Optimizing Big Data
Anuranjan Misra1, Anshul Sharma2, Preeti Gulia3, Akanksha Bana4

1Dr. Anuranjan Misra, Professor, & Dean, Bhagwant Institute of Technology, Ghaziabad (U.P), India.
2Ms. Anshul Sharma, M.Tech Scholar, Department of Computer Science & Applications, Maharshi Dayanand University, Rohtak (Haryana), India.
3Dr. Preeti Gulia, Assistant Professor, Department of Computer Science & Applications, Maharshi Dayanand University, Rohtak (Haryana), India.
4Ms. Akanksha Bana, Assistant Professor, Department of Computer Science & Engineering, Bhagwant Institute of Technology, Ghaziabad (U.P), India.
Manuscript received on 10 July 2014 | Revised Manuscript received on 20 July 2014 | Manuscript Published on 30 July 2014 | PP: 43-46 | Volume-4 Issue-2, July 2014 | Retrieval Number: B1734074214/14©BEIESP
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: When sales representatives and customers negotiate, it must be confirmed that the final deals will render a high enough profit for the selling company. Large companies have different methods of doing this, one of which is to run sales simulations. Such simulation systems often need to perform complex calculations over large amounts of data, which in turn requires efficient models and algorithms. This paper intends to evaluate whether it is possible to optimize and extend an existing sales system called PCT, which is currently suffering from unacceptably high running times in its simulation process. This is done through analysis of the current implementation, followed by optimization of its models and development of efficient algorithms. The performance of these optimized and extended models is compared to the existing one in order to evaluate their improvement. The conclusion of this paper is that the simulation process in PCT can indeed be optimized and extended. The optimized models serve as a proof of concept, which shows that results identical to the original system’s can be calculated within < 1% of the original running time for the largest customers. Keywords: PCT, Optimized, Algorithms, Simulations.
Scope of the Article: Big Data Analytics