An Efficient Pre and Post Processing Skyline Computational Framework Using Mapreduce
P. Venkateswara Rao1, Mohammed Ali Hussain2 

1P. Venkateswara Rao, Asst. Professor and Research Scholar, Department of Computer Science Engineering, Koneru Lakshmaiah Education Foundation (Deemed to be University), Vaddeswaram, Guntur, India.
2Dr. Mohammed Ali Hussain, Professor, ECSE, Koneru Lakshmaiah Education Foundation (Deemed to be University), Vaddeswaram, Guntur, India.
Manuscript received on 09 March 2019 | Revised Manuscript received on 18 March 2019 | Manuscript published on 30 July 2019 | PP: 5954-5959 | Volume-8 Issue-2, July 2019 | Retrieval Number: B3587078219/19©BEIESP | DOI: 10.35940/ijrte.B3587.078219
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Abstract: Parallel processing will change the world of computation. Now a day’s parallel processing will take leading role in all processing that are aided with computer system. Skyline query processing is not exceptional. Skyline processing with MapReduce will take lead role in skyline computation. Still Skyline processing with MapReduce do not take full advantage of parallelism due to processing of including unnecessary data. This paper contains our proposed Map Reduce – Pre and Post Filter based Skyline Computation (MR-PPFS). The aim of this model is to reduce number of candidates before sending dataset to MapReduced skyline computation. This idea reduces MapReduced skyline computation time compare to other algorithms.
Keywords: Angle Based Partitioning, Grid Partitioning, Skyline Query and Parallel Processing, and Threshold Filter.

Scope of the Article: Computational Economics, Digital Photogrammetric