KOBE: Cloud-Native Open Benchmarking Engine for Federated Query Processors

Kostopoulos, Charalampos; Mouchakis, Giannis; Troumpoukis, Antonis; Prokopaki-Kostopoulou, Nefeli; Charalambidis, Angelos; Konstantopoulos, Stasinos

doi:10.1007/978-3-030-77385-4_40

“…Experiment deployment and execution We use a Kubernetes 1.14 cluster with 1 master node and 8 worker nodes with a total if 120 cores and 264GB RAM. Experiment deployment and execution is done through the KOBE benchmarking engine 27 , and the KOBE configurations for reproducing the experiments are publicly available .…”

Section: Methodsmentioning

confidence: 99%

A geospatial source selector for federated GeoSPARQL querying

Troumpoukis¹,

Konstantopoulos²,

Prokopaki-Kostopoulou³

2022

Open Res Europe

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Background: Geospatial linked data brings into the scope of the Semantic Web and its technologies, a wealth of datasets that combine semantically-rich descriptions of resources with their geo-location. There are, however, various Semantic Web technologies where technical work is needed in order to achieve the full integration of geospatial data, and federated query processing is one of these technologies. Methods: In this paper, we explore the idea of annotating data sources with a bounding polygon that summarizes the spatial extent of the resources in each data source, and of using such a summary as an (additional) source selection criterion in order to reduce the set of sources that will be tested as potentially holding relevant data. We present our source selection method, and we discuss its correctness and implementation. Results: We evaluate the proposed source selection using three different types of summaries with different degrees of accuracy, against not using geospatial summaries. We use datasets and queries from a practical use case that combines crop-type data with water availability data for food security. The experimental results suggest that more complex summaries lead to slower source selection times, but also to more precise exclusion of unneeded sources. Moreover, we observe the source selection runtime is (partially or fully) recovered by shorter planning and execution runtimes. As a result, the federated sources are not burdened by pointless querying from the federation engine. Conclusions: The evaluation draws on data and queries from the agroenvironmental domain and shows that our source selection method substantially improves the effectiveness of federated GeoSPARQL query processing.

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“…Setting up federations Setting up a federation of 200 endpoints raises serious questions about the tractability of the experiment. Starting one physical endpoint per vendor/reviewing as proposed in KOBE [12] is not realistic if one considers large federations. We take another approach based on Virtual Endpoints as proposed in Virtuoso.…”

Section: Experimental Studymentioning

confidence: 99%

FedShop: A Benchmark for Testing the Scalability of SPARQL Federation Engines

Dang,

Aimonier-Davat,

Molli

et al. 2023

Lecture Notes in Computer Science

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While several approaches to query a federation of SPARQL endpoints have been proposed in the literature, very little is known about the effectiveness of these approaches and the behavior of the resulting query engines for cases in which the number of federation members increases. The existing benchmarks that are typically used to evaluate SPARQL federation engines do not consider such a form of scalability. In this paper, we set out to close this knowledge gap by investigating the behavior of 4 state-of-the-art SPARQL federation engines using a novel benchmark designed for scalability experiments. Based on the benchmark, we show that scalability is a challenge for each of these engines, especially with respect to the effectiveness of their source selection & query decomposition approaches. FedShop is freely available online at: https://github.com/GDD-Nantes/FedShop

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“…We use a Kubernetes 1.14 cluster with 1 master node and 8 worker nodes with a total if 120 cores and 264GB RAM. Experiment deployment and execution is done through the KOBE benchmarking engine 16 , and the KOBE configurations for reproducing the experiments are publicly available.…”

Section: Experiments Deployment and Executionmentioning

confidence: 99%

A geospatial source selector for federated GeoSPARQL querying

Troumpoukis¹,

Konstantopoulos²,

Prokopaki-Kostopoulou³

2022

View full text Add to dashboard Cite

Background: Geospatial linked data brings into the scope of the Semantic Web and its technologies, a wealth of datasets that combine semantically-rich descriptions of resources with their geo-location. There are, however, various Semantic Web technologies where technical work is needed in order to achieve the full integration of geospatial data, and federated query processing is one of these technologies. Methods: In this paper, we explore the idea of annotating data sources with a bounding polygon that summarizes the spatial extent of the resources in each data source, and of using such a summary as an (additional) source selection criterion in order to reduce the set of sources that will be tested as potentially holding relevant data. We present our source selection method, and we discuss its correctness and implementation. Results: We evaluate the proposed source selection using three different types of summaries with different degrees of accuracy, against not using geospatial summaries. We use datasets and queries from a practical use case that combines crop-type data with water availability data for food security. The experimental results suggest that more complex summaries lead to slower source selection times, but also to more precise exclusion of unneeded sources. Moreover, we observe the source selection runtime is (partially or fully) recovered by shorter planning and execution runtimes. As a result, the federated sources are not burdened by pointless querying from the federation engine. Conclusions: The evaluation draws on data and queries from the agroenvironmental domain and shows that our source selection method substantially improves the effectiveness of federated GeoSPARQL query processing.

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KOBE: Cloud-Native Open Benchmarking Engine for Federated Query Processors

Cited by 5 publications

References 8 publications

A geospatial source selector for federated GeoSPARQL querying

A geospatial source selector for federated GeoSPARQL querying

FedShop: A Benchmark for Testing the Scalability of SPARQL Federation Engines

A geospatial source selector for federated GeoSPARQL querying

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