Process-oriented data mining (process mining) uses algorithms and data (in the form of event logs) to construct models that aim to provide insights into organisational processes. The quality of the data (both form and content) presented to the modeling algorithms is critical to the success of the process mining exercise. Cleaning event logs to address quality issues prior to conducting a process mining analysis is a necessary, but generally tedious and ad hoc task. In this paper we describe a set of data quality issues, distilled from our experiences in conducting process mining analyses, commonly found in process mining event logs or encountered while preparing event logs from raw data sources. We show that patterns are used in a variety of domains as a means for describing commonly encountered problems and solutions. The main contributions of this article are in showing that a patterns-based approach is applicable to documenting commonly encountered event log quality issues, the formulation of a set of components for describing event log quality issues as patterns, and the description of a collection of 11 event log imperfection patterns distilled from our experiences in preparing event logs. We postulate that a systematic approach to using such a pattern repository to identify and repair event log quality issues benefits both the process of preparing an event log and the quality of the resulting event log. The relevance of the pattern-based approach is illustrated via application of the patterns in a case study and through an evaluation by researchers and practitioners in the field.

Process mining analyzes process execution data to derive insights that support operational process improvement. However, event logs often suffer from poor data quality, typically resulting from process deficiencies, which can lead to inaccurate or misleading insights. To mitigate this risk, domain experts and process analysts engage in data validation during event data preparation to assess whether an event log is fit for its intended analytical purpose. Yet, current practices often fail to sufficiently align event logs with their analytical objectives, commonly formalized as analysis questions. This misalignment impedes the detection of data quality issues, which frequently vary across application domains and analytical contexts. Generative artificial intelligence offers promising capabilities in this regard, including adaptability to diverse contexts, the ability to interpret complex data, and the generation of context-aware recommendations. To leverage this potential, we adopt the Design Science Research paradigm to iteratively develop Artificial Intelligence-Assisted Data Validation For Domain Experts (AID4DE) that integrates domain knowledge — rooted in experts’ practical engagement with operational processes — with generative artificial intelligence support to facilitate interaction with complex event log data. We instantiate AID4DE as an open-source software prototype and evaluate it through a three-phase approach: a competing artifact analysis, 14 semi-structured expert interviews, and a user study involving 18 information systems researchers. Our results show that AID4DE is both applicable and effective in supporting domain experts in data validation, enabling the systematic externalization of domain knowledge and rigorous assessment of event log’s fitness for purpose. • Improved data validation for domain experts through artificial intelligence support. • Artificial intelligence derives event log understanding from semantic visual analysis. • Contextual guidance improves experts’ understanding and validation of event log data. • Instantiated prototype evaluated as useful and applicable in a real-world setting. • Artificial intelligence and domain knowledge support fitness for purpose evaluation.

The field of process mining offers a range of techniques for evidence-based improvement of business processes. The quality of the process data used, as stored in so-called event logs, is paramount to the reliability and usefulness of the process mining outcomes. Due to the increased uptake, at scale and for more complex types of applications, the field of process mining has evolved and event logs now need to be object-centric rather than event-centric. To understand and manage the quality problems that can occur in object-centric event logs a systematic approach is required that is different from past investigations into event-centric logs. To this end, we adopt a pattern-based approach, a tried and tested method to characterise problems that are otherwise hard to capture. A new collection of patterns is presented for object-centric logs, where each pattern captures the nature of the problem, how its manifestation can be detected, and how the problem can be remedied. The pattern collection is validated through a multi-prong approach, i.e., evidence-based, literature-based, empirical, and user-based (with the process mining community). The results show that these patterns are perceived as important to identify and that they do occur in practical settings.

Process mining techniques can be used to analyse business processes using the data logged during their execution. These techniques are leveraged in a wide range of domains, including healthcare, where it focuses mainly on the analysis of diagnostic, treatment, and organisational processes. Despite the huge amount of data generated in hospitals by staff and machinery involved in healthcare processes, there is no evidence of a systematic uptake of process mining beyond targeted case studies in a research context. When developing and using process mining in healthcare, distinguishing characteristics of healthcare processes such as their variability and patient-centred focus require targeted attention. Against this background, the Process-Oriented Data Science in Healthcare Alliance has been established to propagate the research and application of techniques targeting the data-driven improvement of healthcare processes. This paper, an initiative of the alliance, presents the distinguishing characteristics of the healthcare domain that need to be considered to successfully use process mining, as well as open challenges that need to be addressed by the community in the future.

The success of a process mining project is highly dependent on the quality of the event log data, the degree to which quality issues are detected, and the way they are resolved. The detection and resolution of data quality issues requires a systematic approach that is aware of the organisational context in which event log data is created. To this end, the Odigos framework has been developed in prior work. The focus of this paper is the validation of this framework through semistructured interviews with a range of experts in process mining. The experts confirmed the utility of the framework, provided valuable insights into data quality in practical settings, and suggested enhancements to the Odigos framework.

To date, the preponderance of techniques for eliciting the knowledge embedded in trained artificial neural networks (ANN's) has focused primarily on extracting rule-based explanations from feedforward ANN's. The ADT taxonomy for categorizing such techniques was proposed in 1995 to provide a basis for the systematic comparison of the different approaches. This paper shows that not only is this taxonomy applicable to a cross section of current techniques for extracting rules from trained feedforward ANN's but also how the taxonomy can be adapted and extended to embrace a broader range of ANN types (e.g., recurrent neural networks) and explanation structures. In addition the paper identifies some of the key research questions in extracting the knowledge embedded within ANN's including the need for the formulation of a consistent theoretical basis for what has been, until recently, a disparate collection of empirical results.