A modified parallel optimization system for updating large-size time-evolving flow matrix
Flow matrices are widely used in many disciplines, but few methods can estimate them. This paper presents a knowledge-based system as capable of estimating and updating large-size time-evolving flow matrix. The system in this paper consists of two major components with the purposes of matrix estimation and parallel optimization. The matrix estimation algorithm interprets and follows users query scripts, retrieves data from various sources and integrates them for the matrix estimation. The parallel optimization component is built upon a supercomputing facility to utilize its computational power to efficiently process a large amount of data and estimate a large-size complex matrix. The experimental results demonstrate its outstanding performance and the acceptable accuracy by directly and indirectly comparing the estimation matrix with the actual matrix constructed by surveys.
parallel optimization time-evolving flow matrix data fusion