ABSTRACT
In this paper we present an algorithm for processing aggregate nearest neighbor queries in time-dependent road networks, i.e., given a road network where the travel time over an edge is time-dependent, a set of query points Q, a set of points of interest (POIs) P and an aggregate function (e.g., sum), we find the k POIs that minimize the aggregated travel time from the query points. For instance, considering a city's road network at a given departure time and a group of friends at different locations wishing to meet at a restaurant, the time-dependent aggregate nearest neighbor query, considering the sum function, would return the restaurant that minimizes the sum of all travel times to it. The main contribution of our work is the consideration of the time-dependency of the network, a realistic characteristic of urban road networks, which has not been considered previously when addressing aggregate nearest neighbor queries. Our approach is based on the ANNQPLB algorithm proposed by Htoo et al. and uses Hub Labels, proposed by Abraham et al., to compute optimistic travel times efficiently. In order to compare our proposal we extended the previously proposed ANNQPLB algorithm aimed at non-time dependent aggregate nearest neighbor queries, enabling it to deal with the time-dependency. Our experiments using a real road network have shown our proposed solution to be up to 94% faster than the temporally extended previous solution.
- I. Abraham, D. Delling, A. V. Goldberg, and R. F. Werneck. A hub-based labeling algorithm for shortest paths in road networks. In Proc. of the 10th SEA, pages 230--241, 2011. Google ScholarDigital Library
- L. A. Cruz, M. A. Nascimento, and J. A. F. de Macêdo. K-nearest neighbors queries in time-dependent road networks. JIDM, pages 211--226, 2012.Google Scholar
- D. Delling, A. Goldberg, T. Pajor, and R. Werneck. Robust distance queries on massive networks. In Proc. of the 22nd ESA, pages 321--333, 2014.Google ScholarCross Ref
- U. Demiryurek, F. Banaei-Kashani, and C. Shahabi. Efficient k-nearest neighbor search in time-dependent spatial networks. In DEXA, pages 432--449. 2010. Google ScholarDigital Library
- U. Demiryurek, F. Banaei-Kashani, and C. Shahabi. Towards k-nearest neighbor search in time-dependent spatial network databases. In Proc. of the 6th DNIS, pages 296--310, 2010. Google ScholarDigital Library
- M. Erwig and F. Hagen. The graph voronoi diagram with applications. Networks, pages 156--163, 2000.Google Scholar
- C. Gavoille, D. Peleg, S. Pérennes, and R. Raz. Distance labeling in graphs. In Proc. of the 12th ACM-SIAM SODA, pages 210--219, 2001. Google ScholarDigital Library
- H. Htoo, Y. Ohsawa, and N. Sonehara. Single-source multi-target a* algorithm for poi queries on road network. In WAIM, pages 51--62. 2012. Google ScholarDigital Library
- H. Htoo, Y. Ohsawa, N. Sonehara, and M. Sakauchi. Aggregate nearest neighbor search methods using ssmta* algorithm on road-network. In ADBIS, pages 181--194, 2012. Google ScholarDigital Library
- E. Ioup, K. Shaw, J. Sample, and M. Abdelguerfi. Efficient aknn spatial network queries using the m-tree. In Proc. of the 15th ACM GIS, pages 46:1--46:4, 2007. Google ScholarDigital Library
- Y. Komai, D. H. Nguyen, T. Hara, and S. Nishio. knn search utilizing index of the minimum road travel time in time-dependent road networks. In Proc. of the 33rd IEEE SRDSW, pages 131--137, 2014. Google ScholarDigital Library
- S. Namnandorj, H. Chen, K. Furuse, and N. Ohbo. Efficient bounds in finding aggregate nearest neighbors. In DEXA, pages 693--700, 2008. Google ScholarDigital Library
- A. Orda and R. Rom. Shortest-path and minimum-delay algorithms in networks with time-dependent edge-length. JACM, pages 607--625, 1990. Google ScholarDigital Library
- D. Papadias, Q. Shen, Y. Tao, and K. Mouratidis. Group nearest neighbor queries. In Proc. of the 20th ICDE, pages 301--312, 2004. Google ScholarDigital Library
- D. Papadias, Y. Tao, K. Mouratidis, and C. K. Hui. Aggregate nearest neighbor queries in spatial databases. ACM TODS, pages 529--576, 2005. Google ScholarDigital Library
- D. Papadias, J. Zhang, N. Mamoulis, and Y. Tao. Query processing in spatial network databases. In Proc. of the 29th VLDB, pages 802--813, 2003. Google ScholarDigital Library
- M. Patella, P. Ciaccia, and P. Zezula. M-tree: An efficient access method for similarity search in metric spaces. In Proc. of the 23rd VLDB, pages 426--435, 1997. Google ScholarDigital Library
- M. Safar. Group k-nearest neighbors queries in spatial network databases. JGIS, pages 407--416, 2008.Google ScholarCross Ref
- M. L. Yiu, N. Mamoulis, and D. Papadias. Aggregate nearest neighbor queries in road networks. IEEE TKDE, pages 820--833, 2005. Google ScholarDigital Library
- L. Zhu, Y. Jing, W. Sun, D. Mao, and P. Liu. Voronoi-based aggregate nearest neighbor query processing in road networks. In Proc. of the 18th ACM SIGSPATIAL, pages 518--521, 2010. Google ScholarDigital Library
Index Terms
- Aggregate k-nearest neighbors queries in time-dependent road networks
Recommendations
An efficient and scalable method for aggregate nearest neighbor queries on time-dependent road networks
AbstractWe study the k aggregate nearest neighbor queries on time-dependent road networks (TDRNs), assuming that the travel time to traverse an edge depends on the time it is initiated. Given a set of query points, a set of points of interest (...
Highlights- Traditional ANN query on time-dependent road network is strict in application.
- ...
Continuous aggregate nearest neighbor queries
This paper addresses the problem of continuous aggregate nearest-neighbor (CANN) queries for moving objects in spatio-temporal data stream management systems. A CANN query specifies a set of landmarks, an integer k, and an aggregate distance function f (...
Fast optimal aggregate point search for a merged set on road networks
Aggregate nearest neighbor query, which returns an optimal target point that minimizes the aggregate distance for a given query point set, is one of the most important operations in spatial databases and their application domains. This paper addresses ...
Comments