Rapid Business Process Discovery (R-BPD)

Ghose, Aditya; Koliadis, George; Chueng, Arthur

doi:10.1007/978-3-540-75563-0_27

Cited by 18 publications

(10 citation statements)

References 13 publications

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“…Another idea is to query heterogeneous information resources (e.g. corporate documentation, web-content) and rapidly build domainspecific ontology that would be incrementally adjusted to correctness by an analyst [25].…”

Section: Discussionmentioning

confidence: 99%

Correlating Business Objectives with Services: An Ontology-Driven Approach

Lê

Ghose

Krishnan

et al. 2011

2011 IEEE International Conference on Services Computing

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Ensuring that the IT/business functions of an organization realize its business objectives has long been recognized as a critically important question. This paper reports on a project that seeks to overturn established management orthodoxy by establishing that business objectives can be adequately modeled by leveraging a domain ontology and that methodological and tool support can be provided for the task of correlating the objectives of an organization and its service offerings. This paper presents an interim report from this project that describes how to leverage a domain ontology in i) building business objective/goal models in a top-down manner (required to be able to refine these to a level where there could be an ontological match between the languages used to describe objectives and services); ii) assessing the degree of ontological match between low-level objectives and business services as a step towards an automated framework for establishing strategic service alignment. We provide a brief illustration of our in-progress implementation within a toolkit called ServAlign.

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Section: Discussionmentioning

confidence: 99%

Correlating Business Objectives with Services: An Ontology-Driven Approach

Lê

Ghose

Krishnan

et al. 2011

2011 IEEE International Conference on Services Computing

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“…implicit conditions, which we explicitly tackle with our approach. The R-BPD toolkit from the University of Wollongong uses a syntax parser to identify verb-object phrases [21]. It also identifies textual patterns like "If <condition/event>, [then] <action>" [20].…”

Section: Related Workmentioning

confidence: 99%

Process Model Generation from Natural Language Text

Friedrich¹,

Mendling

Puhlmann³

2011

Notes on Numerical Fluid Mechanics and Multidisciplinary Design

170

191

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Abstract. Business process modeling has become an important tool for managing organizational change and for capturing requirements of software. A central problem in this area is the fact that the acquisition of as-is models consumes up to 60% of the time spent on process management projects. This is paradox as there are often extensive documentations available in companies, but not in a ready-to-use format. In this paper, we tackle this problem based on an automatic approach to generate BPMN models from natural language text. We combine existing tools from natural language processing in an innovative way and augmented them with a suitable anaphora resolution mechanism. The evaluation of our technique shows that for a set of 47 text-model pairs from industry and textbooks, we are able to generate on average 77% of the models correctly.

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“…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

Leopold

Eid-Sabbagh

Mendling

et al. 2013

Decision Support Systems

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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.

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Rapid Business Process Discovery (R-BPD)

Cited by 18 publications

References 13 publications

Correlating Business Objectives with Services: An Ontology-Driven Approach

Correlating Business Objectives with Services: An Ontology-Driven Approach

Process Model Generation from Natural Language Text

Detection of naming convention violations in process models for different languages

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