A relationship-based approach to model integration

Chećhik, Marsha; Nejati, Shiva; Sabetzadeh, Mehrdad

doi:10.1007/s11334-011-0155-2

Cited by 31 publications

(20 citation statements)

References 37 publications

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“…In the context of personal web, it is very hard to put such conventions in place as the models are developed independently and often without any prior coordination. The relationships between independently-developed models have to be specified explicitly instead [16]. These relationships are often established through a combination of manual and automated matching based on heuristics [57].…”

Section: Mapping and Merging Of Goal Modelsmentioning

confidence: 99%

PWWM: A Personal Web Workflow Methodology

Chećhik

Simmonds

Liaskos

et al. 2013

Lecture Notes in Computer Science

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Abstract. The personal web vision promises to give users a highly personalized experience on the web. This paper proposes and describes a Personal Web Workflow Methodology, designed to elicit, operationalize and execute a personal web user's goals. Our approach relies heavily on our prior research in goal modeling and operationalization, model matching and merging, and web service monitoring and recovery. We integrate this research with the social networking concept of crowd-sourcing to create a novel methodology for allowing users to produce customized workflows in order to accomplish their unique goals.

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Section: Mapping and Merging Of Goal Modelsmentioning

confidence: 99%

PWWM: A Personal Web Workflow Methodology

Chećhik

Simmonds

Liaskos

et al. 2013

Lecture Notes in Computer Science

Self Cite

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“…In both cases (symmetric and asymmetric), the merging operators satisfy the following properties, adapted from Brunet and Chechik [2], [3]:…”

Section: Composition Operators Propertiesmentioning

confidence: 99%

“…Indeed, these merged elements are the ones that have the same antecedents in A by f and g. As A, f and g are generated from the alignment rules, the operators merge only matching elements. The following property, also inspired from [2], [3], is true for a symmetric merge, but false for an asymmetric one:…”

Section: Composition Operators Propertiesmentioning

confidence: 99%

“…This transformation allows us to pinpoint the difference between merging and weaving operators. We also give several properties inspired from [2], [3], that are verified by one or both approaches, and allow further comparison and comprehension of the two kind of operators. Finally, we validate our framework by checking that it correctly identifies three tools from the literature, UML Package Merge [4], Kompose [5] and ADORE [6], as using a merging or a weaving operator.…”

Section: Introductionmentioning

confidence: 97%

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A categorical model of model merging and weaving

Marchand

Combemale

Baudry

2012

2012 4th International Workshop on Modeling in Software Engineering (MISE)

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Abstract-Model driven engineering advocates the separation of concerns during the design time of a system, which leads to the creation of several different models, using several different syntaxes. However, to reason on the overall system, we need to compose these models. Unfortunately, composition of models is done in an ad hoc way, preventing comparison, capitalisation and reuse of the composition operators. In order to improve comprehension and allow comparison of merging and weaving operators, we use category theory to propose a unified framework to formally define merging and weaving of models. We successfully use this framework to compare them, both through the way they are transformed in the formalism, and through several properties, such as completeness or nonredundancy. Finally, we validate this framework by checking that it correctly identifies three tools as performing merging or weaving of models.

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“…Model matching is the task of identifying semantic correspondences between elements of two models [3], [5]. The task is error-prone and time consuming.…”

mentioning

confidence: 99%

UML Class Diagrams: Similarity Aspects and Matching

Al-Khiaty¹,

Ahmed²

2016

LNSE

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Abstract-Model matching is at the core of different model management operations such as model evolution, consolidation, and retrieval. An accurate identification of the similarity and differences between the elements of the matched models leads to an accurate model matching, which, in turn, leads to better model management. Software metrics are the software engineer means to quantify the similarity between the elements of the matched models. In this paper, we empirically validate the use of different metrics for capturing the similarity and the differences between the elements of two matched UML class diagrams. The paper empirically investigates the improvement of the similarity assessment of the class diagrams through the weight calibration of compound metrics. The results, reported based on two case studies, show the superiority of the compound metrics over the individual metrics.Index Terms-Model matching, similarity metrics, reuse, weight calibration.

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A relationship-based approach to model integration

Cited by 31 publications

References 37 publications

PWWM: A Personal Web Workflow Methodology

PWWM: A Personal Web Workflow Methodology

A categorical model of model merging and weaving

UML Class Diagrams: Similarity Aspects and Matching

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