An efficient approach for searching optimal multi-keywords coverage routes

Abstract

Keyword-aware optimal route queries are a combinatorial optimization problem with three factors, namely keyword coverage, route budget constraint and route popularity, which is NP-hard. Previous work takes an adjacent edge expansion approach to get an approximate result and the computational complexity is proportional to the number of vertices and edges of the road network, which is not scalable for large road networks or road networks with sparse points of interest. Motivated by this, we propose an algorithm called Keyword-aware Skyline Route Generating (KSRG*). KSRG* consists of a preprocessing stage, in which KSRG* computes all the skyline routes for each pair of vertices, and an expansion stage, in which KSRG* expands feasible routes from the source vertex to target vertex via vertices containing keywords. Based on KSRG*, another two approximation algorithms with performance guarantees, namely KSRG$^{+}$ and KSRG$^{++}$ are proposed. KSRG$^{+}$ applies three acceleration strategies to KSRG* to greatly reduce the routes to be expanded. KSRG$^{++}$ combines KSRG$^{+}$ with a clustering strategy to solve the inefficient performance when the number of the vertices containing the same query keyword is large. Comprehensive experiments on the datasets of four real road networks show that the execution time and accuracy of results of KSRG$^+$ and KSRG$^{++}$ outperform the state of the art methods.