SPARQLES: Monitoring public SPARQL endpoints

Vandenbussche, Pierre-Yves; Umbrich, Jürgen; Matteis, Luca; Hogan, Aidan; Buil-Aranda, Carlos

doi:10.3233/sw-170254

Cited by 58 publications

(62 citation statements)

References 14 publications

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“…Studies have started to show the extent of the problem [Vandenbussche et al, 2013] and a few proposals exist for specific technological solutions to improve the availability, especially of SPARQL endpoints [Verborgh et al, 2014]. is certainly represents a shift from the traditional environment in which very large databases are being developed, where complex querying and processing is only available to the administrator of the database, and the ability for external agents to access the content of the database is restricted to a few pre-defined and well tested channels.…”

Section: Technological Challenges: Scale Robustness and Distributiomentioning

confidence: 99%

The Epistemology of Intelligent Semantic Web Systems

d’Aquin

Motta

2016

Synthesis Lectures on the Semantic Web: Theory and Technology

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Section: Technological Challenges: Scale Robustness and Distributiomentioning

confidence: 99%

The Epistemology of Intelligent Semantic Web Systems

d’Aquin

Motta

2016

Synthesis Lectures on the Semantic Web: Theory and Technology

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“…This approach is as good as the query endpoints that it relies on. Unfortunately, SPARQL endpoints are known to have low availability [7,21], and federated queries are difficult to optimize beyond a limited number of sources [17].…”

Section: Introductionmentioning

confidence: 99%

LOD-a-lot

Fernández

Beek

Martínez‐Prieto

et al. 2017

Lecture Notes in Computer Science

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Abstract. LOD-a-lot democratizes access to the Linked Open Data (LOD) Cloud by serving more than 28 billion unique triples from 650K datasets over a single self-indexed file. This corpus can be queried online with a sustainable Linked Data Fragments interface, or downloaded and consumed locally: LOD-a-lot is easy to deploy and demands affordable resources (524 GB of disk space and 15.7 GB of RAM), enabling Webscale repeatable experimentation and research even by standard laptops.

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“…1 Recent studies reveal unreliability and unavailability of existing public SPARQL endpoints [4]. According to the SPAR-QLES monitoring system [32] less than a third out of the 545 studied public endpoints exhibits an availability rate of 99-100% (values for November 2015).…”

Section: Introductionmentioning

confidence: 99%

Decomposing Federated Queries in Presence of Replicated Fragments

et al. 2017

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Federated query engines allow for linked data consumption using SPARQL endpoints. Replicating data fragments from different sources enables data re-organization and provides the basis for more effective and efficient federated query processing. However, existing federated query engines are not designed to support replication. In this paper, we propose a replication-aware framework named LILAC, sparqL query decomposItion against federations of repLicAted data sourCes, that relies on replicated fragment descriptions to accurately identify sources that provide replicated data. We defined the query decomposition problem with fragment replication (QDP-FR). QDP-FR corresponds to the problem of finding the sub-queries to be sent to the endpoints that allows the federated query engine to compute the query answer, while the number of tuples to be transferred from endpoints to the federated query engine is minimized. An approximation of QDP-FR is implemented by the LILAC replication-aware query decomposition algorithm. Further, LILAC techniques have been included in the state-of-the-art federated query engines FedX and ANAPSID to evaluate the benefits of the proposed source selection and query decomposition techniques in different engines. Experimental results suggest that LILAC efficiently solves QDP-FR and is able to reduce the number of transferred tuples and the execution time of the studied engines. (Gabriela Montoya), hala.skaf@univ-nantes.fr (Hala Skaf-Molli), pascal.molli@univ-nantes.fr (Pascal Molli), mvidal@ldc.usb.ve (Maria-Esther Vidal) 1 http://stats.lod2.eu designed. Clearly, any data provider can partially or totally replicate datasets from other data providers. The LOD Cloud Cache SPARQL endpoint 2 is an example of an endpoint that provides access to total replicas of several datasets. DBpedia live 3 allows a third party to replicate DBpedia live changes in almost real-time. Data consumers may also replicate RDF datasets for efficient and reliable execution of their applications. However, given the size of the LOD cloud datasets, data consumers may just replicate subsets of RDF datasets or replicated fragments in a way that their applications can be efficiently executed. Partial replication allows for speeding up query execution time. Partial replication can be facilitated by data providers, e.g., DBpedia 2016-04 4 consists of over seventy dump files each of them providing different fragments of the same dataset, or can be facilitated by third party systems. Publish-Subscribe systems such as sparqlPuSH [25] or iRap RDF Update Propagation Framework [11] allow to partially replicate datasets. Additionally, data consumers are also autonomous and can declare federations composed 2 7 Containment testing is adapted from [13]. 8 The substitution operator preserves URIs and literals, i.e., only variables are substituted.

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SPARQLES: Monitoring public SPARQL endpoints

Cited by 58 publications

References 14 publications

The Epistemology of Intelligent Semantic Web Systems

The Epistemology of Intelligent Semantic Web Systems

LOD-a-lot

Decomposing Federated Queries in Presence of Replicated Fragments

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