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- W12544054 abstract "AbstractNon expert users need support to access linked data available on the Web. To this aim, keyword-based search is considered an essential feature of database systems. The distributed nature of the Semantic Web demands query processing techniques to evolve towards a scenario where data is scattered on distributed data stores. Existing approaches to keyword search cannot guarantee scalability in a distributed environment, because, at runtime, they are unaware of the location of the relevant data to the query and thus, they cannot optimize join tasks. In this paper, we illustrate a novel distributed approach to keyword search over RDF data that exploits the MapReduce paradigm by switching the problem from graph-parallel to data-parallel processing. Moreover, our framework is able to consider ranking during the building phase to return directly the best (top-k) answers in the first (k) generated results, reducing greatly the overall computational load and complexity. Finally, a comprehensive evaluation demonstrates that our approach exhibits very good efficiency guaranteeing high level of accuracy, especially with respect to state-of-the-art competitors.Keywords#eswc2014Virgilio" @default.
- W12544054 created "2016-06-24" @default.
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- W12544054 date "2014-01-01" @default.
- W12544054 modified "2023-09-24" @default.
- W12544054 title "Distributed Keyword Search over RDF via MapReduce" @default.
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- W12544054 doi "https://doi.org/10.1007/978-3-319-07443-6_15" @default.
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