Inclusion of Pre-Processing and Time Series Algorithms in Map Reduce Environment using Big Data Analytics
P. Nagaraj1, P. Deepalakshmi2
1P. Nagaraj, Department of Computer Science and Engineering, Kalasalingam Academy of Research and Education, Krishnankoil (Tamil Nadu), India.
2P. Deepalakshmi, Department of Computer Science and Engineering, Kalasalingam Academy of Research and Education, Krishnankoil (Tamil Nadu), India.
Manuscript received on 07 December 2019 | Revised Manuscript received on 19 December 2019 | Manuscript Published on 30 December 2019 | PP: 798-802 | Volume-9 Issue-2S2 December 2019 | Retrieval Number: B11221292S219/2019©BEIESP | DOI: 10.35940/ijitee.B1122.1292S219
Open Access | Editorial and Publishing Policies | Cite | Mendeley | Indexing and Abstracting
© 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: Map Reduce is one of the most effective ways of handling Big Data. Many of existing Data Mining / AI algorithms was developed in Map Reduce to provide effective results. There are many more algorithms including preprocessing algorithms such as Binarization, Normalization etc., Time series algorithms such as Moving average, Sliding Window, Correlation etc., which are not yet implemented in Map Reduce. Although there are not major algorithms they play a vital role in preprocessing and processing chunk data to a meaningful data. In this paper, we proposed a model of including these algorithms in Map Reduce to improve preprocessing outcome of Big Data much faster. The processed data can then be trained by the regression algorithms using Machine learning techniques to preprocess the huge data in a long run automatically.
Keywords: Big Data, Map Reduce, Data Mining, Pre-processing, Time Series, Regression.
Scope of the Article: Big Data Analytics