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...

Celý popis

Uloženo v:
Podrobná bibliografie
Vydáno v:IEEE ... International Conference on Cloud Computing s. 578 - 585
Hlavní autoři: Habib, Wafaa M.A., Mokhtar, Hoda M.O., El Sharkawi, Mohamed E.
Médium: Konferenční příspěvek
Jazyk:angličtina
Vydáno: IEEE 01.06.2014
Témata:
Algebra
Data models
Educational institutions
Facebook
MapReduce
Parallel processing
Partitioning algorithms
Universal Quantification Queries
ISSN:2159-6182
On-line přístup:Získat plný text
Tagy: Přidat tag
Žádné tagy, Buďte první, kdo vytvoří štítek k tomuto záznamu!
Popis
Shrnutí: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