Overview

Data is important in every part of life. The raw data obtained is to be stored and mined to gain information to use in future. This storing of data is there from long ago where people use to arrange it manually which use to consume more time and not a secured one. With the introduction of computer technology, the data storage is maintained digitally. With the appearance of internet and low-cost hardware storage devices the growth of data gathering and storing has increased to peaks. The data was arranged in structured format with many rows and columns. The different resources of data collection have led organization of data under one site to facilitate data management. Data warehouses are the repositories of data storage which needs to be mined for extracting the patterns and information from the given data. The sensor technology used has generated lots of data beyond the storage in single format and created the urgent need to address the problems of analysis of the collected data for the benefit of future generations [1]. This led to the development of big data and its analytics. Big data in general stores enormous amount of intermixed data which cannot be handled by the traditional methods. Big data which is more in volume, velocity and value is basically needed for the prediction of accurate results [1]. It is more in dimensions by gathering the available information from different sectors. Dimensionality reduction of big data is the needed requirement which needs to be reduced for analyzing the accurate results by dealing with the multiple features of the data objects. Now a day with the rise of the machine learning algorithms [2] high velocity data can also be utilized in an efficient way as machine learning algorithms automatically predict the results without human intervention and they can learn and interpret the results in an effective way apart from any others.

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