Shortest path problem has been a classic issue. Even more so difficulties remain involving large data environment. Current research on shortest path problem mainly focuses on seeking the shortest path from a starting point to the destination, with both vertices already given; but the researches of shortest path on a limited time and limited nodes passing through are few, yet such problem could not be more common in real life. In this paper we propose several time-dependent optimization algorithms for this problem. In regard to traditional backtracking and different node compression methods, we first propose an improved backtracking algorithm for one condition in big data environment and three types of optimization algorithms based on node compression involving large data, in order to realize the path selection from the starting point through a given set of nodes to reach the end within a limited time. Consequently, problems involving different data volume and complexity of network structure can be solved with the appropriate algorithm adopted.
BELIEF: A distance-based redundancy-proof feature selection method for Big Data
