Business process models can be (re)constructed using event data recorded during the process’ execution. Similarly, event data can be used to verify conformance to prescribed behavior and to analyze and improve the underlying processes. However, not all events that are related to a process necessarily relate to its control-flow. Some events occur in the context of the process. In this work, we introduce the concept of context events to deal with these types of events. We show how distinguishing between contextual and control-flow events aids process discovery to obtain less complex process models. We demonstrate how visualizing context events on top of process models helps identify points in the process where context events occur often, aiding understanding. We analyze these benefits using two case studies involving real-life processes and event data.

In this work, we propose and apply a methodology for the management and optimization of clinical pathways using process mining. We adapt the Clinical Pathway Analysis Method (CPAM) by taking into consideration healthcare providers' needs. We successfully applied the methodology in the sepsis treatment of a major Brazilian hospital. Using data extracted from the hospital information system, a total of 5,184 deviations in the execution of the sepsis clinical pathway were discovered and categorized in 43 different types. We identified the process as it was actually executed, two bottlenecks, and significant differences in performance in cases that deviated from the prescribed clinical pathway. Furthermore, factors such as patient age, gender, and type of infection were shown to affect performance. The analysis results were validated by an expert panel of clinical professionals and verified to provide valuable, actionable insights. Based on these insights, we were able to suggest optimization points in the sepsis clinical pathway.

Many business processes are supported by information systems that record their execution. Process mining techniques extract knowledge and insights from such process execution data typically stored in event logs or streams. Most process mining techniques focus on process discovery (the automated extraction of process models) and conformance checking (aligning observed and modeled behavior). Existing process performance analysis techniques typically rely on ad-hoc definitions of performance. This paper introduces a novel comprehensive approach to process performance analysis from event data. Our generic technique centers around business artifacts, key conceptual entities that behave according to state-based transactional lifecycle models. We present a formalization of these concepts as well as a structural approach to calculate and monitor process performance from event data. The approach has been implemented in the open source process mining tool ProM and its applicability has been evaluated using public real-life event data.

This document introduces the XES artifact lifecycle extension. It allows for events to relate to transitions in the lifecycles of multiple business artifacts, which are key conceptual entities that behave according to state-based transactional lifecycle models. We present a definition of the concepts of the extension, discuss how it relates to the standard lifecycle extension, and provide an example event log.

Business processes often exhibit a high degree of variability. Process variants may manifest due to the differences in the nature of clients, heterogeneity in the type of cases, etc. Through the use of process mining techniques, one can benefit from historical event data to extract non-trivial knowledge for improving business process performance. Although some research has been performed on supporting process comparison within the process mining context, applying process comparison in practice is far from trivial. Considering all comparable attributes, for example, leads to an exponential number of possible comparisons. In this paper we introduce a novel methodology for applying process comparison in practice. We successfully applied the methodology in a case study within Xerox Services, where a forms handling process was analyzed and actionable insights were obtained by comparing different process variants using event data.

Real-life business processes are complex and often exhibit a high degree of variability. Additionally, due to changing conditions and circumstances, these processes continuously evolve over time. For example, in the healthcare domain, advances in medicine trigger changes in diagnoses and treatment processes. Case data (e.g. treating physician, patient age) also influence how processes are executed. Existing process mining techniques assume processes to be static and therefore are less suited for the analysis of contemporary, flexible business processes. This paper presents a novel comparative case clustering approach that is able to expose changes in behavior. Valuable insights can be gained and process improvements can be made by finding those points in time where behavior changed and the reasons why. Evaluation using both synthetic and real-life event data shows our technique can provide these insights.

Process discovery algorithms typically aim at discovering process models from event logs. Most algorithms achieve this by solely using an event log, without allowing the domain expert to influence the discovery in any way. However, the user may have certain domain expertise which should be exploited to create better process models. In this paper, we address this issue of incorporating domain knowledge to improve the discovered process model. First, we present a verification algorithm to verify the presence of certain constraints in a process model. Then, we present three modification algorithms to modify the process model. The outcome of our approach is a Pareto front of process models based on the constraints specified by the domain expert and common quality dimensions of process mining.

In a typical healthcare setting, specific clinical care pathways can be defined by the hospitals. Process mining provides a way of analyzing the care pathways by analyzing the event data extracted from the hospital information systems. Process mining can be used to optimize the overall care pathway, and gain interesting insights into the actual execution of the process, as well as to compare the expectations versus the reality. In this paper, a generic novel tool called InterPretA, is introduced which builds upon pre-existing process mining and visual analytics techniques to enable the user to perform such process oriented analysis. InterPretA contains a set of options to provide high level conformance analysis of a process from different perspectives. Furthermore, InterPretA enables detailed investigative analysis by letting the user interactively analyze, visualize and explore the execution of the processes from the data perspective.

Business process performance may be affected by a range of factors, such as the volume and characteristics of ongoing cases or the performance and availability of individual resources. Event logs collected by modern information systems provide a wealth of data about the execution of business processes. However, extracting root causes for performance issues from these event logs is a major challenge. Processes may change continuously due to internal and external factors. Moreover, there may be many resources and case attributes influencing performance. This paper introduces a novel approach based on time series analysis to detect cause-effect relations between a range of business process characteristics and process performance indicators. The scalability and practical relevance of the approach has been validated by a case study involving a real-life insurance claims handling process.

Process mining combines model-based process analysis with data-driven analysis techniques. The role of process mining is to extract knowledge and gain insights from event logs. Most existing techniques focus on process discovery (the automated extraction of process models) and conformance checking (aligning observed and modeled behavior). Relatively little research has been performed on the analysis of business process performance. Cooperative business processes often exhibit a high degree of variability and depend on many factors. Finding root causes for inefficiencies such as delays and long waiting times in such flexible processes remains an interesting challenge. This paper introduces a novel approach to analyze key process performance indicators by considering the process context. A generic context-aware analysis framework is presented that analyzes performance characteristics from multiple perspectives. A statistical approach is then utilized to evaluate and find significant differences in the results. Insights obtained can be used for finding high-impact points for optimization, prediction, and monitoring. The practical relevance of the approach is shown in a case study using real-life data.

Real-life business processes are complex and show a high degree of variability. Additionally, due to changing conditions and circumstances, these processes continuously evolve over time. For example, in the healthcare domain, advances in medicine trigger changes in diagnoses and treatment processes. Besides changes over time, case data (e.g. treating physician, patient age) also influence how processes are executed. Existing process mining techniques assume processes to be static and therefore are less suited for the analysis of contemporary flexible business processes. This paper presents a novel comparative trace clustering approach that is able to expose changes in behavior. Valuable insights can be gained and process improvements can be made by finding those points in time where behavior changed and the reasons why. Evaluation on real-life event data shows our technique can provide these insights.

Event data can be found in any information system and provide the starting point for a range of process mining techniques. The widespread availability of large amounts of event data also creates new challenges. Existing process mining techniques are often unable to handle "big event data" adequately. Decomposed process mining aims to solve this problem by decomposing the process mining problem into many smaller problems which can be solved in less time, using less resources, or even in parallel. Many decomposed process mining techniques have been proposed in literature. Analysis shows that even though the decomposition step takes a relatively small amount of time, it is of key importance in finding a high-quality process model and for the computation time required to discover the individual parts. Currently there is no way to assess the quality of a decomposition beforehand. We define three quality notions that can be used to assess a decomposition, before using it to discover a model or check conformance with. We then propose a decomposition approach that uses these notions and is able to find a high-quality decomposition in little time.

Information systems supporting business processes generate event data which provide the starting point for a range of process mining techniques. Lion's share of real-life processes are complex and ad-hoc, which creates problems for traditional process mining techniques, that cannot deal with such unstructured processes. Finding mainstream and deviating cases in such data is problematic, since most cases are unique and therefore determining what is normal or exceptional may depend on many factors. Trace clustering aims to group similar cases in order to find variations of the process and to gain novel insights into the process at hand. However, few trace clustering techniques take the context of the process into account and focus on the control-flow perspective only. Outlier detection techniques provide only a binary distinction between normal and exceptional behavior, or depend on a normative process model to be present. As a result, existing techniques are less suited for processes with a high degree of variability. In this paper, we introduce a novel trace clustering technique that is able to find process variants as well as deviating behavior based on a set of selected perspectives. Evaluation on both artificial and real-life event data reveals that additional insights can consequently be achieved.