Towards Optimisation of Model Queries: A Parallel Execution Approach.

Madani, Sina; Kolovos, Dimitrios S.; Paige, Richard F.

doi:10.5381/jot.2019.18.2.a3

Cited by 11 publications

(17 citation statements)

References 7 publications

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“…Other areas with elements in the order of millions include Building Information Modelling and reverseengineered code from complex systems [25]. While executing complex and computationally expensive queries over such large models, there is a significant performance cost in terms of execution time [21]. Low-code platform is an instance of a generic model-driven platform.…”

Section: Motivationmentioning

confidence: 99%

“…In Listing 1, we have a constraint named SubsystemCounterpart (Line 1-6) that checks that for every Activity in UML model there exists a corresponding Subsystem in the Simulink model with the same name and vice-versa (Line 7-10). The constraints also check that requirements refer to valid subsystem names (Line [16][17][18][19][20][21] and that there is at least one requirement for every subsystem (Line [11][12][13][14][15]. Now, if we consider evaluating this constraint over a pair of large UML/Simulink models, it would become computationally expensive and slow to execute, as each UML activity will be checked against a large number of subsystems within the Simulink model.…”

Section: (B)mentioning

confidence: 99%

“…Cache all the precomputed collections in one pass. In [21], the authors present how combining three optimization techniques (parallelization, lazy evaluation, and short-circuiting) can significantly increase the performance of queries over large models. In [10], a tool called Mogwai is proposed for efficient and scalable querying.…”

Section: Related Workmentioning

confidence: 99%

See 2 more Smart Citations

Efficiently querying large-scale heterogeneous models

Ali

Kolovos

Barmpis

2020

Proceedings of the 23rd ACM/IEEE International Conference on Model Driven Engineering Languages and Systems: Companion Proceedi

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With the increase in the complexity of software systems, the size and the complexity of underlying models also increases proportionally. In a low-code system, models can be stored in different backend technologies and can be represented in various formats. Tailored high-level query languages are used to query such heterogeneous models, but typically this has a significant impact on performance. Our main aim is to propose optimization strategies that can help to query large models in various formats efficiently. In this paper, we present an approach based on compile-time static analysis and specific query optimizers/translators to improve the performance of complex queries over large-scale heterogeneous models. The proposed approach aims to bring efficiency in terms of query execution time and memory footprint, when compared to the naive query execution for low-code platforms. CCS CONCEPTS • Software and its engineering → Model-driven software engineering.

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Section: Motivationmentioning

confidence: 99%

Section: (B)mentioning

confidence: 99%

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Efficiently querying large-scale heterogeneous models

Ali

Kolovos

Barmpis

2020

Proceedings of the 23rd ACM/IEEE International Conference on Model Driven Engineering Languages and Systems: Companion Proceedi

Self Cite

View full text Add to dashboard Cite

show abstract

“…Moreover, EOL comes with imperative programming constructs such as loops and mutable variables. Finally, EOL also supports the parallel execution of some operations as well as laziness through data streams [35].…”

Section: Epsilon As a Complementary Modeling Technology For Model Permentioning

confidence: 99%

Scalable model views over heterogeneous modeling technologies and resources

et al. 2020

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When engineering complex systems, models are typically used to represent various systems aspects. These models are often heterogeneous in terms of modeling languages, provenance, number or scale. As a result, the information actually relevant to engineers is usually split into different kinds of interrelated models. To be useful in practice, these models need to be properly integrated to provide global views over the system. This has to be made possible even when those models are serialized or stored in different formats adapted to their respective nature and scalability needs. Model view approaches have been proposed to tackle this issue. They provide unification mechanisms to combine and query various different models in a transparent way. These views usually target specific engineering tasks such as system design, monitoring, evolution, etc. In an industrial context, there can be many large-scale use cases where model views can be beneficial, in order to trace runtime and design-time data for example. However, existing model view solutions are generally designed to work on top of one single modeling technology (even though

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“…Two main kinds of operation are targeted: the binary operations that can have their operands evaluated simultaneously, and the iterative processes of independent treatments. In [30], Madani et al use multi-threading for "select-based" operations in EOL, the OCL-like language of the Epsilon framework, for querying models. e extension of the language with parallel features for selective operations have shown a non-negligible speed-up (up to 6x with 16 cores) in their evaluations on a model conform to the Internet Movie Database (IMDb) metamodel 1 .…”

Section: Task-parallelismmentioning

confidence: 99%

Towards transparent combination of model management execution strategies for low-code development platforms

Philippe

Coullon

Tisi

et al. 2020

Proceedings of the 23rd ACM/IEEE International Conference on Model Driven Engineering Languages and Systems: Companion Proceedi

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Low-code development platforms are taking an important place in the model-driven engineering ecosystem, raising new challenges, among which transparent e ciency or scalability. Indeed, the increasing size of models leads to di culties in interacting with them e ciently. To tackle this scalability issue, some tools are built upon speci c computational strategies exploiting reactivity, or parallelism. However, their performances may vary depending on the speci c nature of their usage. Choosing the most suitable computational strategy for a given usage is a di cult task which should be automated. Besides, the most e cient solutions may be obtained by the use of several strategies at the same time. is paper motivates the need for a transparent multi-strategy execution mode for model-management operations. We present an overview of the di erent computational strategies used in the model-driven engineering ecosystem, and use a running example to introduce the bene ts of mixing strategies for performing a single computation. is example helps us present our design ideas for a multi-strategy model-management system. e code-related and DevOps challenges that emerged from this analysis are also presented. CCS CONCEPTS •So ware and its engineering →So ware creation and management; •Computing methodologies →Parallel algorithms;

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Towards Optimisation of Model Queries: A Parallel Execution Approach.

Cited by 11 publications

References 7 publications

Efficiently querying large-scale heterogeneous models

Efficiently querying large-scale heterogeneous models

Scalable model views over heterogeneous modeling technologies and resources

Towards transparent combination of model management execution strategies for low-code development platforms

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