Adaptive Semantic Data Management Techniques for Linked Data-Graduate Course Description (2011)
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BibTeX
@MISC{Vidal11adaptivesemantic,
author = {María-esther Vidal},
title = {Adaptive Semantic Data Management Techniques for Linked Data-Graduate Course Description},
year = {2011}
}
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Abstract
In the context of the Cloud of Linked Data, a large number of huge RDF linked datasets have become available, and this number keeps growing. Simultaneously, scalable and efficient RDF engines that follow the traditional optimize-then-execute paradigm have been developed to locally access RDF data, and SPARQL endpoints have been implemented for remote query processing. However, given the size of existing datasets, lack of statistics to describe available sources, and unpredictable conditions of remote queries, existing solutions are still insufficient. First, the most efficient RDF engines rely their query processing algorithms on physical access and storage structures that are locally stored; however, because of the size of existing linked datasets, loading the data and their links is not always feasible. Second, remote linked data query processing can be extremely costly because of the lack of query planning; also, current techniques are not adaptable to unpredictable data transfers or data availability, thus, executions can be unsuccessful. To overcome these limitations, query physical operators and execution engines need to be able to access remote data and adapt query execution schedulers to data availability. In this graduate course we present the basis of adaptive query processing frameworks defined in the database area, and their applicability in the Linked data context. This course targets graduate students who want to know limitations of existing RDF engines, adaptive query processing techniques and how traditional RDF data management approaches can be extended to remotely access linked data and be well-suitable to runtime conditions.
Keyphrases
adaptive semantic data management technique data-graduate course description data availability efficient rdf engine physical access linked data database area storage structure linked data context physical operator execution engine query planning adaptive query processing technique unpredictable condition access rdf data remote query processing graduate course adapt query execution scheduler traditional rdf data management approach adaptive query processing framework huge rdf current technique remote query query processing algorithm graduate student traditional optimize-then-execute paradigm available source sparql endpoint large number rdf engine unpredictable data transfer
