A Semantic Search Capability for a Grid Model Repository

Nielsen, Terry; Rouillard, Travis; Makarov, Nickolay

doi:10.1109/pesgm40551.2019.8973955

Cited by 1 publication

(2 citation statements)

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“…Model repositories offer a valuable resource for users who are interested in acquiring knowledge from already developed modeling artifacts. In recent years, the adoption of repositories is increasingly becoming commonplace in different application domains including power grids [63], biology, e.g., BioModels Database, 3 CellML 4 ; and software development, e.g., GitHub, 5 SourceForge 6 ; or MDE development, e.g., ReMoDD [29], ATL Zoos, 7 MDEForge [11], to name a few. Since the searchability and discoverability of metamodels strongly impact on the reuse of repositories, such platforms have to provide domain experts with advanced searching algorithms and facilities, which are suitable to manage large datasets and to enable quick identification of stored items [63].…”

Section: Motivating Examplementioning

confidence: 99%

“…In recent years, the adoption of repositories is increasingly becoming commonplace in different application domains including power grids [63], biology, e.g., BioModels Database, 3 CellML 4 ; and software development, e.g., GitHub, 5 SourceForge 6 ; or MDE development, e.g., ReMoDD [29], ATL Zoos, 7 MDEForge [11], to name a few. Since the searchability and discoverability of metamodels strongly impact on the reuse of repositories, such platforms have to provide domain experts with advanced searching algorithms and facilities, which are suitable to manage large datasets and to enable quick identification of stored items [63]. To give an intuition about the possible extension of a publicly available dataset of models, we refer to GenMyModel, 8 an online modeling platform that offers a predefined set of modeling formalisms, e.g., UML, BPMN, Flowchart.…”

Section: Motivating Examplementioning

confidence: 99%

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Evaluation of a machine learning classifier for metamodels

et al. 2021

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Modeling is a ubiquitous activity in the process of software development. In recent years, such an activity has reached a high degree of intricacy, guided by the heterogeneity of the components, data sources, and tasks. The democratized use of models has led to the necessity for suitable machinery for mining modeling repositories. Among others, the classification of metamodels into independent categories facilitates personalized searches by boosting the visibility of metamodels. Nevertheless, the manual classification of metamodels is not only a tedious but also an error-prone task. According to our observation, misclassification is the norm which leads to a reduction in reachability as well as reusability of metamodels. Handling such complexity requires suitable tooling to leverage raw data into practical knowledge that can help modelers with their daily tasks. In our previous work, we proposed AURORA as a machine learning classifier for metamodel repositories. In this paper, we present a thorough evaluation of the system by taking into consideration different settings as well as evaluation metrics. More importantly, we improve the original AURORA tool by changing its internal design. Experimental results demonstrate that the proposed amendment is beneficial to the classification of metamodels. We also compared our approach with two baseline algorithms, namely gradient boosted decision tree and support vector machines. Eventually, we see that AURORA outperforms the baselines with respect to various quality metrics.

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