Martin Kabierski

Process mining traditionally assumes centralized event data collection and analysis. However, modern Industrial Internet of Things systems increasingly operate over distributed, resource-constrained edge-cloud infrastructures. This paper proposes a structured approach for decentralizing process mining by enabling event data to be mined directly within the IoT systems edge-cloud continuum. We introduce ContinuumConductor a layered decision framework that guides when to perform process mining tasks such as preprocessing, correlation, and discovery centrally or decentrally. Thus, enabling privacy, responsive and resource-efficient process mining. For each step in the process mining pipeline, we analyze the trade-offs of decentralization versus centralization across these layers and propose decision criteria. We demonstrate ContinuumConductor at a real-world use-case of process optimazition in inland ports. Our contributions lay the foundation for computing-aware process mining in cyber-physical and IIoT systems.

Process analysis is concerned with analyzing recorded process executions to validate, monitor, or improve the underlying processes according to business goals. In this context, the paradigm of object-centric event data (OCED) has recently emerged, which relates activity executions to multiple objects instead of a single, pre-determined case. Since OCED can integrate various process perspectives simultaneously, it represents real-life activities more accurately than traditional event logs. Being the input of Object-Centric Process Mining (OCPM), the quality of the data recorded in OCED logs directly influences the results of the process analysis. To ensure reliable outcomes, it is imperative to assess potential quality problems manifesting in the data. While frameworks for such an assessment are available for classic event data, equivalent approaches for assessing quality issues in OCED do not yet exist. This paper provides an analysis and classification of data quality issues in OCED, and compares them to the issues in traditional event data. Thus, this study is a first step in the systematic assessment and management of data quality in OCED.

Online process analysis aims at identifying behavioral regularities or abnormalities in processes in near-real-time from continuous event streams. Yet, its realization is challenging, due to the requirements in terms of scalability and accuracy imposed by processes in Internet-of-Things environments. Against this background, this paper presents an approach for online process analysis that is based on standard models and systems for complex event processing (CEP). We present the “Detect and Conquer” approach that includes generic process templates to accurately capture behavioral regularities or deviations, which are then mapped to CEP queries to achieve their efficient evaluation. We evaluated our approach against synthetic and real-world datasets. The results demonstrate the feasibility and efficiency of our approach.

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.