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Adaptive mechanism for distributed query processing and data loading using the RDF data in the cloud
Author(s) -
Ranichandra Dharmaraj Chandrasekaran,
Tripathy BalaKrushna
Publication year - 2018
Publication title -
international journal of communication systems
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.344
H-Index - 49
eISSN - 1099-1131
pISSN - 1074-5351
DOI - 10.1002/dac.3784
Subject(s) - computer science , rdf , rdf query language , search engine indexing , sparql , overhead (engineering) , query optimization , database , rdf schema , cloud computing , information retrieval , response time , data mining , semantic web , web search query , distributed computing , web query classification , search engine , operating system
Summary With the development of semantic web, the Resource Description Framework (RDF) data management has gained more importance in the recent years. The RDF data management involves processing the large datasets. The existing mechanism had performance issues with respect to data loading and query response time. In this paper, we developed an efficient mechanism for handling the RDF data using the data partitioning approach. The proposed approach follows the low‐level indexing and runtime indexing for the statements in the query to reduce the data loading time as well as to reduce the communication overhead over the nodes. The performance of the proposed method is evaluated using the Amazon web services. The proposed achieved faster loading time and better query response time compared to the RDF‐3X and SHARD models.