Process mining techniques are able to extract knowledge from event logs commonly available in today’s information systems. These techniques provide new means to discover, monitor, and improve processes in a variety of application domains. There are two main drivers for the growing interest in process mining. On the one hand, more and more events are being recorded, thus, providing detailed information about the history of processes. On the other hand, there is a need to improve and support business processes in competitive and rapidly changing environments. This manifesto is created by the IEEE Task Force on Process Mining and aims to promote the topic of process mining. Moreover, by defining a set of guiding principles and listing important challenges, this manifesto hopes to serve as a guide for software developers, scientists, consultants, business managers, and end-users. The goal is to increase the maturity of process mining as a new tool to improve the (re)design, control, and support of operational business processes

Organizations maintain process models that describe or prescribe how cases (e.g., orders) are handled. However, reality may not agree with what is modeled. Conformance checking techniques reveal and diagnose differences between the behavior that is modeled and what is observed. Existing conformance checking approaches tend to focus on the control-flow in a process, while abstracting from data dependencies, resource assignments, and time constraints. Even in those situations when other perspectives are considered, the control-flow is aligned first, i.e., priority is given to this perspective. Data dependencies, resource assignments, and time constraints are only considered as "second-class citizens", which may lead to misleading conformance diagnostics. For example, a data attribute may provide strong evidence that the wrong activity was executed. Existing techniques will still diagnose the data-flow as deviatWhen conducting most of the research reported on in this paper, Dr. de Leoni was also affiliated with University of Padua, Italy, and financially supported by the Eurostar-Eureka Project PROMPT (E! 6696). ing, whereas our approach will indeed point out that the control-flow is deviating. In this paper, a novel algorithm is proposed that balances the deviations with respect to all these perspectives based on a customizable cost function. Evaluations using both synthetic and real data sets show that a multi-perspective approach is indeed feasible and may help to circumvent misleading results as generated by classical single-perspective or staged approaches. F. Mannhardt (B)·

Document VersionPublisher's PDF, also known as Version of Record (includes final page, issue and volume numbers) Please check the document version of this publication:• A submitted manuscript is the author's version of the article upon submission and before peer-review. There can be important differences between the submitted version and the official published version of record. People interested in the research are advised to contact the author for the final version of the publication, or visit the DOI to the publisher's website.• The final author version and the galley proof are versions of the publication after peer review.• The final published version features the final layout of the paper including the volume, issue and page numbers. Link to publication Citation for published version (APA):Conforti, R., Leoni, de, M., La Rosa, M., Aalst, van der, W. M. P., & Hofstede, ter, A. H. M. (2015). A recommendation system for predicting risks across multiple business process instances. Decision Support Systems, 69, 1-19. DOI: 10.1016/j.dss.2014 General rights Copyright and moral rights for the publications made accessible in the public portal are retained by the authors and/or other copyright owners and it is a condition of accessing publications that users recognise and abide by the legal requirements associated with these rights.• Users may download and print one copy of any publication from the public portal for the purpose of private study or research.• You may not further distribute the material or use it for any profit-making activity or commercial gain • You may freely distribute the URL identifying the publication in the public portal ? Take down policyIf you believe that this document breaches copyright please contact us providing details, and we will remove access to the work immediately and investigate your claim. This paper proposes a recommendation system that supports process participants in taking risk-informed decisions, with the goal of reducing risks that may arise during process execution. Risk reduction involves decreasing the likelihood and severity of a process fault from occurring. Given a business process exposed to risks, e.g. a financial process exposed to a risk of reputation loss, we enact this process and whenever a process participant needs to provide input to the process, e.g. by selecting the next task to execute or by filling out a form, we suggest to the participant the action to perform which minimizes the predicted process risk. Risks are predicted by traversing decision trees generated from the logs of past process executions, which consider process data, involved resources, task durations and other information elements like task frequencies. When applied in the context of multiple process instances running concurrently, a second technique is employed that uses integer linear programming to compute the optimal assignment of resources to tasks to be performed, in order to deal with the interplay between risks relative to different instances. The recommendation system has been impl...

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Clinical guidelines aim at improving the quality of care processes based on 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 towards a particular setting (e.g., hospital policy or patient group characteristics). Therefore, we propose an approach to mediate between event data reflecting the clinical reality and clinical guidelines describing best-practices in medicine. Concretely, we repair and enrich declarative process models based on event logs. Initial models based on clinical guidelines are improved by observing the real process.Declarative models are used as they allow for more flexibility. Process mining techniques are used to check conformance, analyze deviations, and enrich models with conformance-related diagnostics. The approach has been applied in the urology department of the Isala hospital in the Netherlands. The results demonstrate that the approach is feasible and that our toolset based on ProM and Declare is indeed able to provide valuable insights related to process conformance.

The ability to know in advance the trend of running process instances, with respect to different features, such as the expected completion time, would allow business managers to timely counteract to undesired situations, in order to prevent losses. Therefore, the ability to accurately predict future features of running business process instances would be a very helpful aid when managing processes, especially under service level agreement constraints. However, making such accurate forecasts is not easy: many factors may influence the predicted features.Many approaches have been proposed to cope with this problem but all of them assume that the underling process is stationary. However, in real cases this assumption is not always true. In this work we present new methods for predicting the remaining time of running cases. In particular we propose a method, assuming process stationarity, which outperforms the state-of-the-art and two other methods which are able to make predictions even with nonstationary processes. We also describe an approach able to predict the full sequence of activities that a running case is going to take. All these methods are extensively evaluated on two real case studies.

Abstract. Process mining can be seen as the "missing link" between data mining and business process management. Although nowadays, in the context of process mining, process discovery attracts the lion's share of attention, conformance checking is at least as important. Conformance checking techniques verify whether the observed behavior recorded in an event log matches a modeled behavior. This type of analysis is crucial, because often real process executions deviate from the predefined process models. Although there exist solid conformance checking techniques for procedural models, little work has been done to adequately support conformance checking for declarative models. Typically, traces are classified as fitting or non-fitting without providing any detailed diagnostics. This paper aligns event logs and declarative models, i.e., events in the log are related to activities in the model if possible. The alignment provides then sophisticated diagnostics that pinpoint where deviations occur and how severe they are. The approach has been implemented in ProM and has been evaluated using both synthetic logs and real-life logs from Dutch municipalities.

Abstract. Modern organizations have invested in collections of descriptive and/or normative process models, but these rarely describe the actual processes adequately. Therefore, a variety of techniques for conformance checking have been proposed to pinpoint discrepancies between modeled and observed behavior. However, these techniques typically focus on the control-flow and abstract from data, resources and time. This paper describes an approach that aligns event log and model while taking all perspectives into account (i.e., also data, time and resources). This way it is possible to quantify conformance and analyze differences between model and reality. The approach was implemented using ProM and has been evaluated using both synthetic event logs and a real-life case study.