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34
Predicting deadline transgressions using event logs
- In Proc. of BPM Workshop 2012, volume 132 of LNBIP
, 2013
"... Abstract. Effective risk management is crucial for any organisation. One of its key steps is risk identification, but few tools exist to support this process. Here we present a method for the automatic discovery of a particular type of process-related risk, the danger of deadline transgressions or o ..."
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Abstract. Effective risk management is crucial for any organisation. One of its key steps is risk identification, but few tools exist to support this process. Here we present a method for the automatic discovery of a particular type of process-related risk, the danger of deadline transgressions or overruns, based on the analysis of event logs. We define a set of time-related process risk indicators, i.e., patterns observable in event logs that highlight the likelihood of an overrun, and then show how instances of these patterns can be identified automatically using statistical principles. To demonstrate its feasibility, the approach has been implemented as a plug-in module to the process mining framework ProM and tested using an event log from a Dutch financial institution. 1
Automated risk mitigation in business processes
- In Proc. of CoopIS, volume 7565 of LNCS
, 2012
"... Abstract. This paper proposes a concrete approach for the automatic mitigation of risks that are detected during process enactment. Given a process model exposed to risks, e.g. a financial process exposed to the risk of approval fraud, we enact this process and as soon as the likelihood of the assoc ..."
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Abstract. This paper proposes a concrete approach for the automatic mitigation of risks that are detected during process enactment. Given a process model exposed to risks, e.g. a financial process exposed to the risk of approval fraud, we enact this process and as soon as the likelihood of the associated risk(s) is no longer tolerable, we generate a set of possible mitigation actions to reduce the risks ’ likelihood, ideally annulling the risks altogether. A mitigation action is a sequence of controlled changes applied to the running process instance, taking into account a snapshot of the process resources and data, and the current status of the system in which the process is executed. These actions are proposed as recommendations to help process administrators mitigate process-related risks as soon as they arise. The approach has been implemented in the YAWL environment and its performance evaluated. The results show that it is possible to mitigate process-related risks within a few minutes. 1
Supporting Risk-Informed Decisions during Business Process Execution
"... Abstract. This paper proposes a technique that supports process participants in making risk-informed decisions, with the aim to reduce the process risks. Risk reduction involves decreasing the likelihood and severity of a process fault from occurring. Given a process exposed to risks, e.g. a financi ..."
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Abstract. This paper proposes a technique that supports process participants in making risk-informed decisions, with the aim to reduce the process risks. Risk reduction involves decreasing the likelihood and severity of a process fault from occurring. Given a 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 prompt the participant with the expected risk that a given fault will occur given the particular input. These risks are predicted by traversing decision trees generated from the logs of past process executions and considering process data, involved resources, task durations and contextual information like task frequencies. The approach has been implemented in the YAWL system and its effectiveness evaluated. The results show that the process instances executed in the tests complete with significantly fewer faults and with lower fault severities, when taking into account the recommendations provided by our technique. 1
Service Mining: Using Process Mining to Discover, Check, and Improve Service Behavior
, 2011
"... Web services are an emerging technology to implement and integrate business processes within and across enterprises. Service-orientation can be used to decompose complex systems into loosely coupled software components that may run remotely. However, the distributed nature of services complicates t ..."
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Web services are an emerging technology to implement and integrate business processes within and across enterprises. Service-orientation can be used to decompose complex systems into loosely coupled software components that may run remotely. However, the distributed nature of services complicates the design and analysis of service-oriented systems that support end-to-end business processes. Fortunately, services leave trails in so-called event logs and recent breakthroughs in process mining research make it possible to discover, analyze, and improve business processes based on such logs. Recently, the Task Force on Process Mining released the Process Mining Manifesto. This manifesto is supported by 53 organizations and 77 process mining experts contributed to it. The active participation from end-users, tool vendors, consultants, analysts, and researchers illustrate the growing significance of process mining as a bridge between data mining and business process modeling. In this paper, we focus on the opportunities and challenges for service mining, i.e., applying process mining techniques to services. We discuss the guiding principles and challenges listed in the Process Mining Manifesto and also highlight challenges specific for service-orientated systems.
A framework for cost-aware process management: cost reporting and cost prediction
- Journal of Universal Computer Science Special Issue: Conceptual
, 2013
"... Abstract: Organisations are constantly seeking efficiency gains for their business processes in terms of time and cost. Management accounting enables detailed cost reporting of business operations for decision making purposes, although significant effort is required to gather accurate operational d ..."
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Cited by 3 (3 self)
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Abstract: Organisations are constantly seeking efficiency gains for their business processes in terms of time and cost. Management accounting enables detailed cost reporting of business operations for decision making purposes, although significant effort is required to gather accurate operational data. Process mining, on the other hand, may provide valuable insight into processes through analysis of events recorded in logs by IT systems, but its primary focus is not on cost implications. In this paper, a framework is proposed which aims to exploit the strengths of both fields in order to better support management decisions on cost control. This is achieved by automatically merging cost data with historical data from event logs for the purposes of monitoring, predicting, and reporting process-related costs. The on-demand generation of accurate, relevant and timely cost reports, in a style akin to reports in the area of management accounting, will also be illustrated. This is achieved through extending the open-source process mining framework ProM.
M.: A General Framework for Correlating Business Process Characteristics
- In: Proceedings of the 12th International Conference of Business Process Management (BPM 2014). Volume 8659 of LNCS
, 2014
"... Abstract. Process discovery techniques make it possible to automatically derive process models from event data. However, often one is not only interested in dis-covering the control-flow but also in answering questions like “What do the cases that are late have in common?”, “What characterizes the w ..."
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Abstract. Process discovery techniques make it possible to automatically derive process models from event data. However, often one is not only interested in dis-covering the control-flow but also in answering questions like “What do the cases that are late have in common?”, “What characterizes the workers that skip this check activity?”, and “Do people work faster if they have more work?”, etc. Such questions can be answered by combining process mining with classification (e.g., decision tree analysis). Several authors have proposed ad-hoc solutions for spe-cific questions, e.g., there is work on predicting the remaining processing time and recommending activities to minimize particular risks. However, as shown in this paper, it is possible to unify these ideas and provide a general framework for deriving and correlating process characteristics. First, we show how the de-sired process characteristics can be derived and linked to events. Then, we show that we can derive the selected dependent characteristic from a set of indepen-dent characteristics for a selected set of events. This can be done for any process characteristic one can think of. The approach is highly generic and implemented as plug-in for the ProM framework. Its applicability is demonstrated by using it to answer to a wide range of questions put forward by the UWV (the Dutch Employee Insurance Agency). 1
Profiling Event Logs to Configure Risk Indicators for Process Delays
"... Abstract. Risk identification is one of the most challenging stages in the risk management process. Conventional risk management approaches provide little guidance and companies often rely on the knowledge of experts for risk identification. In this paper we demonstrate how risk indicators can be us ..."
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Abstract. Risk identification is one of the most challenging stages in the risk management process. Conventional risk management approaches provide little guidance and companies often rely on the knowledge of experts for risk identification. In this paper we demonstrate how risk indicators can be used to predict process delays via a method for configuring so-called Process Risk Indicators (PRIs). The method learns suitable configurations from past process behaviour recorded in event logs. To validate the approach we have implemented it as a plug-in of the ProM process mining framework and have conducted experiments using various data sets from a major insurance company.
C.: Predictive monitoring of business processes
- In: Proceedings of the 26th International Conference on Advanced Information Systems Engineering (CAiSE 2014). Volume 8484 of LNCS. (2014) 457–472
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Automated risk mitigation in business processes (extended version). QUT ePrints 49331
, 2012
"... Abstract. This paper proposes a concrete approach for the automatic mitigation of risks that are detected during process enactment. Given a process model af-fected by risks, e.g. a financial process exposed to the risk of approval fraud, we enact this process and as soon as the likelihood of the ass ..."
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Abstract. This paper proposes a concrete approach for the automatic mitigation of risks that are detected during process enactment. Given a process model af-fected by risks, e.g. a financial process exposed to the risk of approval fraud, we enact this process and as soon as the likelihood of the associated risk(s) is no longer tolerable, we generate a set of possible mitigation actions to reduce the risks ’ likelihood, ideally annulling the risks altogether. A mitigation action is a sequence of controlled changes applied to the running process instance, taking into account a snapshot of the process resources and data, and the current sta-tus of the system in which the process is executed. These actions are proposed as recommendations to help process administrators mitigate process-related risks as soon as they arise. The approach has been implemented in the YAWL envi-ronment and its performance evaluated. The results show that it is possible to mitigate process-related risks within a few minutes. 1
Model-driven Instrumentation with Kieker and Palladio to forecast Dynamic Applications
"... Abstract: Providing applications in stipulated qualities is a challenging task in today’s cloud environments. The dynamic nature of the cloud requires special runtime models that reflect changes in the application structure and their deployment. These runtime models are used to forecast the applicat ..."
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Abstract: Providing applications in stipulated qualities is a challenging task in today’s cloud environments. The dynamic nature of the cloud requires special runtime models that reflect changes in the application structure and their deployment. These runtime models are used to forecast the application performance in order to carry out mitigative actions proactively. Current runtime models do not evolve with the application structure and quickly become outdated. Further, they do not support the derivation of probing information that is required to gather the data for evolving the runtime model. In this paper, we present the initial results of our research on a forecasting approach that combines Kieker and Palladio in order to forecast the application performance based on a dynamic runtime model. To be specific, we present two instrumentation languages to specify Kieker monitoring probes based on structural information of the application specified in Palladio component models. Moreover, we sketch a concept to forward the monitored data to our PCM-based runtime model. This will empower Palladio to carry out performance forecasts of applications deployed in dynamic environments, which is to be tackled in future research steps. 1