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RSSMSO Rapid Similarity Search on Metric Space Object Stored in Cloud Environment

RSSMSO Rapid Similarity Search on Metric Space Object Stored in Cloud Environment

Raghavendra S., Nithyashree K., Geeta C.M., Rajkumar Buyya, Venugopal K. R., S. S. Iyengar, L. M. Patnaik
Copyright: © 2016 |Volume: 6 |Issue: 3 |Pages: 17
ISSN: 1947-9344|EISSN: 1947-9352|EISBN13: 9781466691261|DOI: 10.4018/IJOCI.2016070103
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MLA

Raghavendra S., et al. "RSSMSO Rapid Similarity Search on Metric Space Object Stored in Cloud Environment." IJOCI vol.6, no.3 2016: pp.33-49. http://doi.org/10.4018/IJOCI.2016070103

APA

Raghavendra S., Nithyashree K., Geeta C.M., Buyya, R., Venugopal K. R., Iyengar, S. S., & Patnaik, L. M. (2016). RSSMSO Rapid Similarity Search on Metric Space Object Stored in Cloud Environment. International Journal of Organizational and Collective Intelligence (IJOCI), 6(3), 33-49. http://doi.org/10.4018/IJOCI.2016070103

Chicago

Raghavendra S., et al. "RSSMSO Rapid Similarity Search on Metric Space Object Stored in Cloud Environment," International Journal of Organizational and Collective Intelligence (IJOCI) 6, no.3: 33-49. http://doi.org/10.4018/IJOCI.2016070103

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Abstract

This paper involves a cloud computing environment in which the dataowner outsource the similarity search service to a third party service provider. Privacy of the outsourced data is important because they may be confidential data. The data should be made available to the authorized client groups, but not to be revealed to the service provider in which the data is stored. Given this scenario, the paper presents a technique called RSSMSO which has build phase, query phase, data transformation and search phase. The build phase and the query phase are about uploading the data and querying the data respectively; the data transformation phase transforms the data before submitting it to the service provider for similarity queries on the transformed data; search phase involves searching similar object with respect to query object. The RSSMSO technique provides enhanced query accuracy with low communication cost. Experiments have been carried out on real data sets which exhibits that the proposed work is capable of providing privacy and achieving accuracy at a low cost in comparison with FDH

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