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Information systems have been widely adopted to support service processes in various domains, e.g., in the telecommunication, finance, and health sectors. Information recorded by systems during the operation of these processes provides an angle for operational process analysis, commonly referred to as process mining. In this work, we establish a queueing perspective in process mining to address the online delay prediction problem, which refers to the time that the execution of an activity for a running instance of a service process is delayed due to queueing effects. We present predictors that treat queues as first-class citizens and either enhance existing regression-based techniques for process mining or are directly grounded in queueing theory. In particular, our predictors target multi-class service processes, in which requests are classified by a type that influences their processing. Further, we introduce queue mining techniques that derive the predictors from event logs recorded by an information system during process execution. Our evaluation based on large real-world datasets, from the telecommunications and financial sectors, shows that our techniques yield accurate online predictions of case delay and drastically improve over predictors neglecting the queueing perspective. [ABSTRACT FROM AUTHOR]

Event logs that are recorded by information systems provide a valuable starting point for the analysis of processes in various domains, reaching from healthcare, through logistics, to e-commerce. Specifically, behavioral patterns discovered from an event log enable operational insights, even in scenarios where process execution is rather unstructured and shows a large degree of variability. While such behavioral patterns capture frequently recurring episodes of a process’ behavior, they are not limited to sequential behavior but include notions of concurrency and exclusive choices. Existing algorithms to discover behavioral patterns are context-agnostic, though. They neglect the context in which patterns are observed, which severely limits the granularity at which behavioral regularities are identified. In this paper, we therefore present an approach to discover contextual behavioral patterns. Contextual patterns may be frequent solely in a certain partition of the event log, which enables fine-granular insights into the aspects that influence the conduct of a process. Moreover, we show how to analyze the discovered contextual behavioral patterns in terms of causal relations between context information and the patterns, as well as correlations between the patterns themselves. A complete analysis methodology leveraging all the tools presented in the paper and supplemented by interpretations guidelines is also provided. Finally, experiments with real-world event logs demonstrate the effectiveness of our techniques in obtaining fine-granular process insights. [ABSTRACT FROM AUTHOR]

Process mining provides a rich set of techniques to discover valuable knowledge of business processes based on data that was recorded in different types of information systems. It enables analysis of end‐to‐end processes to facilitate process re‐engineering and process improvement. Process mining techniques rely on the availability of data in the form of event logs. In order to enable process mining in diverse environments, the recorded data need to be located and transformed to event logs. The journey from raw data to event logs suitable for process mining can be addressed by a variety of methods and techniques, which are the focus of this article. In particular, techniques proposed in the literature to support the creation of event logs from raw data are reviewed and classified. This includes techniques for identification and extraction of the required event data from diverse sources as well as their correlation and abstraction. This article is categorized under:Technologies > Structure Discovery and ClusteringFundamental Concepts of Data and Knowledge > Data ConceptsTechnologies > Data Preprocessing [ABSTRACT FROM AUTHOR]

Event logs that originate from information systems enable comprehensive analysis of business processes. These logs serve as the starting point for the discovery of process models or the analysis of conformance of a log with a given specification. However, logs potentially contain personal information about individuals involved in process execution. In this paper, we therefore address the risk of privacy attacks on event logs. Specifically, we rely on group-based privacy guarantees instead of noise insertion in order to enable anonymization without adding new behaviour to the log. To this end, we propose two new algorithms for event log sanitization that provide privacy guarantees in terms of k-anonymity for the behavioural perspective of a process and t-closeness for sensitive information associated with events. The algorithms thereby avoid the disclosure of employee identities, prevent the identification of employee membership in the log, and preclude the characterization of employees based on sensitive attributes. Our algorithms overcome the limitations of an existing, greedy algorithm, providing users with a trade-off between computational complexity and the utility of the sanitized event log for downstream analysis. Our Experiments demonstrate that sanitization with our algorithms generates event logs of higher utility compared to the state of the art. [ABSTRACT FROM AUTHOR]

Information systems support the execution of business processes. As part of that, data about process execution is recorded in event logs, which can be used to analyse the control-flow of the respective processes. However, such data may contain personal information on process stakeholders that is protected by privacy regulations. Process analysis based on event logs shall, therefore, employ anonymization techniques. In this paper, we introduce two approaches to anonymize the recorded control-flow of a process. Specifically, we present SaCoFa and SaPa as two techniques to anonymize the result of trace-variant queries over an event log. Unlike existing techniques that achieve differential privacy through randomized noise insertion, our techniques rely on noise insertion mechanisms that incorporate a process' semantics, thereby avoiding easily-recognizable noise. Both techniques take different design choices, though. SaCoFa anonymizes a trace-variant distribution directly, thereby focusing on utility preservation at the expense of potentially changing the number of a traces in the result considerably. SaPa, in turn, anonymizes a trace-variant distribution indirectly, through play-out of an anonymized directly-follows distribution. This way, the number of traces in the result is close to the original log, but the drop in utility may become larger due to using only local control-flow information. However, our experiments demonstrate that both approaches strike a better balance of preserving the utility of an event log compared to existing techniques. • We propose methods to anonymize the control-flow perspective of business processes. • The anonymized data is protected by differential privacy. • Our techniques provide better utility for process discovery than the state of the art. [ABSTRACT FROM AUTHOR]

Abstract: Companies have to adhere to compliance requirements. The compliance analysis of business operations is typically a joint effort of business experts and compliance experts. Those experts need to create a common understanding of business processes to effectively conduct compliance management. In this paper, we present a technique that aims at supporting this process. We argue that process templates generated out of compliance requirements provide a basis for negotiation among business and compliance experts. We introduce a semi-automated and iterative approach to the synthesis of such process templates from compliance requirements expressed in Linear Temporal Logic (LTL). We show how generic constraints related to business process execution are incorporated and present criteria that point at underspecification. Further, we outline how such underspecification may be resolved to iteratively build up a complete specification. For the synthesis, we leverage existing work on process mining and process restructuring. However, our approach is not limited to the control-flow perspective, but also considers direct and indirect data-flow dependencies. Finally, we elaborate on the application of the derived process templates and present an implementation of our approach. [Copyright &y& Elsevier]