Towards Scalable Querying of Large-Scale Models

Barmpis, Konstantinos; Kolovos, Dimitrios S.

doi:10.1007/978-3-319-09195-2_3

Cited by 17 publications

(16 citation statements)

References 7 publications

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“…O3 and O4 confirm the findings of our previous work in scalable querying [9,14]: adding derived attributes to reduce the levels of iteration required in a query speeds up running times by orders of magnitude, while adding minimal overhead due to the use of incremental updating. These derived attributes can be seen as application-specific caches that precompute parts of a query, unlike the application-agnostic caches present in CDO.…”

Section: Rq3: Impact Of Tool Internalssupporting

confidence: 79%

“…To evaluate Hawk, we used the three EOL implementations of the GraBaTs query of our previous work [13]. The first version of the query ("Hawk query 1" or HQ1, shown in Listing 2) is a translation of OQ to EOL, and follows the same approach.…”

Section: Queries Under Studymentioning

confidence: 99%

“…The query starts off from the MethodDeclarations so Hawk can take advantage of the fact that derived attributes are also indexed, so Hawk can use lookups instead of iterations to find the methods of interest. A detailed discussion about how derived attributes are declared in Hawk and how they are incrementally re-computed upon model changes is available in our previous works [9,14].…”

Section: Queries Under Studymentioning

confidence: 99%

See 2 more Smart Citations

Stress-Testing Centralised Model Stores

García-Domínguez

Barmpis

Kolovos

et al. 2016

Modelling Foundations and Applications

Self Cite

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Abstract. One of the current challenges in model-driven engineering is enabling effective collaborative modelling. Two common approaches are either storing the models in a central repository, or keeping them under a traditional file-based version control system and build a centralized index for model-wide queries. Either way, special attention must be paid to the nature of these repositories and indexes as networked services: they should remain responsive even with an increasing number of concurrent clients. This paper presents an empirical study on the impact of certain key decisions on the scalability of concurrent model queries, using an Eclipse Connected Data Objects model repository and a Hawk model index. The study evaluates the impact of the network protocol, the API design and the internal caching mechanisms and analyzes the reasons for their varying performance.

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Section: Rq3: Impact Of Tool Internalssupporting

confidence: 79%

Section: Queries Under Studymentioning

confidence: 99%

Section: Queries Under Studymentioning

confidence: 99%

See 1 more Smart Citation

Stress-Testing Centralised Model Stores

García-Domínguez

Barmpis

Kolovos

et al. 2016

Modelling Foundations and Applications

Self Cite

View full text Add to dashboard Cite

show abstract

“…To evaluate Hawk, we used the three EOL implementations of the GraBaTs query of our previous work [3]. The first version of the query ("Hawk query 1" or HQ1, shown in Listing 2) is a translation of OQ to EOL and follows the same approach.…”

Section: Singletons In Java Models: Grabats'2009 Queriesmentioning

confidence: 99%

“…2 https://github.com/atlanmod/Mogwai. 3 https://github.com/atlanmod/NeoEMF. 4 http://tinkerpop.apache.org/gremlin.html.…”

Section: Introductionmentioning

confidence: 99%

Stress-testing remote model querying APIs for relational and graph-based stores

et al. 2017

Self Cite

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Recent research in scalable model-driven engineering now allows very large models to be stored and queried. Due to their size, rather than transferring such models over the network in their entirety, it is typically more efficient to access them remotely using networked services (e.g. model repositories, model indexes). Little attention has been paid so far to the nature of these services, and whether they remain responsive with an increasing number of concurrent clients. This paper extends a previous empirical study on the impact of certain key decisions on the scalability of concurrent model queries on two domains, using an Eclipse Connected Data Objects model repository, four configurations of the Hawk model index and a Neo4j-based configuration of the NeoEMF model store. The study evaluates the impact of the network protocol, the API design, the caching layer, the query language and the type of database and analyses the reasons for their varying levels of performance. The design of the API was shown to make a bigger difference compared to the network protocol (HTTP/TCP) used. Where available, the query-specific indexed and derived attributes in Hawk outperformed the comprehensive generic caching in CDO. Finally, the results illustrate the still ongoing evolution of graph databases: two tools using different versions of the same backend had very different performance, with one slower than CDO and the other faster than it.

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Advanced discovery mechanisms in model repositories

Indamutsa,

Di Rocco,

Almonte

et al. 2024

Softw Pract Exp

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SummaryAs model‐driven engineering gains traction and poses as the new paradigm for software engineering, it raises a need for efficient approaches and tools to manage, discover, and retrieve relevant modeling artifacts. Hence, industry and academia are conceiving effective ways to store, search, and retrieve heterogeneous model artifacts that employ advanced discovery mechanisms. This paper presents MDEForge‐Search, a novel approach to discovering heterogeneous model artifacts over MDEForge, a distributed cloud‐based model repository. We designed advanced discovery mechanisms that retrieve heterogeneous artifacts within their context (megamodel) and reuse them across model management services. In addition, a domain‐specific approach has been proposed to formulate queries in terms of keywords, search tags, conditional operators, quality model assessment services and a transformation chain discoverer. Finally, the applicability of our approach was assessed in a recommender system modeling framework, which, thanks to the operated integration, can rely on the availability of more than 5000 model artifacts currently persisted in our cloud‐based model repository.

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Towards Scalable Querying of Large-Scale Models

Cited by 17 publications

References 7 publications

Stress-Testing Centralised Model Stores

Stress-Testing Centralised Model Stores

Stress-testing remote model querying APIs for relational and graph-based stores

Advanced discovery mechanisms in model repositories

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