Scalable Queries and Model Transformations with the Mogwaï Tool

Daniel, Gwendal; Sunyé, Gerson; Cabot, Jordi

doi:10.1007/978-3-319-93317-7_9

Cited by 8 publications

(5 citation statements)

References 9 publications

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“…In terms of performance, new incremental [EKK + 13], parallel [BWV16], distributed [BGTC18] or NoSQL-based [DSC18] transformation engines were developed.…”

Section: State Of the Artmentioning

confidence: 99%

The Future of Model Transformation Languages: An Open Community Discussion.

Burgueño¹,

Cabot²,

Gérard³

2019

JOT

Self Cite

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Model transformations are the key element that brings life to model-driven engineering. Animation, simulations, VV, code-generation, etc. all depend on some kind of model transformation to work. Model transformations are typically defined via specialized model transformation languages but this is now in question due to the lack of convincing evidence that specialised languages are substantially better than generalpurpose languages for model transformation specification, and the rise of artificial intelligence. We report on the results of an open discussion with the model transformation community on the future of these languages, including whether such a future exists at all.

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“…In terms of performance, new incremental [EKK + 13], parallel [BWV16], distributed [BGTC18] or NoSQL-based [DSC18] transformation engines were developed.…”

Section: State Of the Artmentioning

confidence: 99%

The Future of Model Transformation Languages: An Open Community Discussion.

Burgueño¹,

Cabot²,

Gérard³

2019

JOT

Self Cite

View full text Add to dashboard Cite

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“…We use parallelisation proposed in [24] as a comparison baseline for our approach. In [25], a tool called Mogwai is proposed for efficient and scalable querying. Mogwai maps OCL and ATL expressions to Gremlin scripts -a query language for NoSQL databases.…”

Section: Related Workmentioning

confidence: 99%

Identification and Optimisation of Type-Level Model Queries

Ali

Kolovos

Barmpis

2021

2021 ACM/IEEE International Conference on Model Driven Engineering Languages and Systems Companion (MODELS-C)

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The main appeal of task-specific model management languages such as ATL, OCL, Epsilon etc. is that they offer tailored syntaxes for the tasks they target, and provide concise first-class support for recurring activities in these tasks. On the flip side, task-specific model management languages are typically interpreted and are therefore significantly slower than generalpurpose programming languages (which can be also used to query and modify models) such as Java. While this is not an issue for smaller models, as models grow in size, naive execution of interpreted model management programs against them can become a scalability bottleneck. In this paper, we demonstrate an architecture for optimisation of model management programs written in languages of the Epsilon platform using static analysis and program rewriting techniques. The proposed architecture facilitates optimisation of queries that target models of heterogeneous technologies in an orthogonal way. We demonstrate how the proposed architecture is used to identify and optimise typelevel queries against EMF-based models in the context of EOL programs and EVL validation constraints. We also demonstrate the performance benefits that can be delivered by this form of optimisation through a series of experiments on EMF-based models. Our experiments have shown performance improvements of up to 99.56%.

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“…Mogwai [16] is a tool to efficiently query large-scale models persisted in NoSQL backends. It translates the scripts written in OCL and ATL to Gremlin, a native query language for NoSQL databases.…”

Section: B Threats To Validitymentioning

confidence: 99%

Towards Scalable Validation of Low-Code System Models: Mapping EVL to VIATRA Patterns

Ali

Horváth

Kolovos

et al. 2021

2021 ACM/IEEE International Conference on Model Driven Engineering Languages and Systems Companion (MODELS-C)

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Adoption of low-code engineering in complex enterprise applications also increases the size of the underlying models. In such cases, the increasing complexity of the applications and the growing size of the underlying artefacts, various scalability challenges might arise for low-code platforms. Task-specific programming languages, such as OCL and EOL, are tailored to manage the underlying models. Existing model management languages have significant performance impact when it comes to complex queries operating over large-scale models reaching magnitudes of millions of elements in size. We propose an approach for automatically mapping expressions in Epsilon validation programs to VIATRA graph patterns to make the validation of large-scale low-code system models scalable by leveraging the incremental execution engine of VIATRA. Finally, we evaluate the performance of the proposed approach on large Java models of the Eclipse source code. Our results show performance speed-up up to 1481x compared to the sequential execution in Epsilon.

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Scalable Queries and Model Transformations with the Mogwaï Tool

Cited by 8 publications

References 9 publications

The Future of Model Transformation Languages: An Open Community Discussion.

The Future of Model Transformation Languages: An Open Community Discussion.

Identification and Optimisation of Type-Level Model Queries

Towards Scalable Validation of Low-Code System Models: Mapping EVL to VIATRA Patterns

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