MapReduce Algorithms for Processing Universal Quantifier Queries

Although quantification queries are important for querying sets and databases, nevertheless, they haven't yet been directly supported by the MapReduce paradigm. Universal quantification queries are considered a powerful and important type of queries that appear in many applications. Today with...

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Bibliographic Details
Main Authors: Habib, Wafaa M.A., Mokhtar, Hoda M.O., El Sharkawi, Mohamed E.
Format: Conference Proceeding
Language:English
Subjects:
Algebra
Data models
Educational institutions
Facebook
MapReduce
Parallel processing
Partitioning algorithms
Universal Quantification Queries
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Summary:Although quantification queries are important for querying sets and databases, nevertheless, they haven't yet been directly supported by the MapReduce paradigm. Universal quantification queries are considered a powerful and important type of queries that appear in many applications. Today with the continuous increase in the size of the data has driven the need for new processing environments to access, process, store, and maintain huge amounts of valuable data. Thus, using clusters of commodity machines turned to be an optimal solution for several big data problems. In this paper, we present a number of algorithms for processing universal quantification queries on large datasets using the popular MapReduce framework. In addition, we present experimental results that show the speed-up and scale-out properties of our proposed algorithms.
ISSN:2159-6182
DOI:10.1109/CLOUD.2014.83