Séminaire Donnees et APprentissage Artificiel
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Heuristic based Query Optimisation for SPARQL
Intervenant(s) : Irini FUNDULAKI - ICS FORTH, CrèteDuring the last decade we have witnessed an increase in the amount of semantic data. The so called Web of Data extents the current Web to a global data space connecting data from diverse domains. A central issue in such setting is the efficient support for storing, querying, and manipulating semantic RDF data. In this work we focus on the problem of scalable processing and optimisation of semantic queries expressed in SPARQL using modern relational engines. Existing solutions heavily rely on statistics for the stored RDF graphs, and on cost-based planning algorithms. Extensive data statistics are quite expensive to compute and maintain for large scale and always evolving semantic data. The main challenge is to devise heuristic-based query optimisation techniques generating near to optimal execution plans without any knowledge of the underlying datasets.
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