A Benchmark Suite for Federated SPARQL Query Processing from Existing Workflows

Antonis Troumpoukis; Angelos Charalambidis; Giannis Mouchakis; Stasinos Konstantopoulos; Daniela Digles; Ronald Siebes; Victor de Boer; Stian Soiland-Reyes

A Benchmark Suite for Federated SPARQL Query Processing from Existing Workflows

Antonis Troumpoukis, Angelos Charalambidis, Giannis Mouchakis, Stasinos Konstantopoulos, Daniela Digles, Ronald Siebes, Victor de Boer, Stian Soiland-Reyes

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Abstract

This paper presents a new benchmark suite for SPARQL query processors. The benchmark is derived from workflows established by the pharmacology community and exploits the fact that these workflows are not only applied to voluminous data, but they are also equivalent to complex and challenging queries. The value of this queryset is that it realistically represents actual community needs in a challenging domain, testing not only speed and robustness to large data volumes but also all features of modern query processing systems.

In addition, the natural partitioning of the data into meaningful datasets makes these workflows ideal for benchmarking *federated* query processors. This emphasis on federated query processing drived complementing the benchmark with an execution engine that can reproduce distributed and federated query processing experiments.

Original language	English
Publication status	Submitted - 15 Mar 2017

Keywords

Triple store benchmarking
Pharmacology data
Distributed and federated querying

Research Beacons, Institutes and Platforms

Institute for Data Science and AI

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Submitted to SWJ 2017-03-15Submitted manuscript, 162 KBLicence: CC BY

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title = "A Benchmark Suite for Federated SPARQL Query Processing from Existing Workflows",

abstract = "This paper presents a new benchmark suite for SPARQL query processors. The benchmark is derived from workflows established by the pharmacology community and exploits the fact that these workflows are not only applied to voluminous data, but they are also equivalent to complex and challenging queries. The value of this queryset is that it realistically represents actual community needs in a challenging domain, testing not only speed and robustness to large data volumes but also all features of modern query processing systems.In addition, the natural partitioning of the data into meaningful datasets makes these workflows ideal for benchmarking *federated* query processors. This emphasis on federated query processing drived complementing the benchmark with an execution engine that can reproduce distributed and federated query processing experiments.",

keywords = "Triple store benchmarking, Pharmacology data, Distributed and federated querying",

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AU - Troumpoukis, Antonis

AU - Charalambidis, Angelos

AU - Mouchakis, Giannis

AU - Konstantopoulos, Stasinos

AU - Digles, Daniela

AU - Siebes, Ronald

AU - de Boer, Victor

AU - Soiland-Reyes, Stian

N1 - Submitted on 2017-03-15 to Semanatic Web Journal call for "Special issue on Benchmarking Linked Data" http://www.semantic-web-journal.net/blog/special-issue-benchmarking-linked-data Decision: Reject

PY - 2017/3/15

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N2 - This paper presents a new benchmark suite for SPARQL query processors. The benchmark is derived from workflows established by the pharmacology community and exploits the fact that these workflows are not only applied to voluminous data, but they are also equivalent to complex and challenging queries. The value of this queryset is that it realistically represents actual community needs in a challenging domain, testing not only speed and robustness to large data volumes but also all features of modern query processing systems.In addition, the natural partitioning of the data into meaningful datasets makes these workflows ideal for benchmarking *federated* query processors. This emphasis on federated query processing drived complementing the benchmark with an execution engine that can reproduce distributed and federated query processing experiments.

AB - This paper presents a new benchmark suite for SPARQL query processors. The benchmark is derived from workflows established by the pharmacology community and exploits the fact that these workflows are not only applied to voluminous data, but they are also equivalent to complex and challenging queries. The value of this queryset is that it realistically represents actual community needs in a challenging domain, testing not only speed and robustness to large data volumes but also all features of modern query processing systems.In addition, the natural partitioning of the data into meaningful datasets makes these workflows ideal for benchmarking *federated* query processors. This emphasis on federated query processing drived complementing the benchmark with an execution engine that can reproduce distributed and federated query processing experiments.

KW - Triple store benchmarking

KW - Pharmacology data

KW - Distributed and federated querying

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UR - https://github.com/semagrow/sevod-scraper

M3 - Paper

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A Benchmark Suite for Federated SPARQL Query Processing from Existing Workflows

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Keywords

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