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Service processes, for example in transportation, telecommunications or the health sector, are the backbone of today׳s economies. Conceptual models of service processes enable operational analysis that supports, e.g., resource provisioning or delay prediction. In the presence of event logs containing recorded traces of process execution, such operational models can be mined automatically. In this work, we target the analysis of resource-driven, scheduled processes based on event logs. We focus on processes for which there exists a pre-defined assignment of activity instances to resources that execute activities. Specifically, we approach the questions of conformance checking ( how to assess the conformance of the schedule and the actual process execution ) and performance improvement ( how to improve the operational process performance ). The first question is addressed based on a queueing network for both the schedule and the actual process execution. Based on these models, we detect operational deviations and then apply statistical inference and similarity measures to validate the scheduling assumptions, thereby identifying root-causes for these deviations. These results are the starting point for our technique to improve the operational performance. It suggests adaptations of the scheduling policy of the service process to decrease the tardiness (non-punctuality) and lower the flow time. We demonstrate the value of our approach based on a real-world dataset comprising clinical pathways of an outpatient clinic that have been recorded by a real-time location system (RTLS). Our results indicate that the presented technique enables localization of operational bottlenecks along with their root-causes, while our improvement technique yields a decrease in median tardiness and flow time by more than 20%. [ABSTRACT FROM AUTHOR]

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]

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]

Highlights • A technique for performance-driven model reduction of GSPNs is proposed. • The technique relies on foldings that aggregate or eliminate performance information. • Foldings preserve model stability and have a bound for the introduced performance estimation error. • Given a budget for the estimation error, an optimal sequence of foldings can be found. Abstract Operational process models such as generalised stochastic Petri nets (GSPNs) are useful when answering performance questions about business processes (e.g. ‘how long will it take for a case to finish?’). Recently, methods for process mining have been developed to discover and enrich operational models based on a log of recorded executions of processes, which enables evidence-based process analysis. To avoid a bias due to infrequent execution paths, discovery algorithms strive for a balance between over-fitting and under-fitting regarding the originating log. However, state-of-the-art discovery algorithms address this balance solely for the control-flow dimension, neglecting the impact of their design choices in terms of performance measures. In this work, we thus offer a technique for controlled performance-driven model reduction of GSPNs, using structural simplification rules, namely foldings. We propose a set of foldings that aggregate or eliminate performance information. We further prove the soundness of these foldings in terms of stability preservation and provide bounds on the error that they introduce with respect to the original model. Furthermore, we show how to find an optimal sequence of simplification rules, such that their application yields a minimal model under a given error budget for performance estimation. We evaluate the approach with two real-world datasets from the healthcare and telecommunication domains, showing that model simplification indeed enables a controlled reduction of model size, while preserving performance metrics with respect to the original model. Moreover, we show that aggregation dominates elimination when abstracting performance models by preventing under-fitting due to information loss. [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]

Conformance checking enables organizations to automatically assess whether their business processes are executed according to their specification. State-of-the-art conformance checking algorithms perform this task by establishing alignments between behaviour recorded by IT systems to a process model capturing desired behaviour. While such alignments clearly highlight conformance issues, a major downside is that these algorithms scale exponentially in the size of both the event data, capturing recorded behaviour, and the process model used as input. At the same time, it is crucial to recognize that event data used for such analyses typically only relates to a specific interval of process execution rather than the entire history, meaning that the employed event data is inherently incomplete. Therefore, we argue that statistical methods allow one to obtain a proper understanding of the overall conformance of a process by considering only a fraction of the available data. In this paper, we therefore present a statistical approach to conformance checking that employs trace sampling and result approximation in order to derive conformance results in an efficient manner. The approach reduces the runtime significantly, while still providing guarantees on the accuracy of the estimated conformance result. We instantiate the general approach for different measures of the overall conformance of an event log and a process model, including fitness as a direct quantification of conformance as well as the distribution of deviations over activities and deviations related to contextual factors, such as the involved resources. Moreover, to increase the robustness of our approach, we elaborate on mechanisms to reveal biases in sampling procedures. Experiments with real-world and synthetic datasets show that our approach speeds up state-of-the-art conformance checking algorithms by up to three orders of magnitude, while largely maintaining the analysis accuracy. • We propose sample and approximation-based techniques for conformance checking. • Cover three types of conformance measures, fitness, deviations, and resources. • Experiments show that runtime efficiency is increased by orders of magnitude. [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]