High Accuracy and Efficency Prediction of Herms Using Mapreduce Technique
Devaraj K S1, Janaki K2, Harshitha T N3, Gowtham Das V4, Jinka Kavya5
1Devaraj K S, Department of CSE, Rajarajeswari College of Engineering, Bangalore (Karnataka), India.
2Janaki K, Department of CSE, RRCE, Bangalore (Karnataka), India.
3Harshitha T N, Department of CSE, RRCE, Bangalore (Karnataka), India.
4Gowtham Das V, Department of CSE, RRCE, Bangalore (Karnataka), India.
5Jinka Kavya, Department of CSE, RRCE, Bangalore (Karnataka), India.
Manuscript received on 04 May 2019 | Revised Manuscript received on 16 May 2019 | Manuscript Published on 23 May 2019 | PP: 416-420 | Volume-7 Issue-6S5 April 2019 | Retrieval Number: F10700476S519/2019©BEIESP
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: In this modern era of big data, a conventional scanning seek sample is progressively unable to satisfying user needs due to its lengthy computing manner. here we use a framework called sampling-based approximate search framework which is referred to as Hadoop framework( map lessen), inorder to satisfy consumer’s query demand for each correct and green consequences .In novel body, the work is provided to measure accuracy and efficiency uniformly for a large facts search carrier, which permits to training session a feasible searching process. based on this, we appoint the bootstrapping approach in addition to accelerate the hunt procedure. moreover, an incremental sampling strategy is investigated to method homogeneous queries; similarly, the reuse principle of ancient outcomes is also studied for the scenario of appending records. Experiments and Theoretical analyses on a real-international dataset exhibit that map reduce algorithm is able to generating approximate results meeting the preset question requirements with each excessive accuracy and performance.
Keywords: Big Data, Data Search Pattern, Hadoop Framework, Mapreduce.
Scope of the Article: Regression and Prediction