Big data algorithm optimization

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 project 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 are compared to the existing one in order to evaluate their improvement. The conclusion of this project 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.