Efficiently querying large-scale heterogeneous models

Ali, Qurat Ul Ain; Kolovos, Dimitrios S.; Barmpis, Konstantinos

doi:10.1145/3417990.3420207

Cited by 8 publications

(7 citation statements)

References 14 publications

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“…The static analyzer first invokes the algorithm presented in [11], in charge of building the dependency graph between the rules based on the equivalent(s) operator used in statements of those rules [12]. The equivalent operator is a built-in operator of ETL which automatically resolves features of source elements to their transformed counterparts in the target models.…”

Section: Static Analyzermentioning

confidence: 99%

“…It computes type information of elements in model transformation and also it detects typing errors with respect to source and target metamodel. Static analysis can be used for a number of purposes such as optimisations in [11,17,18].…”

Section: External Threatsmentioning

confidence: 99%

“…The main idea is to analyze the transformation and propagate the analysis to the entire chain from the initial source to the final target. The static analyzer is a helper module based on Java and Epsilon, which allows interacting with a model transformation as a model, so it can be queried and managed for instance by using EOL scripts [11]. For every transformation rule, we check whether the target parameter(s) of the source model (see line X of listing 1) is the source parameter of any rule in the next intermediate or target model (see line X of listing 2).…”

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confidence: 99%

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Optimizing the Execution of Model Transformation Chain

Sahay

2024

Preprint

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Model transformation is the key concept in the field of model-driven engineering. To scale up the model transformation tasks in a cloud-based low-code platform, reusing and composing smaller model transformations enables a complex transformation to be executed sequentially and optimally. To achieve reusing and composition of smaller transformations, we need to identify all the model transformation chains possible in the repository/folder to transform the source model to the target model. Then, with the help of static analysis, the total structural features involved in a model transformation are calculated and the chain with optimum quality criteria are considered to be the best chain. In this paper, we propose an approach that takes the benefit of static analysis and automatic program rewriting to optimize the selected chain. The approach will rewrite the transformations to keep only the rules and bindings that generate the intermediate model elements required to generate target model elements in the model transformation chain. The performance is improved by reducing the execution time and intermediate elements generated required to execute the selected transformation chain. This optimizing the chain' elements fasten the execution of the whole model transformation chain. The optimization of the execution of the model transformation chain helps in achieving the scalable solution that manages several workflows originating from heterogeneous model-driven platforms.

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Section: Static Analyzermentioning

confidence: 99%

Section: External Threatsmentioning

confidence: 99%

mentioning

confidence: 99%

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Optimizing the Execution of Model Transformation Chain

Sahay

2024

Preprint

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“…In our previous work, we used static analysis for enabling the translation from EOL to SQL [13]. A solution for efficient querying large-scale databases is presented in [14], where OCL queries are translated to SQL at runtime.…”

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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“…We have evaluated the proposed optimisation technique using large models that have been reverseengineered from Java code and a set of existing constraints, and we have observed performance improvements of up to 99.56%. A speculative overview of the proposed approach was first presented in a workshop paper [3] but without a supporting implementation, or the ability to carry out evaluation experiments, at that stage.…”

Section: Introductionmentioning

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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Efficiently querying large-scale heterogeneous models

Cited by 8 publications

References 14 publications

Optimizing the Execution of Model Transformation Chain

Optimizing the Execution of Model Transformation Chain

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

Identification and Optimisation of Type-Level Model Queries

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