The Community for Technology Leaders
RSS Icon
Subscribe
Issue No.03 - March (2011 vol.23)
pp: 433-446
Man Lung Yiu , Hong Kong Politechnic University, Hong Kong
Nikos Mamoulis , University of Hong Kong, Hong Kong
Michail Vaitis , University of the Aegean, Mytilene
ABSTRACT
A spatial preference query ranks objects based on the qualities of features in their spatial neighborhood. For example, using a real estate agency database of flats for lease, a customer may want to rank the flats with respect to the appropriateness of their location, defined after aggregating the qualities of other features (e.g., restaurants, cafes, hospital, market, etc.) within their spatial neighborhood. Such a neighborhood concept can be specified by the user via different functions. It can be an explicit circular region within a given distance from the flat. Another intuitive definition is to assign higher weights to the features based on their proximity to the flat. In this paper, we formally define spatial preference queries and propose appropriate indexing techniques and search algorithms for them. Extensive evaluation of our methods on both real and synthetic data reveals that an optimized branch-and-bound solution is efficient and robust with respect to different parameters.
INDEX TERMS
Query processing, spatial databases.
CITATION
Man Lung Yiu, Nikos Mamoulis, Michail Vaitis, "Ranking Spatial Data by Quality Preferences", IEEE Transactions on Knowledge & Data Engineering, vol.23, no. 3, pp. 433-446, March 2011, doi:10.1109/TKDE.2010.119
REFERENCES
[1] M.L. Yiu, X. Dai, N. Mamoulis, and M. Vaitis, "Top-k Spatial Preference Queries," Proc. IEEE Int'l Conf. Data Eng. (ICDE), 2007.
[2] N. Bruno, L. Gravano, and A. Marian, "Evaluating Top-k Queries over Web-Accessible Databases," Proc. IEEE Int'l Conf. Data Eng. (ICDE), 2002.
[3] A. Guttman, "R-Trees: A Dynamic Index Structure for Spatial Searching," Proc. ACM SIGMOD, 1984.
[4] G.R. Hjaltason and H. Samet, "Distance Browsing in Spatial Databases," ACM Trans. Database Systems, vol. 24, no. 2, pp. 265-318, 1999.
[5] R. Weber, H.-J. Schek, and S. Blott, "A Quantitative Analysis and Performance Study for Similarity-Search Methods in High-Dimensional Spaces," Proc. Int'l Conf. Very Large Data Bases (VLDB), 1998.
[6] K.S. Beyer, J. Goldstein, R. Ramakrishnan, and U. Shaft, "When is 'Nearest Neighbor' Meaningful?" Proc. Seventh Int'l Conf. Database Theory (ICDT), 1999.
[7] R. Fagin, A. Lotem, and M. Naor, "Optimal Aggregation Algorithms for Middleware," Proc. Int'l Symp. Principles of Database Systems (PODS), 2001.
[8] I.F. Ilyas, W.G. Aref, and A. Elmagarmid, "Supporting Top-k Join Queries in Relational Databases," Proc. 29th Int'l Conf. Very Large Data Bases (VLDB), 2003.
[9] N. Mamoulis, M.L. Yiu, K.H. Cheng, and D.W. Cheung, "Efficient Top-k Aggregation of Ranked Inputs," ACM Trans. Database Systems, vol. 32, no. 3, p. 19, 2007.
[10] D. Papadias, P. Kalnis, J. Zhang, and Y. Tao, "Efficient OLAP Operations in Spatial Data Warehouses," Proc. Int'l Symp. Spatial and Temporal Databases (SSTD), 2001.
[11] S. Hong, B. Moon, and S. Lee, "Efficient Execution of Range Top-k Queries in Aggregate R-Trees," IEICE Trans. Information and Systems, vol. 88-D, no. 11, pp. 2544-2554, 2005.
[12] T. Xia, D. Zhang, E. Kanoulas, and Y. Du, "On Computing Top-t Most Influential Spatial Sites," Proc. 31st Int'l Conf. Very Large Data Bases (VLDB), 2005.
[13] Y. Du, D. Zhang, and T. Xia, "The Optimal-Location Query," Proc. Int'l Symp. Spatial and Temporal Databases (SSTD), 2005.
[14] D. Zhang, Y. Du, T. Xia, and Y. Tao, "Progessive Computation of The Min-Dist Optimal-Location Query," Proc. 32nd Int'l Conf. Very Large Data Bases (VLDB), 2006.
[15] Y. Chen and J.M. Patel, "Efficient Evaluation of All-Nearest-Neighbor Queries," Proc. IEEE Int'l Conf. Data Eng. (ICDE), 2007.
[16] P.G.Y. Kumar and R. Janardan, "Efficient Algorithms for Reverse Proximity Query Problems," Proc. 16th ACM Int'l Conf. Advances in Geographic Information Systems (GIS), 2008.
[17] M.L. Yiu, P. Karras, and N. Mamoulis, "Ring-Constrained Join: Deriving Fair Middleman Locations from Pointsets via a Geometric Constraint," Proc. 11th Int'l Conf. Extending Database Technology (EDBT), 2008.
[18] M.L. Yiu, N. Mamoulis, and P. Karras, "Common Influence Join: A Natural Join Operation for Spatial Pointsets," Proc. IEEE Int'l Conf. Data Eng. (ICDE), 2008.
[19] Y.-Y. Chen, T. Suel, and A. Markowetz, "Efficient Query Processing in Geographic Web Search Engines," Proc. ACM SIGMOD, 2006.
[20] V.S. Sengar, T. Joshi, J. Joy, S. Prakash, and K. Toyama, "Robust Location Search from Text Queries," Proc. 15th Ann. ACM Int'l Symp. Advances in Geographic Information Systems (GIS), 2007.
[21] S. Berchtold, C. Boehm, D. Keim, and H. Kriegel, "A Cost Model for Nearest Neighbor Search in High-Dimensional Data Space," Proc. ACM Symp. Principles of Database Systems (PODS), 1997.
[22] E. Dellis, B. Seeger, and A. Vlachou, "Nearest Neighbor Search on Vertically Partitioned High-Dimensional Data," Proc. Seventh Int'l Conf. Data Warehousing and Knowledge Discovery (DaWaK), pp. 243-253, 2005.
[23] N. Mamoulis and D. Papadias, "Multiway Spatial Joins," ACM Trans. Database Systems, vol. 26, no. 4, pp. 424-475, 2001.
[24] A. Hinneburg and D.A. Keim, "An Efficient Approach to Clustering in Large Multimedia Databases with Noise," Proc. Fourth Int'l Conf. Knowledge Discovery and Data Mining (KDD), 1998.
28 ms
(Ver 2.0)

Marketing Automation Platform Marketing Automation Tool