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Query Processing for RDF Graphs using Hadoop Mapreduce Framework in the Heterogeneous Environment

E. Reena Christy, K. Prasanthi, Dr.A. Askerunisa

Abstract


Semantic web technology represents data in a standardized way such that the data can be retrieved and understood by both humans and machines.W3C developed Resource Description Framework (RDF) standard for encoding metadata and other knowledge on the Semantic Web.With the evolution of Semantic web technologies, the storage and retrieval of large RDF graphs poses significant challenges. Current frameworks do not address these challenges. In this paper we describe how Hadoop mapreduce framework is used to store and retrieve large numbers of RDF triples. We describe a scheme to store RDF/XML data in Hadoop Distributed File System as N-Triple. Hadoop’s MapReduce framework is used to answer the SPARQL Protocol and RDF Query Language (SPARQL) queries. We have compared the performance of Hadoop’s MapReduce framework with the results of Apache Jena framework and the results show that the Hadoop’s MapReduce Framework outperforms the Apache Jena framework for complex queries and fulfils the essentials of semantic web such as scalability and high speed response time.

Keywords


RDF, SPARQL, Hadoop’s MapReduce Framework

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References


Peng Wang.et.al “Transformer: A New Paradigm for Building Data-Parallel Programming Models” in IEEE transaction on Micro, Volume: 30, Issue: 4, Publication Year: 2010, Page(s): 55 - 64

Husain, M.et.al “Heuristics-Based Query Processing for Large RDF Graphs Using Cloud Computing” in IEEE Transactions on Knowledge and Data Engineering, Volume: 23, Issue: 9, Publication Year: 2011, Page(s): 1312 – 1327.

Jiang.D.et.al “MAP-JOIN-REDUCE: Toward Scalable and Efficient Data Analysis on Large Clusters” in IEEE Transactions on Knowledge and Data Engineering, Volume: 23, Issue: 9, Publication Year: 2011, Page(s): 1299 – 1311.

Li-Yung Ho.et.al “Optimal Algorithms for Cross-Rack Communication Optimization in Map Reduce Framework” in IEEE transaction on Cloud Computing (CLOUD), Volume: 16, Issue: 6, Publication Year: 2011, Page(s): 420 – 427.

Warneke, D.et.al “Exploiting Dynamic Resource Allocation for Efficient Parallel Data Processing in the Cloud” in IEEE Transactions on Parallel and Distributed Systems, Volume: 22, Issue: 6, Publication Year: 2011, Page(s): 985 - 997

Candan, K.S.et.al” RanKloud: Scalable Multimedia Data Processing in ServerClusters” in IEEE transaction on MultiMedia, Volume: 18, Issue: 1, PublicationYear: 2011, Page(s):64-77

Loughran,S.et.al ”Dynamic Cloud Deployment of a MapReduce Architecture” in IEEE transaction on Internet Computing, Volume: 16 , Issue: 6 , Publication Year: 2012 , Page(s):40-50

ZhengWe.et.al “An Optimized High-Throughput Strategy for Constructing Inverted Files” in IEEE Transactions on Parallel and Distributed Systems, Volume: 23, Issue: 11, Publication Year: 2012, Page(s): 2033 – 2044

Nand.et.al “Data Cube Materialization and Mining over MapReduce” in IEEE Transactions on Knowledge and Data Engineering, Volume: 24, Issue: 10, Publication Year: 2012, Page(s): 1747 - 1759

Bahga.et.al “Analyzing Massive Machine Maintenance Data in a Computing Cloud” in IEEE Transactions on Parallel and Distributed Systems, Volume: 23, Issue: 10, Publication Year: 2012, Page(s): 1831 – 1843.


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