This paper is an effort to present the basic understanding of BIG DATA is and it's usefulness to an organization from the performance perspective. Along-with the introduction of BIG DATA, the important parameters and attributes that make this emerging concept attractive to organizations has been highlighted. The paper also evaluates the difference in the challenges faced by a small organization as compared to a medium or large scale operation and therefore the differences in their approach and treatment of BIG DATA. A number of application examples of implementation of BIG DATA across industries varying in strategy, product and processes have been presented. The second part of the paper deals with the technology aspects of BIG DATA for it's implementation in organizations. Since HADOOP has emerged as a popular tool for BIG DATA implementation, the paper deals with the overall architecture of HADOOP alongwith the details of it's various components. Further each of the components of the architecture has been taken up and described in detail.
The prediction of the stock market was one of the greatest obstacles. Since there are many reserves whose values are extremely unpredictable, good returns from investment must be calculated and determined. We tried our best to address these challenges by picking an index (a collection of stocks in the same sector) and developed an arbitration style trading mechanism in which the leading stock of that index could be chosen by correlation analyzes and technical analyses. When people hear the trade in similarities, they will wonder of arbitration. And that’s why we’re dreaming about investing in arbitrage. Although this is not true: arbitrariness is when two entities that are similar or rather connected are out of whack. So, we will look at a stock and its index and they are not identical, so they’re never out of whack. There are rules to define the entries and exits in a chosen stock in the trading system. The additional advantage of this method is to forecast the movement of one stock using its relationship with another stock of the same index. Established method is split into two phases: stock correlation and technological analysis. Experiments between July 2017 and June 2020 indicate that the know-how could include an index and its entries and exits.
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