Toward FPGA-Based Semantic Caching for Accelerating Data Analysis with Spark and HDFS

With the increase of data, traditional methods of data processing have become time and power inefficient. As enhancement, we propose a new accelerated architecture for querying big Databases. This architecture combines the advantages of the HDFS for the management of huge amount of data and the fast...

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Bibliographic Details
Published inInformation Search, Integration, and Personalization Vol. 1040; pp. 104 - 115
Main Authors Maghzaoui, Marouan, d’Orazio, Laurent, Lallet, Julien
Format Book Chapter
LanguageEnglish
Published Switzerland Springer International Publishing AG 2019
Springer International Publishing
SeriesCommunications in Computer and Information Science
Subjects
FPGA
HDFS
Semantic caching
Spark
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Summary:With the increase of data, traditional methods of data processing have become time and power inefficient. As enhancement, we propose a new accelerated architecture for querying big Databases. This architecture combines the advantages of the HDFS for the management of huge amount of data and the fast processing of queries of Spark SQL. It also benefits of the processing efficiency of the hardware acceleration of FPGAs and of the semantic caching architecture to process recently used data stored in the cache.
ISBN:9783030302832
3030302830
ISSN:1865-0929
1865-0937
DOI:10.1007/978-3-030-30284-9_7