The ROAD from Sensor Data to Process Instances via Interaction Mining

Senderovich, Arik; Solti, Andreas; Gal, Avigdor; Mendling, Jan; Mandelbaum, Avishai

doi:10.1007/978-3-319-39696-5_16

Cited by 47 publications

(40 citation statements)

References 17 publications

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“…While these approaches only focus on one-to-one relations, they have been shown to be relatively robust to noise and non-conforming behavior. Work by Senderovich et al [251] focuses on a specific situation for event to activity mapping. Specifically, the approach derives events based on information obtained from sensors in hospitals and aligns these logged events to process activities.…”

Section: Related Workmentioning

confidence: 99%

“…Note that the question of how to obtain an uncertain mapping, which consists of a number of potential event-to-activity mappings, is not the focus of this chapter. Potential mappings can be obtained using one or more mapping techniques, such as [38,40,251]. In the remainder, Section 4.2.1 first describes the notion of a behavioral space, which we use to capture the impact of mapping uncertainty on the process behavior described by trace σ.…”

Section: Conformance-checking Techniquementioning

confidence: 99%

“…These are, among others, used to detect differences between models [156], for the harmonization of process model variants [157], process querying [131], and to automatically propagate process changes [280]. Other techniques focus on the alignment of other process-information artifacts, such as techniques that align event classes from event logs to process model activities [37,251].…”

Section: Matchingmentioning

confidence: 99%

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Comparing and Aligning Process Representations

2018

Lecture Notes in Business Information Processing

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Section: Related Workmentioning

confidence: 99%

Section: Conformance-checking Techniquementioning

confidence: 99%

Section: Matchingmentioning

confidence: 99%

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Comparing and Aligning Process Representations

2018

Lecture Notes in Business Information Processing

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“…Machine learning methods are used to infer the correct mapping. Senderovich et al [38] determine an optimal mapping between sensor data of a real-time locating system and activities based on nding an optimal mapping using integer linear programming. Ferreira et al [39] assume a complete process model of the high-level activities.…”

Section: Related Workmentioning

confidence: 99%

Guided Process Discovery – A pattern-based approach

Mannhardt

Leoni

Reijers

et al. 2018

Information Systems

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“…Techniques that match different types of process-oriented artifacts exist for process models and taxonomies [32], process models and textual process descriptions [33,34] as well as process models and event logs [35,36,37]. The alignments that results from such techniques have various use cases.…”

Section: Related Workmentioning

confidence: 99%

A probabilistic evaluation procedure for process model matching techniques

Kuss

Leopold

et al. 2018

Data & Knowledge Engineering

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Process model matching refers to the automatic identification of corresponding activities between two process models. It represents the basis for many advanced process model analysis techniques such as the identification of similar process parts or process model search. A central problem is how to evaluate the performance of process model matching techniques. Current evaluation methods require a binary gold standard that clearly defines which correspondences are correct. The problem is that often not even humans can agree on a set of correct correspondences. Hence, evaluating the performance of matching techniques based on a binary gold standard does not take the true complexity of the matching problem into account and does not fairly assess the capabilities of a matching technique. In this paper, we propose a novel evaluation procedure for process model matching techniques. In particular, we build on the assessments of multiple annotators to define the notion of a non-binary gold standard. In this way, we avoid the problem of agreeing on a single set of correct correspondences. Based on this non-binary gold standard, we introduce probabilistic versions of precision, recall, and F-measure as well as a distance-based performance measure. We use a dataset from the Process Model Matching Contest 2015 and a total of 16 matching systems to assess and compare the insights that can be obtained by using our evaluation procedure. We find that our probabilistic evaluation procedure allows us to gain more detailed insights into the performance of matching systems than a traditional evaluation based on a binary gold standard.

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The ROAD from Sensor Data to Process Instances via Interaction Mining

Cited by 47 publications

References 17 publications

Comparing and Aligning Process Representations

Comparing and Aligning Process Representations

Guided Process Discovery – A pattern-based approach

A probabilistic evaluation procedure for process model matching techniques

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