Abstract
The k nearest neighbor (kNN) query on road networks finds the k closest points of interest (POIs) by network distance from a query point. A past study showed that a kNN technique using a simple Euclidean distance heuristic to generate candidate POIs significantly outperforms more complex techniques. While Euclidean distance is an effective lower bound when network distances represent physical distance, its accuracy degrades greatly for metrics such as travel time. Landmarks have been used to compute tighter lower bounds in such cases, however past attempts to use them in kNN querying failed to retrieve candidates efficiently. We present two techniques to address this problem, one using ordered Object Lists for each landmark and another using a combination of landmarks and Network Voronoi Diagrams (NVDs) to only compute lower bounds to a small subset of objects that may be kNNs. Our extensive experimental study shows these techniques (particularly NVDs) significantly improve on the previous best techniques in terms of both heuristic and query time performance.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Notes
References
Abeywickrama, T., Cheema, M.A., Taniar, D.: K-nearest neighbors on road networks: a journey in experimentation and in-memory implementation. PVLDB 9(6), 492–503 (2016)
Akiba, T., Iwata, Y.: Kawarabayashi, K.I., Kawata, Y.: Fast shortest-path distance queries on road networks by pruned highway labeling. In: ALENEX, pp. 147–154 (2014)
Erwig, M., Hagen, F.: The graph voronoi diagram with applications. Networks 36, 156–163 (2000)
Goldberg, A.V., Harrelson, C.: Computing the shortest path: a search meets graph theory. In: SODA, pp. 156–165 (2005)
Goldberg, A.V., Werneck, R.F.F.: Computing point-to-point shortest paths from external memory. In: ALENEX, pp. 26–40 (2005)
Kolahdouzan, M., Shahabi, C.: Voronoi-based k nearest neighbor search for spatial network databases. In: VLDB, pp. 840–851 (2004)
Kriegel, H.-P., Kröger, P., Kunath, P., Renz, M.: Generalizing the optimality of multi-step k-nearest neighbor query processing. In: Papadias, D., Zhang, D., Kollios, G. (eds.) SSTD 2007. LNCS, vol. 4605, pp. 75–92. Springer, Heidelberg (2007). doi:10.1007/978-3-540-73540-3_5
Kriegel, H.-P., Kröger, P., Renz, M., Schmidt, T.: Hierarchical graph embedding for efficient query processing in very large traffic networks. In: Ludäscher, B., Mamoulis, N. (eds.) SSDBM 2008. LNCS, vol. 5069, pp. 150–167. Springer, Heidelberg (2008). doi:10.1007/978-3-540-69497-7_12
Okabe, A., Boots, B., Sugihara, K.: Spatial Tessellations: Concepts and Applications of Voronoi Diagrams, 2nd edn. Wiley, Hoboken (2000)
Papadias, D., Zhang, J., Mamoulis, N., Tao, Y.: Query processing in spatial network databases. In: VLDB, pp. 802–813 (2003)
Qiao, M., Qin, L., Cheng, H., Yu, J.X., Tian, W.: Top-k nearest keyword search on large graphs. PVLDB 6(10), 901–912 (2013)
Samet, H., Sankaranarayanan, J., Alborzi, H.: Scalable network distance browsing in spatial databases. In: SIGMOD, pp. 43–54 (2008)
Seidl, T., Kriegel, H.P.: Optimal multi-step k-nearest neighbor search. In: SIGMOD, pp. 154–165 (1998)
Shahabi, C., Kolahdouzan, M., Sharifzadeh, M.: A road network embedding technique for k-nearest neighbor search in moving object databases. GeoInformatica 7(3), 255–273 (2003)
Zheng, B., Zheng, K., Xiao, X., Su, H., Yin, H., Zhou, X., Li, G.: Keyword-aware continuous kNN query on road networks. In: ICDE, pp. 871–882 (2016)
Zhong, R., Li, G., Tan, K., Zhou, L., Gong, Z.: G-tree: an efficient and scalable index for spatial search on road networks. TKDE 27(8), 2175–2189 (2015)
Acknowledgements
We sincerely thank the anonymous reviewers for their feedback which helped improve our work. The research of Muhammad Aamir Cheema is supported by ARC DE130101002 and DP130103405. Tenindra Abeywickrama is supported by an Australian Government RTP Scholarship.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2017 Springer International Publishing AG
About this paper
Cite this paper
Abeywickrama, T., Cheema, M.A. (2017). Efficient Landmark-Based Candidate Generation for kNN Queries on Road Networks. In: Candan, S., Chen, L., Pedersen, T., Chang, L., Hua, W. (eds) Database Systems for Advanced Applications. DASFAA 2017. Lecture Notes in Computer Science(), vol 10178. Springer, Cham. https://doi.org/10.1007/978-3-319-55699-4_26
Download citation
DOI: https://doi.org/10.1007/978-3-319-55699-4_26
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-55698-7
Online ISBN: 978-3-319-55699-4
eBook Packages: Computer ScienceComputer Science (R0)