Towards Incremental Updates in Large-Scale Model Indexes

Barmpis, Konstantinos; Shah, Seyyed; Kolovos, Dimitrios S.

doi:10.1007/978-3-319-21151-0_10

Cited by 6 publications

(14 citation statements)

References 11 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%

“…As discussed in [9] this leverages the benefits of widely-used file-based version control systems such as SVN and Git, but retains the issues file-based models face. To address this issue a model indexer can be introduced that monitors the models and indices them in a scalable model index.…”

Section: Heterogeneous Model Indexingmentioning

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

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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: Heterogeneous Model Indexingmentioning

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

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show abstract

“…For databasebacked models, the query must be transformed to an efficient use of the database, and the database must provide a high level of performance. This is the approach taken by Mogwaï, 2 a query engine for models stored in the NeoEMF 3 layer that transforms OCL queries into Gremlin 4 API calls. -Collaborative work: multiple developers being able to query, modify and version control large-scale shared models in a non-invasive manner.…”

Section: Introductionmentioning

confidence: 99%

“…3 https://github.com/atlanmod/NeoEMF. 4 http://tinkerpop.apache.org/gremlin.html. 5 http://wiki.eclipse.org/CDO.…”

Section: Introductionmentioning

confidence: 99%

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

et al. 2017

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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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Incremental execution of temporal graph queries over runtime models with history and its applications

et al. 2021

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Modern software systems are intricate and operate in highly dynamic environments for which few assumptions can be made at design-time. This setting has sparked an interest in solutions that use a runtime model which reflects the system state and operational context to monitor and adapt the system in reaction to changes during its runtime. Few solutions focus on the evolution of the model over time, i.e., its history, although history is required for monitoring temporal behaviors and may enable more informed decision-making. One reason is that handling the history of a runtime model poses an important technical challenge, as it requires tracing a part of the model over multiple model snapshots in a timely manner. Additionally, the runtime setting calls for memory-efficient measures to store and check these snapshots. Following the common practice of representing a runtime model as a typed attributed graph, we introduce a language which supports the formulation of temporal graph queries, i.e., queries on the ordering and timing in which structural changes in the history of a runtime model occurred. We present a querying scheme for the execution of temporal graph queries over history-aware runtime models. Features such as temporal logic operators in queries, the incremental execution, the option to discard history that is no longer relevant to queries, and the in-memory storage of the model, distinguish our scheme from relevant solutions. By incorporating temporal operators, temporal graph queries can be used for runtime monitoring of temporal logic formulas. Building on this capability, we present an implementation of the scheme that is evaluated for runtime querying, monitoring, and adaptation scenarios from two application domains.

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Towards Incremental Updates in Large-Scale Model Indexes

Cited by 6 publications

References 11 publications

Stress-Testing Centralised Model Stores

Stress-Testing Centralised Model Stores

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

Incremental execution of temporal graph queries over runtime models with history and its applications

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