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View all- Pham TMolli PNédelec BSkaf-Molli HAimonier-Davat J(2024)CRAWD: Sampling-Based Estimation of Count-Distinct SPARQL QueriesThe Semantic Web – ISWC 202410.1007/978-3-031-77850-6_6(98-115)Online publication date: 27-Nov-2024
Online Sampling of Summaries from Public SPARQL Endpoints
Pages 617 - 620
Abstract
Collecting statistics from online public SPARQL endpoints is hampered by their fair usage policies. These restrictions hinder several critical operations, such as aggregate query processing, portal development, and data summarization. Online sampling enables the collection of statistics while respecting fair usage policies. However, sampling has not yet been integrated into the SPARQL standard. Although integrating sampling into the SPARQL standard appears beneficial, its effectiveness must be demonstrated in a practical semantic web context. This paper investigates whether online sampling can generate summaries useful in cutting-edge SPARQL federation engines. Our experimental studies indicate that sampling allows the creation and maintenance of summaries by exploring less than 20% of datasets.
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References
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- Online Sampling of Summaries from Public SPARQL Endpoints
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Published In
May 2024
1928 pages
ISBN:9798400701726
DOI:10.1145/3589335
- General Chairs:
- Tat-Seng Chua,
- Chong-Wah Ngo,
- Program Chairs:
- Ravi Kumar,
- Hady W. Lauw,
- Roy Ka-Wei Lee
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Published: 13 May 2024
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- Agence Nationale de la Recherche
- Labex CominLabs
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View all- Pham TMolli PNédelec BSkaf-Molli HAimonier-Davat J(2024)CRAWD: Sampling-Based Estimation of Count-Distinct SPARQL QueriesThe Semantic Web – ISWC 202410.1007/978-3-031-77850-6_6(98-115)Online publication date: 27-Nov-2024
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