Prediction of Remaining Service Execution Time Using Stochastic Petri Nets with Arbitrary Firing Delays

Solti, Andreas; Weske, Mathias

doi:10.1007/978-3-642-45005-1_27

Cited by 95 publications

(45 citation statements)

References 29 publications

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“…The majority of research concerning this process prediction focuses on predicting the result of a process or the next step in a process ( fig. 1), where there are multiple possibilities available [23,24,25]. Evermann et al [26] transferred the applicability of neural networks from Natural Language Processing to the domain of Next…”

Section: Requirements For Ppi Prediction In Business Processesmentioning

confidence: 99%

Event Entry Time Prediction in Financial Business Processes Using Machine Learning - A Use Case From Loan Applications

Frey¹,

Emrich²,

Fettke³

et al. 2018

Proceedings of the 51st Hawaii International Conference on System Sciences

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The recent financial crisis has forced politics to overthink regulatory structures and compliance mechanisms for the financial industry. Faced with these new challenges the financial industry in turn has to reevaluate their risk assessment mechanisms. While approaches to assess financial risks, have been widely addressed, the compliance of the underlying business processes is also crucial to ensure an end-to-end traceability of the given business events. This paper presents a novel approach to predict entry times and other key performance indicators of such events in a business process. A loan application process is used as a data example to evaluate the chosen feature modellings and algorithms.

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Section: Requirements For Ppi Prediction In Business Processesmentioning

confidence: 99%

Event Entry Time Prediction in Financial Business Processes Using Machine Learning - A Use Case From Loan Applications

Frey¹,

Emrich²,

Fettke³

et al. 2018

Proceedings of the 51st Hawaii International Conference on System Sciences

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“…A more sophisticated approach to remaining time prediction based on stochastic Petri nets is proposed by [81]. In [80], the authors use time series Petri nets to model the execution time of single activities.…”

Section: Timementioning

confidence: 99%

Feature extraction from event logs for predictive monitoring of business processes

Borchert¹

2018

Preprint

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Many organizations record information about their IT-driven processes in some form of event logs, be it for auditing or debugging purposes or simply by accident. Obtaining knowledge from such event logs is the goal of process mining. Process mining techniques can be used to discover process models from event logs or relate existing process models and observed behaviour in various ways. While we usually envision process models to carry some executable semantics, like Petri net systems or BPMN models, we can also think of purely statistical models or a combination of both. Statistical models are particularly suited for predictive tasks such as early classification of a running case or anomaly detection. In the context of business process management, these activities are denoted as predictive monitoring.However, most standard machine learning and data mining algorithms cannot be directly applied to complex event logs. Some of the most widespread algorithms, like Support Vector Machines or decision tree learners, require the data to be mapped into a vector space first. To this end, meaningful features need to be extracted from event logs. Now, it is not obvious what good features in the domain of business processes are: events describe observed behaviour and are typically equipped with information about execution times and quite often also resources and other attributes. Business processes usually show a certain degree of structure in terms of subprocesses and workflow patterns, which is reflected in the logs. Furthermore, processes may allow for a parallel execution of activities, so that the order in which events are recorded can have different interpretations. Treating the traces in an event log simply as symbolic sequences of activities neglects all of these potentially useful perspectives.In this master's thesis, we investigate feature extraction techniques for event log traces. We provide an overview over the field of predictive monitoring and analyze existing trace profiles proposed in the literature. Based on this, we apply the results obtained in recent research on process discovery to find meaningful abstractions over related subsequences of events.We assess different feature sets by evaluating their predictive power in a supervised classification setting as well as a semi-supervised outlier detection setting. For this purpose, we use two datasets from public administration, describing complex processes in EU agricultural subsidy management. Our particular goal in this domain is to predict negative outcomes, for instance, additional work due to legal claims or corrections necessary after initial payment decisions. iv

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“…Models are discovered from and validated against event data from recorded process executions, see [43]. Mined models are used as the basis for prediction [44,45], simulation [46], and resource-behavior analysis [47,48]. However, much work in this field focuses on the control-flow perspective, i.e.…”

Section: Related Workmentioning

confidence: 99%

Conformance checking and performance improvement in scheduled processes: A queueing-network perspective

Senderovich

Weidlich

Yedidsion

et al. 2016

Information Systems

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Prediction of Remaining Service Execution Time Using Stochastic Petri Nets with Arbitrary Firing Delays

Cited by 95 publications

References 29 publications

Event Entry Time Prediction in Financial Business Processes Using Machine Learning - A Use Case From Loan Applications

Event Entry Time Prediction in Financial Business Processes Using Machine Learning - A Use Case From Loan Applications

Feature extraction from event logs for predictive monitoring of business processes

Conformance checking and performance improvement in scheduled processes: A queueing-network perspective

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