Fast Matrix Multiplication with Big Sparse Data

Somasekhar, G.; Karthikeyan, K.

doi:10.1515/cait-2017-0002

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2018

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“…A big data solution for a redundancy problem in data matching was given by G. Somasekhar and K. Karthikeyan [28]. A fast multiplication approach for large sparse matrices was proposed by G. Somasekhar and K. Karthikeyan [29]. Mapreduce was applied on neighbourhood blocking by L. Kolb et al [30].…”

Section: Introductionmentioning

confidence: 99%

A Big Data Solution to Detect Conditional Functional Dependency Violations

Somasekhar¹,

Karthikeyan²

2018

IJET

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The violation detection of conditional functional dependencies in distributed environment has been a research problem giving inspiration to many researchers recently. A very few solutions were given in the recent past to handle conditional functional dependencies. Unfortunately, these are inappropriate in real time big data applications. This article mainly focuses on the big data solution to such type of problems. The proposed IMRCFDHBD algorithm reduces elapsed time and provides scalability with minimum data shipment. The result proves that the algorithm outperforms the state-of-the-art techniques in the big data scenarios.

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Section: Introductionmentioning

confidence: 99%