Deducing Basic Graph Patterns from Logs of Linked Data Providers

Abstract : Following the principles of Linked Data, data providers published billions of facts as RDF data. Executing SPARQL queries over SPARQL endpoints or Triple Pattern Fragments (TPF) servers allow to easily consume Linked Data. However, federated SPARQL query processing and TPF query processing decompose the initialquery into subqueries. Consequently, the data providers only see subqueries and the initial query is only known by end users. Knowing executed SPARQL queries is fundamental for data providers, to ensure usage control, to optimize costs of query answering, to justify return of investment, to improve the user experience or to create business models of usage trends. In this thesis, we focus on analyzing execution logs of TPF servers and SPARQL endpoints to extract Basic Graph Patterns (BGP) of executed SPARQL queries. The main challenge to extract BGPs is the concurrent execution of SPARQL queries. We propose two algorithms: LIFT and FETA. LIFT extracts BGPs of executed queries from a single TPF server log. FETA extracts BGPs of federated queries from a log of a set of SPARQL endpoints. For experiments, we run LIFT and FETA on synthetic logs and real logs. LIFT and FETA are able to extract BGPs with good precision and recall under certain conditions.
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Contributor : Georges Nassopoulos <>
Submitted on : Tuesday, June 13, 2017 - 11:32:08 AM
Last modification on : Tuesday, March 26, 2019 - 9:25:22 AM
Document(s) archivé(s) le : Wednesday, December 13, 2017 - 11:15:30 AM


Distributed under a Creative Commons Attribution 4.0 International License


  • HAL Id : tel-01536912, version 1



Georges Nassopoulos. Deducing Basic Graph Patterns from Logs of Linked Data Providers. Computer Science [cs]. Universite de Nantes, 2017. English. 〈tel-01536912〉



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