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Using Precomputed Bloom Filters to Speed Up SPARQL Processing in the Cloud
BibTeX
@MISC{A_usingprecomputed,
author = {Thomas Kiencke A and Stefan Werner A and Dennis Heinrich A and Marc Stelzner A and Le Gruenwald B},
title = {Using Precomputed Bloom Filters to Speed Up SPARQL Processing in the Cloud},
year = {}
}
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Abstract
Increasingly data on the Web is stored in the form of Semantic Web data. Because of today’s information overload, it becomes very important to store and query these big datasets in a scalable way and hence in a distributed fash-ion. Cloud Computing offers such a distributed environment with dynamic reallocation of computing and storing resources based on needs. In this work we introduce a scalable distributed Semantic Web database in the Cloud. In order to reduce the number of (unnecessary) intermediate results early, we apply bloom filters. Instead of computing bloom filters, a time-consuming task during query processing as it has been done traditionally, we precompute the bloom filters as much as possible and store them in the indices besides the data. The experimental results with data sets up to 1 billion triples show that our approach speeds up query processing significantly and sometimes even reduces the processing time to less than half. TYPE OF PAPER AND KEYWORDS
Keyphrases
bloom filter precomputed bloom filter sparql processing semantic web database big datasets distributed fash-ion today information overload paper keywords intermediate result semantic web data processing time data set time-consuming task query processing scalable way experimental result dynamic reallocation distributed environment
