Automated Error Correction of Business Process Models

Gambini, Mauro; Rosa, Marcello La; Migliorini, Sara; Hofstede, Arthur H. M. ter

doi:10.1007/978-3-642-23059-2_14

Cited by 28 publications

(18 citation statements)

References 19 publications

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“…Unsound models can be repaired to become sound by simulated annealing [16], though fitness to a given log is not preserved. Non-fitting models can be repaired to become fitting by adding subprocesses [14], though soundness is not guaranteed.…”

Section: Related Workmentioning

confidence: 99%

Discovering Block-Structured Process Models from Event Logs - A Constructive Approach

Leemans

Fahland

Aalst

2013

Lecture Notes in Computer Science

442

354

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Process discovery is the problem of, given a log of observed behaviour, finding a process model that 'best' describes this behaviour. A large variety of process discovery algorithms has been proposed. However, no existing algorithm guarantees to return a fitting model (i.e., able to reproduce all observed behaviour) that is sound (free of deadlocks and other anomalies) in finite time. We present an extensible framework to discover from any given log a set of block-structured process models that are sound and fit the observed behaviour. In addition we characterise the minimal information required in the log to rediscover a particular process model. We then provide a polynomial-time algorithm for discovering a sound, fitting, block-structured model from any given log; we give sufficient conditions on the log for which our algorithm returns a model that is languageequivalent to the process model underlying the log, including unseen behaviour. The technique is implemented in a prototypical tool.

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Section: Related Workmentioning

confidence: 99%

Discovering Block-Structured Process Models from Event Logs - A Constructive Approach

Leemans

Fahland

Aalst

2013

Lecture Notes in Computer Science

442

354

View full text Add to dashboard Cite

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“…The idea is to use genetic algorithms such as simulated annealing [24] to create perturbations on the current process instance in order to rectify its execution and thus avoid a fault from eventually occurring. Our previous application of simulated annealing to the problem of automatically correcting business process models [8] has shown that such perturbations can be obtained very efficiently. The challenge stands in properly defining the objective functions so as to create meaningful perturbations.…”

Section: Resultsmentioning

confidence: 99%

History-Aware, Real-Time Risk Detection in Business Processes

Conforti

Fortino

Rosa

et al. 2011

On the Move to Meaningful Internet Systems: OTM 2011

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Abstract. This paper proposes a novel approach for identifying risks in executable business processes and detecting them at run-time. The approach considers risks in all phases of the business process management lifecycle, and is realized via a distributed, sensor-based architecture. At design-time, sensors are defined to specify risk conditions which when fulfilled, are a likely indicator of faults to occur. Both historical and current process execution data can be used to compose such conditions. At run-time, each sensor independently notifies a sensor manager when a risk is detected. In turn, the sensor manager interacts with the monitoring component of a process automation suite to prompt the results to the user who may take remedial actions. The proposed architecture has been implemented in the YAWL system and its performance has been evaluated in practice.

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“…al. [11]. Their approach considers three dimensions: the structural distance, behavioral distance and 'badness' of a solution w.r.t.…”

Section: Related Workmentioning

confidence: 99%

Improving Business Process Models Using Observed Behavior

Buijs

Rosa

Reijers

et al. 2013

Lecture Notes in Business Information Processing

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Abstract. Process-aware information systems (PAISs) can be configured using a reference process model, which is typically obtained via expert interviews. Over time, however, contextual factors and system requirements may cause the operational process to start deviating from this reference model. While a reference model should ideally be updated to remain aligned with such changes, this is a costly and often neglected activity. We present a new process mining technique that automatically improves the reference model on the basis of the observed behavior as recorded in the event logs of a PAIS. We discuss how to balance the four basic quality dimensions for process mining (fitness, precision, simplicity and generalization) and a new dimension, namely the structural similarity between the reference model and the discovered model. We demonstrate the applicability of this technique using a real-life scenario from a Dutch municipality.

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Automated Error Correction of Business Process Models

Cited by 28 publications

References 19 publications

Discovering Block-Structured Process Models from Event Logs - A Constructive Approach

Discovering Block-Structured Process Models from Event Logs - A Constructive Approach

History-Aware, Real-Time Risk Detection in Business Processes

Improving Business Process Models Using Observed Behavior

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