Data placement strategies that speed-up distributed graph query processing

Janke, Daniel; Staab, Steffen; Leinberger, Martin

doi:10.1145/3391274.3393633

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2022

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Cited by 2 publications

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“…Additionally there are various clustering strategies [1,12,14,15,19,21,22,29]. Clustering algorithms work best if the data doesn't change.…”

Section: Semantic Web Databasesmentioning

confidence: 99%

A SPARQL benchmark for distributed databases in IoT environments

Warnke

Mantler

Groppe

et al. 2022

Proceedings of the International Workshop on Big Data in Emergent Distributed Environments

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Data transmission is one of the biggest energy consumers in low power networks. So, any application running in such environment must reduce the volume of communication caused by its presence. We want to deploy our database on a wireless sensor network. Therefore, we want to gain insights into the interoperability between routing and application in order to reduce communication costs. To do this, we propose our new benchmark framework in this contribution. Using this benchmark framework, we compare state-of-the-art database indexes and how much traffic they cause in a fully distributed environment. We believe that computing speed shouldn't be the only metric to compare databases with each other.

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“…Additionally there are various clustering strategies [1,12,14,15,19,21,22,29]. Clustering algorithms work best if the data doesn't change.…”

Section: Semantic Web Databasesmentioning

confidence: 99%