Abstract
The shortest path distance and related concepts lay the foundations of many real-world applications in road network analysis. The shortest path count has drawn much research attention in academia, not only as a closeness metric accompanying the shorted distance but also serving as a building block of centrality computation. This paper aims to improve the efficiency of counting the shortest paths between two query vertices on a large road network. We propose a novel index solution by organizing all vertices in a tree structure and propose several optimizations to speed up the index construction. We conduct extensive experiments on 14 realworld networks. Compared with the state-of-the-art solution, we achieve much higher efficiency on both query processing and index construction with a more compact index.
| Original language | English |
|---|---|
| Pages (from-to) | 2098-2110 |
| Number of pages | 13 |
| Journal | Proceedings of the VLDB Endowment |
| Volume | 15 |
| Issue number | 10 |
| DOIs | |
| Publication status | Published - 2022 |
| Event | 48th International Conference on Very Large Data Bases, VLDB 2022 - Sydney, Australia Duration: 5 Sept 2022 → 9 Sept 2022 |
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