities and the community. These process reform efforts enabled not only a one-off improvement initiative but a sustained success story that has received national and international attention. (d) Lessons learned: A key takeaway is how all of the enabling elements (champion

This paper discusses the integration of Nirdizati, a tool for predictive process monitoring, into the web-based process analytics platform Apromore. Through this integration, Apromore's users can use event logs stored in the Apromore repository to train a range of predictive models, and later use the trained models to predict various performance indicators of running process cases from a live event stream. For example, one can predict the remaining time or the next events until case completion, the case outcome, or the violation of compliance rules or internal policies. The predictions can be presented graphically via a dashboard that offers multiple visualization options, including a range of summary statistics about ongoing and past process cases. They can also be exported into a text file for periodic reporting or to be visualized in third-parties business intelligence tools. Based on these predictions, operations managers may identify potential issues early on, and take remedial actions in a timely fashion, e.g. reallocating resources from one case onto another to avoid that the case runs overtime.

(a) Situation faced: Processing injury-compensation claims, such as compulsory third party (CTP) claims, is complex, as it involves negotiations among multiple parties (e.g., claimants, insurers, law firms, health providers). Queensland’s CTP program is regulated by the Motor Accident Insurance Commission (MAIC). The Nominal Defendant, an arm of MAIC, determines liability for claims when the vehicle “at fault” is unregistered or unidentified and manages such claims from injured persons. While the relevant legislation mandates milestones for claims processing, the Nominal Defendant sees significant behavioral and performance variations in CTP claims processing, affecting the costs and durations of claims. The reasons for these variations are poorly understood. (b) Action taken: The BPM initiative took a process-mining approach that focused on the process identification, discovery, and analysis phases of the BPM Lifecycle. We undertook automated process discovery and comparative performance analysis with the aim of identifying where claims processing across cohorts of interest to the Nominal Defendant differed. In parallel, we conducted a context analysis with the aim of identifying the context factors that affect claim duration and cost. The personal injury literature and interviews with representative Nominal Defendant staff informed our selection of data attributes. (c) Results achieved: Process models were developed to facilitate comparative visualization of processes. The Nominal Defendant was particularly interested in differences in the processes for specific cohorts of claims: (i) overall claims; (ii) claims involving unregistered vehicles versus unidentified vehicles, and; (iii) direct claims versus legally represented claims. The model facilitated identification of aspects of claims processing where there were significant differences between cohorts. Data mining/feature selection techniques identified a set of process-relat

(a) Situation faced: Improving Emergency Department (ED) patient flows in terms of processing time, resource use, costs, and patient outcomes is a priority for health service professionals and is vital to the delivery of safe, timely, and effective patient care. Poor patient flows manifest as overcrowding in the ED, prolonged length of stay (LoS), patients “boarding” in EDs and “access block” for admission to inpatient wards. Consequences include poor patient outcomes, reduced access for new patients who present at the ED, and negative effects on staff, including dissatisfaction and stress. Further motivation for improving patient flows in EDs arises because Commonwealth- and state-sponsored financial incentives for hospitals are tied to achieving targets for improved patient access to emergency services. One measure of such improved access is meeting nationally agreed targets for the percentage of patients who are physically discharged from the ED within four hours of arrival. (b) Action taken: A key challenge in deriving evidence-based improvements for patient flows is that of gaining insight into the process factors and context factors that affect patient flows. The case study reported here adopted the BPM Lifecycle reference framework to improve patient flows. In particular we focused on the process identification, discovery, and analysis phases of the BPM Lifecycle. Process-oriented data-mining techniques were applied to real practices to discover models of current patient flows in the ED of St. Andrew’s War Memorial Hospital (SAWMH) in Queensland, Australia. The discovered models were used to evaluate the effect on patient flows of certain context factors of interest to stakeholders. Case histories of 1,473 chest pain presentations at SAWMH between September 2011 and March 2013 were analyzed to determine process differences be-tween ED patients with short stays (<4 hours) and those with long stays (>4 hours). (c) Results achieved

The comparison of the languages of software systems, i.e., their behaviours in terms of specified executions, is a prerequisite for many applications, reaching from system validation through management of a system's evolution to conformance checking of observed and expected behaviour. If two systems are not language-equivalent, the quantification of behavioural differences enables conclusions on the extent of deviation. Such quantifications are commonly done in a relative manner: A quotient is defined over some measure of two languages, which have potentially been derived via algebraic operations. However, there exists no systematic approach for defining quotients and it is unclear which measures enable meaningful comparisons of systems having infinite behaviours. This paper introduces a framework for defining language quotients. We instantiate the framework with cardinality-based and entropy-based measures to handle finite and infinite behaviours, and prove important properties of the quotients. We demonstrate application of quotients in the field of process mining to capture precision and recall between a log of recorded system executions and a model of expected system executions. An experimental evaluation of the quotients using our open-source implementation demonstrates their feasibility and indicates that the quotients enable a monotonic assessment, unlike state-of-the-art measures in process mining.

This textbook covers the entire Business Process Management (BPM) lifecycle, from process identification to process monitoring, covering along the way process modelling, analysis, redesign and automation. Concepts, methods and tools from business management, computer science and industrial engineering are blended into one comprehensive and inter-disciplinary approach. The presentation is illustrated using the BPMN industry standard defined by the Object Management Group and widely endorsed by practitioners and vendors worldwide. In addition to explaining the relevant conceptual background, the book provides dozens of examples, more than 230 exercises – many with solutions – and numerous suggestions for further reading. This second edition includes extended and completely revised chapters on process identification, process discovery, qualitative process analysis, process redesign, process automation and process monitoring. A new chapter on BPM as an enterprise capability has been added, which expands the scope of the book to encompass topics such as the strategic alignment and governance of BPM initiatives. The textbook is the result of many years of combined teaching experience of the authors, both at the undergraduate and graduate levels as well as in the context of professional training. Students and professionals from both business management and computer science will benefit from the step-by-step style of the textbook and its focus on fundamental concepts and proven methods. Lecturers will appreciate the class-tested format and the additional teaching material available on the accompanying website.