Process Discovery from Model and Text Artefacts

Pittke

et al. 2017

Softw Syst Model

Documenting business processes using process models is common practice in many organizations. However, not all process information is best captured in process models. Hence, many organizations complement these models with textual descriptions that specify additional details. The problem with this supplementary use of textual descriptions is that existing techniques for automatically searching process repositories are limited to process models. They are not capable of taking the information from textual descriptions into account and, therefore, provide incomplete search results. In this paper, we address this problem and propose a technique that is capable of searching textual as well as model-based process descriptions. It automatically extracts activity-related and behavioral information from both descriptions types and stores it in a unified data format. An evaluation with a large Austrian bank demonstrates that the additional consideration of textual descriptions allows us to identify more relevant processes from a repository.

Section: Related Workmentioning

confidence: 99%

Section: Dobj(collects-5 Documents-8) Dobj(sends-10 Form-12)mentioning

confidence: 99%

Searching textual and model-based process descriptions based on a unified data format

Pittke

et al. 2017

Softw Syst Model

“…Examples include the (semi)-automatic creation of process models [31,37,35,34,77], conceptual dependency diagrams [33], entity-relationship models [36,66], and UML diagrams [3,18,19,64]. inconsistencies with requirement documents [11].…”

Section: State Of the Artmentioning

confidence: 99%

Detection of naming convention violations in process models for different languages

Decision Support Systems

Eid-Sabbagh

Mendling

et al. 2013

Companies increasingly use business process modeling for documenting and redesigning their operations.However, due to the size of such modeling initiatives, they often struggle with the quality assurance of their model collections. While many model properties can already be checked automatically, there is a notable gap of techniques for checking linguistic aspects such as naming conventions of process model elements. In this paper, we address this problem by introducing an automatic technique for detecting violations of naming conventions. This technique is based on text corpora and independent of linguistic resources such as WordNet.Therefore, it can be easily adapted to the broad set of languages for which corpora exist. We demonstrate the applicability of the technique by analyzing nine process model collections from practice, including over 27,000 labels and covering three di↵erent languages. The results of the evaluation show that our technique yields stable results and can reliably deal with ambiguous cases. In this way, this paper provides an important contribution to the field of automated quality assurance of conceptual models.

“…A group from Rio de Janeiro works with group stories provided in Portuguese for generating BPMN models [72]. Syntax parsing and textual patterns are used to create BPMN models in the approach of the R-BPD toolkit [73,74]. Further approaches on generating BPMN from text have been presented [75,76,77,78].…”

Section: Related Workmentioning

confidence: 99%

On the refactoring of activity labels in business process models

Smirnov

Mendling

2012

Information Systems

Large corporations increasingly utilize business process models for documenting and redesigning their operations. The extent of such modeling initiatives with several hundred models and dozens of often hardly trained modelers calls for automated quality assurance. While formal properties of control flow can easily be checked by existing tools, there is a notable gap for checking the quality of the textual content of models, in particular, its activity labels. In this paper, we address the problem of activity label quality in business process models. We designed a technique for the recognition of labeling styles, and the automatic refactoring of labels with quality issues. More specifically, we developed a parsing algorithm that is able to deal with the shortness of activity labels, which integrates natural language tools like WordNet and the Stanford Parser. Using three business process model collections from practice with differing labeling style distributions, we demonstrate the applicability of our technique. In comparison to a straightforward application of standard natural language tools, our technique provides much more stable results. As an outcome, the technique shifts the boundary of process model quality issues that can be checked automatically from syntactic to semantic aspects.