BPMN Miner: Automated discovery of BPMN process models with hierarchical structure

Conforti, Raffaele; Dumas, Marlon; García‐Bañuelos, Luciano; Rosa, Marcello La

doi:10.1016/j.is.2015.07.004

Cited by 81 publications

(58 citation statements)

References 22 publications

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“…Out of these methods, only four can also discover inclusive choices, but none in the context of non-block-structured models. In fact, [16], [38] are able to directly identify block-structured inclusive choices (using process trees), while [79], [96] can detect this construct only when used on top of the methods in [16] or [38] (i.e., indirectly).…”

Section: Procedural Language Constructs (Rq3)mentioning

confidence: 99%

“…One of the methods which has a ProM implementation [33] is also available as standalone tool. The works [19], [79], [92], [95] provide both a standalone implementation and a further implementation as a plug-in for Apromore. 2 Apromore is an online process analytics platform, also open source, and has a growing consensus among academics as a process mining tool oriented towards end users.…”

Section: Implementation (Rq4)mentioning

confidence: 99%

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Automated Discovery of Process Models from Event Logs: Review and Benchmark

Augusto

Conforti

Dumas

et al. 2019

IEEE Trans. Knowl. Data Eng.

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326

266

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Process mining allows analysts to exploit logs of historical executions of business processes to extract insights regarding the actual performance of these processes. One of the most widely studied process mining operations is automated process discovery. An automated process discovery method takes as input an event log, and produces as output a business process model that captures the control-flow relations between tasks that are observed in or implied by the event log. Various automated process discovery methods have been proposed in the past two decades, striking different tradeoffs between scalability, accuracy and complexity of the resulting models. However, these methods have been evaluated in an ad-hoc manner, employing different datasets, experimental setups, evaluation measures and baselines, often leading to incomparable conclusions and sometimes unreproducible results due to the use of closed datasets. This article provides a systematic review and comparative evaluation of automated process discovery methods, using an open-source benchmark and covering twelve publicly-available real-life event logs, twelve proprietary real-life event logs, and nine quality metrics. The results highlight gaps and unexplored tradeoffs in the field, including the lack of scalability of some methods and a strong divergence in their performance with respect to the different quality metrics used.

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Section: Procedural Language Constructs (Rq3)mentioning

confidence: 99%

Section: Implementation (Rq4)mentioning

confidence: 99%

Automated Discovery of Process Models from Event Logs: Review and Benchmark

Augusto

Conforti

Dumas

et al. 2019

IEEE Trans. Knowl. Data Eng.

Self Cite

326

266

View full text Add to dashboard Cite

show abstract

“…Many process discovery techniques have been developed over the last decade (e.g. [2,3,4,5]), producing process models in various forms, such as Petri nets [6], process trees [7], YAWL models [8], and BPMN models [9].…”

Section: Introductionmentioning

confidence: 99%

The imprecisions of precision measures in process mining

Tax

Sidorova

et al. 2018

Information Processing Letters

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In process mining, precision measures are used to quantify how much a process model overapproximates the behavior seen in an event log. Although several measures have been proposed throughout the years, no research has been done to validate whether these measures achieve the intended aim of quantifying over-approximation in a consistent way for all models and logs. This paper fills this gap by postulating a number of axioms for quantifying precision consistently for any log and any model. Further, we show through counter-examples that none of the existing measures consistently quantifies precision.

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“…1.1. And indeed, it is possible to discover BPMN models and other workflow models (such as Petri net systems) from event logs [29].…”

Section: Introductionmentioning

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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BPMN Miner: Automated discovery of BPMN process models with hierarchical structure

Cited by 81 publications

References 22 publications

Automated Discovery of Process Models from Event Logs: Review and Benchmark

Automated Discovery of Process Models from Event Logs: Review and Benchmark

The imprecisions of precision measures in process mining

Feature extraction from event logs for predictive monitoring of business processes

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