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Process mining provides methods to analyse event logs generated by information systems during the execution of processes. It thereby supports the design, validation, and execution of processes in domains ranging from healthcare, through manufacturing, to e-commerce. To explore the regularities of flexible processes that show a large behavioral variability, it was suggested to mine recurrent behavioral patterns that jointly describe the underlying process. Existing approaches to behavioral pattern mining, however, suffer from two limitations. First, they show limited scalability as incremental computation is incorporated only in the generation of pattern candidates, but not in the evaluation of their quality. Second, process analysis based on mined patterns shows limited effectiveness due to an overwhelmingly large number of patterns obtained in practical application scenarios, many of which are redundant. In this paper, we address these limitations to facilitate the analysis of complex, flexible processes based on behavioral patterns. Specifically, we improve COBPAM, our initial behavioral pattern mining algorithm, by an incremental procedure to evaluate the quality of pattern candidates, optimizing thereby its efficiency. Targeting a more effective use of the resulting patterns, we further propose pruning strategies for redundant patterns and show how relations between the remaining patterns are extracted and visualized to provide process insights. Our experiments with diverse real-world datasets indicate a considerable reduction of the runtime needed for pattern mining, while a qualitative assessment highlights how relations between patterns guide the analysis of the underlying process.

Anonymization of event logs facilitates process mining while protecting sensitive information of process stakeholders. Existing techniques, however, focus on the privatization of the control-flow. Other process perspectives, such as roles, resources, and objects are neglected or subject to randomization, which breaks the dependencies between the perspectives. Hence, existing techniques are not suited for advanced process mining tasks, e.g., social network mining or predictive monitoring. To address this gap, we propose PMDG, a framework to ensure privacy for multi-perspective process mining through data generalization. It provides group-based privacy guarantees for an event log, while preserving the characteristic dependencies between the control-flow and further process perspectives. Unlike existin privatization techniques that rely on data suppression or noise insertion, PMDG adopts data generalization: a technique where the activities and attribute values referenced in events are generalized into more abstract ones, to obtain equivalence classes that are sufficiently large from a privacy point of view. We demonstrate empirically that PMDG outperforms state-of-the-art anonymization techniques, when mining handovers and predicting outcomes., Comment: 16 Pages

Process mining enables the analysis of complex systems using event data recorded during the execution of processes. Specifically, models of these processes can be discovered from event logs, i.e., sequences of events. However, the recorded events are often too fine-granular and result in unstructured models that are not meaningful for analysis. Log abstraction therefore aims to group together events to obtain a higher-level representation of the event sequences. While such a transformation shall be driven by the analysis goal, existing techniques force users to define how the abstraction is done, rather than what the result shall be. In this paper, we propose GECCO, an approach for log abstraction that enables users to impose requirements on the resulting log in terms of constraints. GECCO then groups events so that the constraints are satisfied and the distance to the original log is minimized. Since exhaustive log abstraction suffers from an exponential runtime complexity, GECCO also offers a heuristic approach guided by behavioral dependencies found in the log. We show that the abstraction quality of GECCO is superior to baseline solutions and demonstrate the relevance of considering constraints during log abstraction in real-life settings., Comment: 14 pages, 8 figures, accepted for IEEE ICDE 2022

Privacy-preserving process mining enables the analysis of business processes using event logs, while giving guarantees on the protection of sensitive information on process stakeholders. To this end, existing approaches add noise to the results of queries that extract properties of an event log, such as the frequency distribution of trace variants, for analysis.Noise insertion neglects the semantics of the process, though, and may generate traces not present in the original log. This is problematic. It lowers the utility of the published data and makes noise easily identifiable, as some traces will violate well-known semantic constraints.In this paper, we therefore argue for privacy preservation that incorporates a process semantics. For common trace-variant queries, we show how, based on the exponential mechanism, semantic constraints are incorporated to ensure differential privacy of the query result. Experiments demonstrate that our semantics-aware anonymization yields event logs of significantly higher utility than existing approaches.

Process performance indicators (PPIs) are metrics to quantify the degree with which organizational goals defined based on business processes are fulfilled. They exploit the event logs recorded by information systems during the execution of business processes, thereby providing a basis for process monitoring and subsequent optimization. However, PPIs are often evaluated on processes that involve individuals, which implies an inevitable risk of privacy intrusion. In this paper, we address the demand for privacy protection in the computation of PPIs. We first present a framework that enforces control over the data exploited for process monitoring. We then show how PPIs defined based on the established PPINOT meta-model are instantiated in this framework through a set of data release mechanisms. These mechanisms are designed to provide provable guarantees in terms of differential privacy. We evaluate our framework and the release mechanisms in a series of controlled experiments. We further use a public event log to compare our framework with approaches based on privatization of event logs. The results demonstrate feasibility and shed light on the trade-offs between data utility and privacy guarantees in the computation of PPIs.

Event logs capture the execution of business processes in terms of executed activities and their execution context. Since logs contain potentially sensitive information about the individuals involved in the process, they should be pre-processed before being published to preserve the individuals' privacy. However, existing techniques for such pre-processing are limited to a process' control-flow and neglect contextual information, such as attribute values and durations. This thus precludes any form of process analysis that involves contextual factors. To bridge this gap, we introduce PRIPEL, a framework for privacy-aware event log publishing. Compared to existing work, PRIPEL takes a fundamentally different angle and ensures privacy on the level of individual cases instead of the complete log. This way, contextual information as well as the long tail process behaviour are preserved, which enables the application of a rich set of process analysis techniques. We demonstrate the feasibility of our framework in a case study with a real-world event log.