RDFStats - An Extensible RDF Statistics Generator and Library

Langegger, Andreas; Wöß, Wolfram

doi:10.1109/dexa.2009.25

Cited by 48 publications

(37 citation statements)

References 4 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Query optimization: Research on query optimization for SPARQL includes query rewriting [18], join re-ordering based on selectivity estimations [25,4,28], and other statistical information gathering over RDF sources [22,15]. RDFStats [22] is an extensible RDF statistics generator that records how often RDF properties are used and feeds automatically generated histograms to SemWIQ.…”

Section: Related Workmentioning

confidence: 99%

Querying a Messy Web of Data with Avalanche

Basca

Bernstein

2014

SSRN Journal

View full text Add to dashboard Cite

Recent efforts have enabled applications to query the entire Semantic Web. Such approaches are either based on a centralised store or link traversal and URI dereferencing as often used in the case of Linked Open Data. These approaches make additional assumptions about the structure and/or location of data on the Web and are likely to limit the diversity of resulting usages. In this article we propose a technique called Avalanche, designed for querying the Semantic Web without making any prior assumptions about the data location or distribution, schema-alignment, pertinent statistics, data evolution, and accessibility of servers. Specifically, Avalanche finds up-to-date answers to queries over SPARQL endpoints. It first gets on-line statistical information about potential data sources and their data distribution. Then, it plans and executes the query in a concurrent and distributed manner trying to quickly provide first answers. We empirically evaluate Avalanche using the realistic FedBench data-set over 26 servers and investigate its behaviour for varying degrees of instance-level distribution "messiness" using the LUBM synthetic data-set spread over 100 servers. Results show that Avalanche is robust and stable in spite of varying network latency finding first results for 80% of the queries in under 1 second. It also exhibits stability for some classes of queries when instance-level distribution messiness increases. We also illustrate, how Avalanche addresses the other sources of messiness (pertinent data statistics, data evolution and data presence) by design and show its robustness by removing endpoints during query execution. AbstractRecent efforts have enabled applications to query the entire Semantic Web. Such approaches are either based on a centralised store or link traversal and URI dereferencing as often used in the case of Linked Open Data. These approaches make additional assumptions about the structure and/or location of data on the Web and are likely to limit the diversity of resulting usages.In this article we propose a technique called Avalanche, designed for querying the Semantic Web without making any prior assumptions about the data location or distribution, schema-alignment, pertinent statistics, data evolution, and accessibility of servers. Specifically, Avalanche finds up-to-date answers to queries over SPARQL endpoints. It first gets on-line statistical information about potential data sources and their data distribution. Then, it plans and executes the query in a concurrent and distributed manner trying to quickly provide first answers.We empirically evaluate Avalanche using the realistic FedBench data-set over 26 servers and investigate its behaviour for varying degrees of instance-level distribution "messiness" using the LUBM synthetic dataset spread over 100 servers. Results show that Avalanche is robust and stable in spite of varying network latency finding first results for 80% of the queries in under 1 second. It also exhibits stability for some classes of queries when ins...

show abstract

Section: Related Workmentioning

confidence: 99%

Querying a Messy Web of Data with Avalanche

Basca

Bernstein

2014

SSRN Journal

View full text Add to dashboard Cite

show abstract

“…While these descriptions could be published as Linked Data as well, there are no agreed-upon vocabularies to describe this meta-data, yet. Nonetheless, there are first approaches such as the vocabulary of interlinked datasets [2], the service description vocabulary for SPARQL [34], and the RDFstats vocabulary [21].…”

Section: Active Discovery Based Query Federationmentioning

confidence: 99%

A Database Perspective on Consuming Linked Data on the Web

Hartig

Langegger

2010

Datenbank Spektrum

Self Cite

View full text Add to dashboard Cite

During recent years an increasing number of data providers adopted the Linked Data principles for publishing and connecting structured data on the Web, thus creating a globally distributed dataspacethe Web of Data. While the execution of structured, SQL-like queries over this dataspace opens possibilities not conceivable before, query execution on the Web of Data poses novel challenges. These challenges provide great opportunities for the database community.In this article we introduce the concept of Linked Data and discuss different approaches to query the Web of Data. Our goal is to provide a general understanding of this new research area and of the challenges and open issues that must be addressed.

show abstract

“…Little work has been done in the area of RDF statistics, mainly including make-void 5 and rdfstats [9]. Make-void is written in Java and utilizes the Jena toolkit to import RDF data and generate statistics conforming to VoID using SPARQL queries via Jena's ARQ SPARQL processor.…”

Section: Related Workmentioning

confidence: 99%

“…possible elements as subjects, predicates and objects in an RDF statement, composition of IRIs (comprising namespaces) and literals (comprising datatypes and language tags, (2) surveying and combining statistical criteria from related work particularly from VoID and RDFStats [9], (3) expert interviews, which we performed with representatives from the AKSW research group and the LOD2 project. forall the Criteria C P CS do execute C.P and return results…”

Section: Statistical Criteria Surveymentioning

confidence: 99%

LODStats – An Extensible Framework for High-Performance Dataset Analytics

Auer

Demter

Martin

et al. 2012

Lecture Notes in Computer Science

116

102

View full text Add to dashboard Cite

Abstract. One of the major obstacles for a wider usage of web data is the difficulty to obtain a clear picture of the available datasets. In order to reuse, link, revise or query a dataset published on the Web it is important to know the structure, coverage and coherence of the data. In order to obtain such information we developed LODStats -a statement-stream-based approach for gathering comprehensive statistics about datasets adhering to the Resource Description Framework (RDF). LODStats is based on the declarative description of statistical dataset characteristics. Its main advantages over other approaches are a smaller memory footprint and significantly better performance and scalability. We integrated LODStats with the CKAN dataset metadata registry and obtained a comprehensive picture of the current state of a significant part of the Data Web.

show abstract

RDFStats - An Extensible RDF Statistics Generator and Library

Cited by 48 publications

References 4 publications

Querying a Messy Web of Data with Avalanche

Querying a Messy Web of Data with Avalanche

A Database Perspective on Consuming Linked Data on the Web

LODStats – An Extensible Framework for High-Performance Dataset Analytics

Contact Info

Product

Resources

About