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These days, businesses keep track of more and more data in their information systems. Moreover, this data becomes more fine-grained than ever, tracking clicks and mutations in databases at the lowest level possible. Faced with such data, process discovery often struggles with producing comprehensible models, as they instead return spaghetti-like models. Such finely granulated models do not fit the business user's mental model of the process under investigation. To tackle this, event log abstraction (ELA) techniques can transform the underlying event log to a higher granularity level. However, insights into the performance of these techniques are lacking in literature as results are only based on small-scale experiments and are often inconclusive. Against this background, this paper evaluates state-of-the-art abstraction techniques on 400 event logs. Results show that ELA sacrifices fitness for precision, but complexity reductions heavily depend on the ELA technique used. This study also illustrates the importance of a larger-scale experiment, as sub-sampling of results leads to contradictory conclusions. [ABSTRACT FROM AUTHOR]

Process mining techniques analyze processes based on events stored in event logs. Yet, low-level events recorded by information systems may not directly match high-level activities that make sense to process stakeholders. This results in discovered process models that cannot be easily understood. To prevent such situations from happening, low-level events need to be translated into high-level activities that are recognizable by stakeholders. This paper proposes the Guided Process Discovery method (GPD). Low-level events are grouped based on behavioral activity patterns, which capture domain knowledge on the relation between high-level activities and low-level events. Events in the resulting abstracted event log correspond to instantiations of high-level activities. We validate process models discovered on the abstracted event log by checking conformance between the low-level event log and an expanded model in which the high-level activities are replaced by activity patterns. The method was tested using two real-life event logs. We show that the process models discovered with the GPD method are more comprehensible and can be used to answer process questions, whereas process models discovered using standard process discovery techniques do not provide the insights needed. [ABSTRACT FROM AUTHOR]

This paper addresses the problem of comparing different variants of the same process. We aim to detect relevant differences between processes based on what was recorded in event logs. We use transition systems to model behavior and to highlight differences. Transition systems are annotated with measurements, used to compare the behavior in the different variants. Decision points in the transition system are also analyzed, and the business rules of the process variants in such points are compared. The results are visualized as colored transitions systems, where the colored states and transitions indicate the existence and magnitude of differences. The approach has been implemented in ProM, and the implementation is publicly available. The approach has been evaluated using real-life data sets. The results show how our technique is able to detect relevant differences that could not be captured using existing approaches. Moreover, the user is not overloaded with diagnostics on differences that are less significant. [ABSTRACT FROM AUTHOR]

Predictive Process Analytics is becoming an essential aid for organizations, providing online operational support of their processes. However, process stakeholders need to be provided with an explanation of the reasons why a given process execution is predicted to behave in a certain way. Otherwise, they will be unlikely to trust the predictive monitoring technology and, hence, adopt it. This paper proposes a predictive analytics framework that is also equipped with explanation capabilities based on the game theory of Shapley Values. The framework has been implemented in the IBM Process Mining suite and commercialized for business users. The framework has been tested on real-life event data to assess the quality of the predictions and the corresponding evaluations. In particular, a user evaluation has been performed in order to understand if the explanations provided by the system were intelligible to process stakeholders. • Predictive Business Process Monitoring provides online operational support. • Explainability is a necessary condition to build trust in the predictive technology. • Explainable AI framework needs to be validated with real end users. • Deployment of an Explainable Predictive framework to provide process insights. • Shapley values provide the importance of the factors influencing the prediction. [ABSTRACT FROM AUTHOR]

Object-centric processes (also known as Artifact-centric processes) are implementations of a paradigm where an instance of one process is not executed in isolation but interacts with other instances of the same or other processes. Interactions take place through bridging events where instances exchange data. Object-centric processes are recently gaining popularity in academia and industry, because their nature is observed in many application scenarios. This poses significant challenges in predictive analytics due to the complex intricacy of the process instances that relate to each other via many-to-many associations. Existing research is unable to directly exploit the benefits of these interactions, thus limiting the prediction quality. This paper proposes an approach to incorporate the information about the object interactions into the predictive models. The approach is assessed on real-life object-centric process event data, using different Key Performance Indicators (KPIs). The results are compared with a naïve approach that overlooks the object interactions, thus illustrating the benefits of their use on the prediction quality. • Object-centric processes as a new emerging paradigm in industry. • Traditional approaches are unable to exploit the benefits of object-interaction. • Considering processes interactions when predicting improves prediction quality. • Integration of Explainable AI techniques to highlight relevant features. • Shapley values proved the importance of object-interaction and aggregated features. [ABSTRACT FROM AUTHOR]

Process mining can be viewed as the missing link between model-based process analysis and data-oriented analysis techniques. Lion׳s share of process mining research has been focusing on process discovery (creating process models from raw data) and replay techniques to check conformance and analyze bottlenecks. These techniques have helped organizations to address compliance and performance problems. However, for a more refined analysis, it is essential to correlate different process characteristics . For example, do deviations from the normative process cause additional delays and costs? Are rejected cases handled differently in the initial phases of the process? What is the influence of a doctor׳s experience on treatment process? These and other questions may involve process characteristics related to different perspectives (control-flow, data-flow, time, organization, cost, compliance, etc.). Specific questions (e.g., predicting the remaining processing time) have been investigated before, but a generic approach was missing thus far. The proposed framework unifies a number of approaches for correlation analysis proposed in literature, proposing a general solution that can perform those analyses and many more. The approach has been implemented in ProM and combines process and data mining techniques. In this paper, we also demonstrate the applicability using a case study conducted with the UWV (Employee Insurance Agency), one of the largest “administrative factories” in The Netherlands. [ABSTRACT FROM AUTHOR]

Process mining can be seen as the “missing link” between data mining and business process management. The lion's share of process mining research has been devoted to the discovery of procedural process models from event logs. However, often there are predefined constraints that (partially) describe the normative or expected process, e.g., “activity A should be followed by B ” or “activities A and B should never be both executed”. A collection of such constraints is called a declarative process model . Although it is possible to discover such models based on event data, this paper focuses on aligning event logs and predefined declarative process models. Discrepancies between log and model are mediated such that observed log traces are related to paths in the model. The resulting alignments provide sophisticated diagnostics that pinpoint where deviations occur and how severe they are . Moreover, selected parts of the declarative process model can be used to clean and repair the event log before applying other process mining techniques. Our alignment-based approach for preprocessing and conformance checking using declarative process models has been implemented in ProM and has been evaluated using both synthetic logs and real-life logs from a Dutch hospital. [ABSTRACT FROM AUTHOR]

Clinical guidelines aim at improving the quality of care processes through evidence-based insights. However, there may be good reasons to deviate from such guidelines or the guidelines may provide insufficient support as they are not tailored toward a particular setting (e.g., hospital policy or patient group characteristics). Therefore, we report a case study that shows how process mining techniques can be used to mediate between event data reflecting the clinical reality and clinical guidelines describing best-practices in medicine. Declarative models are used as they allow for more flexibility and are more suitable for describing healthcare processes that are highly unpredictable and unstable. Concretely, initial (hand made) models based on clinical guidelines are improved based on actual process executions (if these executions are proven to be correct). Process mining techniques can be also used to check conformance, analyze deviations, and enrich models with conformance-related diagnostics. The techniques have been applied in the urology department of the Isala hospital in the Netherlands. The results demonstrate that the techniques are feasible and that our toolset based on ProM and Declare is indeed able to provide valuable insights related to process conformance. [ABSTRACT FROM AUTHOR]