Comprehensible Predictive Models for Business Processes

Breuker, Dominic; Matzner, Martin; Delfmann, Patrick; Becker, Jörg

doi:10.25300/misq/2016/40.4.10

Cited by 163 publications

(132 citation statements)

References 67 publications

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“…Another major stream of works tackle the problem of predicting the future activities/events of a running process (cf. [63,26,27,23,40,15,53]). The works by [63,26,27,23,40] use deep learning approach for predicting the future events, e.g., the next event of the current running process.…”

Section: Related Workmentioning

confidence: 99%

Specification-driven predictive business process monitoring

Santoso

Felderer

2019

Softw Syst Model

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Predictive analysis in business process monitoring aims at forecasting the future information of a running business process. The prediction is typically made based on the model extracted from historical process execution logs (event logs). In practice, different business domains might require different kinds of predictions. Hence, it is important to have a means for properly specifying the desired prediction tasks, and a mechanism to deal with these various prediction tasks. Although there have been many studies in this area, they mostly focus on a specific prediction task. This work introduces a language for specifying the desired prediction tasks, and this language allows us to express various kinds of prediction tasks. This work also presents a mechanism for automatically creating the corresponding prediction model based on the given specification. Differently from previous studies, instead of focusing on a particular prediction task, we present an approach to deal with various prediction tasks based on the given specification of the desired prediction tasks. We also provide an implementation of the approach which is used to conduct experiments using real-life event logs.

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Section: Related Workmentioning

confidence: 99%

Specification-driven predictive business process monitoring

Santoso

Felderer

2019

Softw Syst Model

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“…The work of [22] and [38] is inspired by natural language processing techniques. The former work employs probabilistic finite automata (PFA) of activities to model transition probabilities, while the latter use recurrent neural networks over sequences of activities and sequences of activity-resource pairs.…”

Section: Next Eventmentioning

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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“…Predicting the next event elicits special attention since it gives organizations the ability to forecast process deviations. This type of early detection is essential for intervenability before a process enters risky states [12]. Moreover, predictive process management assists businesses in resource planning and allocation, providing insights on the condition of a process to fulfill for instance service-level agreements [13]- [15].…”

Section: Introductionmentioning

confidence: 99%

Decay Replay Mining to Predict Next Process Events

Theis

Darabi

2019

IEEE Access

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In complex processes, various events can happen in different sequences. The prediction of the next event given an a-priori process state is of importance in such processes. Recent methods have proposed deep learning techniques such as recurrent neural networks, developed on raw event logs, to predict the next event from a process state. However, such deep learning models by themselves lack a clear representation of the process states. At the same time, recent methods have neglected the time feature of event instances. In this paper, we take advantage of Petri nets as a powerful tool in modeling complex process behaviors considering time as an elemental variable. We propose an approach which starts from a Petri net process model constructed by a process mining algorithm. We enhance the Petri net model with time decay functions to create continuous process state samples. Finally, we use these samples in combination with discrete token movement counters and Petri net markings to train a deep learning model that predicts the next event. We demonstrate significant performance improvements and outperform the state-of-the-art methods on nine real-world benchmark event logs.

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Comprehensible Predictive Models for Business Processes

Cited by 163 publications

References 67 publications

Specification-driven predictive business process monitoring

Specification-driven predictive business process monitoring

Feature extraction from event logs for predictive monitoring of business processes

Decay Replay Mining to Predict Next Process Events

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