Hierarchical Encoded Path Views for Path Query Processing: An Optimal Model and Its Performance Evaluation
Issue No.03 - May/June (1998 vol.10)
DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/69.687976
<p><b>Abstract</b>—Efficient path computation is essential for applications such as intelligent transportation systems (ITS) and network routing. In ITS navigation systems, many path requests can be submitted over the same, typically huge, transportation network within a small time window. While path precomputation (path view) would provide an efficient path query response, it raises three problems which must be addressed: 1) precomputed paths exceed the current computer main memory capacity for large networks; 2) disk-based solutions are too inefficient to meet the stringent requirements of these target applications; and 3) path views become too costly to update for large graphs (resulting in out-of-date query results). We propose a <it>hierarchical encoded path view</it> (<it>HEPV</it>) model that addresses all three problems. By hierarchically encoding partial paths, <it>HEPV</it> reduces the view encoding time, updating time and storage requirements beyond previously known path precomputation techniques, while significantly minimizing path retrieval time. We prove that paths retrieved over <it>HEPV</it> are optimal. We present complete solutions for all phases of the <it>HEPV</it> approach, including graph partitioning, hierarchy generation, path view encoding and updating, and path retrieval. In this paper, we also present an in-depth experimental evaluation of <it>HEPV</it> based on both synthetic and real GIS networks. Our results confirm that <it>HEPV</it> offers advantages over alternative path finding approaches in terms of performance and space efficiency.</p>
Path queries, path view materialization, hierarchical path search, GIS databases, graph partitioning.
Ning Jing, Elke A. Rundensteiner, "Hierarchical Encoded Path Views for Path Query Processing: An Optimal Model and Its Performance Evaluation", IEEE Transactions on Knowledge & Data Engineering, vol.10, no. 3, pp. 409-432, May/June 1998, doi:10.1109/69.687976