Efficiently Querying Large-Scale Heterogeneous Models

Qurat ul ain Ali; Dimitris Kolovos; Konstantinos Barmpis

doi:10.1145/3417990.3420207

Efficiently Querying Large-Scale Heterogeneous Models

Qurat ul ain Ali, Dimitris Kolovos, Konstantinos Barmpis

Computer Science

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Abstract

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.

Original language	English
Title of host publication	Proceedings of the 23rd ACM/IEEE International Conference on Model Driven Engineering Languages and Systems: Companion Proceedings
Place of Publication	New York, NY, USA
Publisher	ACM
ISBN (Print)	9781450381352
DOIs	https://doi.org/10.1145/3417990.3420207
Publication status	Published - 27 Oct 2020

Publication series

Name	MODELS '20
Publisher	Association for Computing Machinery

Keywords

scalability
model-driven engineering
model querying
static analysis

Access to Document

10.1145/3417990.3420207

Quratulain-LowcodeAccepted author manuscript, 830 KB

https://doi.org/10.1145/3417990.3420207

Cite this

@inproceedings{8d5e1cc27a3e4b7f904562b87acf9863,

title = "Efficiently Querying Large-Scale Heterogeneous Models",

abstract = "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.",

keywords = "scalability, model-driven engineering, model querying, static analysis",

author = "Ali, {Qurat ul ain} and Dimitris Kolovos and Konstantinos Barmpis",

year = "2020",

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Efficiently Querying Large-Scale Heterogeneous Models. / Ali, Qurat ul ain ; Kolovos, Dimitris ; Barmpis, Konstantinos.
Proceedings of the 23rd ACM/IEEE International Conference on Model Driven Engineering Languages and Systems: Companion Proceedings. New York, NY, USA: ACM, 2020. (MODELS '20).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

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