The Community for Technology Leaders
RSS Icon
Issue No.03 - March (2014 vol.26)
pp: 608-622
Ying Zhang , The University of New South Wales, Sydney
Wenjie Zhang , The University of New South Wales, Sydney
Qianlu Lin , The University of New South Wales, Sydney
Xuemin Lin , The University of New South Wales, Sydney
Heng Tao Shen , The University of Queensland, Brisbane
As the uncertainty is inherent in a wide spectrum of applications such as radio frequency identification (RFID) networks and location-based services (LBS), it is highly demanded to address the uncertainty of the objects. In this paper, we propose a novel indexing structure, named $(U)$-Quadtree, to organize the uncertain objects in the multidimensional space such that the queries can be processed efficiently by taking advantage of $(U)$-Quadtree. Particularly, we focus on the range search on multidimensional uncertain objects since it is a fundamental query in a spatial database. We propose a cost model which carefully considers various factors that may impact the performance. Then, an effective and efficient index construction algorithm is proposed to build the optimal $(U)$-Quadtree regarding the cost model. We show that $(U)$-Quadtree can also efficiently support other types of queries such as uncertain range query and nearest neighbor query. Comprehensive experiments demonstrate that our techniques outperform the existing works on multidimensional uncertain objects.
Search problems, Indexing, Probabilistic logic, Probability density function, Upper bound, Radiofrequency identification,nearest neighbor search, Multidimensional uncertain objects, range search
Ying Zhang, Wenjie Zhang, Qianlu Lin, Xuemin Lin, Heng Tao Shen, "Effectively Indexing the Multidimensional Uncertain Objects", IEEE Transactions on Knowledge & Data Engineering, vol.26, no. 3, pp. 608-622, March 2014, doi:10.1109/TKDE.2013.21
[1] T.T.L. Tran, C. Sutton, R. Cocci, Y. Nie, Y. Diao, and P.J. Shenoy, "Probabilistic Inference over RFID Streams in Mobile Environments," Proc. IEEE Int'l Conf. Data Eng. (ICDE), 2009.
[2] Y. Tao, X. Xiao, and R. Cheng, "Range Search on Multidimensional Uncertain Data," ACM Trans. Database Systems, vol. 32, no. 3, article 15, 2007.
[3] H.-P. Kriegel, P. Kunath, M. Pfeifle, and M. Renz, "Probabilistic Similarity Join on Uncertain Data," Proc. 11th Int'l Conf. Database Systems for Advanced Applications (DASFAA), 2006.
[4] S. Singh, C. Mayfield, S. Prabhakar, R. Shah, and S.E. Hambrusch, "Indexing Uncertain Categorical Data," Proc. IEEE 23rd Int'l Conf. Data Eng. (ICDE), 2007.
[5] Y. Zhang, X. Lin, W. Zhang, J. Wang, and Q. Lin, "Effectively Indexing the Uncertain Space," IEEE Trans. Knowledge and Data Eng. (TKDE), 2010.
[6] X. Lian and L. Chen, "A Generic Framework for Handling Uncertain Data with Local Correlations," Proc. VLDB Endowment, vol. 4, pp. 12-21, 2010.
[7] R. Cheng, Y. Xia, S. Prabhakar, R. Shah, and J.S. Vitter, "Efficient Indexing Methods for Probabilistic Threshold Queries over Uncertain Data," Proc. 13th Int'l Conf. Very Large Data Bases (VLDB), 2004.
[8] F. Angiulli and F. Fassetti, "Indexing Uncertain Data in General Metric Space," IEEE Trans. Knowledge and Data Eng., vol. 24, no. 9, pp. 1640-1657, Sept. 2012.
[9] C. Böhm, M. Gruber, P. Kunath, A. Pryakhin, and M. Schubert, "ProVer: Probabilistic Video Retrieval Using the Gauss-Tree," Proc. IEEE 23rd Int'l Conf. Data Eng. (ICDE), 2007.
[10] C. Böhm, A. Pryakhin, and M. Schubert, "Probabilistic Ranking Queries on Gaussians," Proc. 18th Int'l Conf. Scientific and Statistical Database Management, 2006.
[11] P.K. Agarwal, S.-W. Cheng, Y. Tao, and K. Yi, "Indexing Uncertain Data," Proc. 28th ACM SIGMOD-SIGACT-SIGART Symp. Principles of Database Systems (PODS '09), 2009.
[12] C. Aggarwal and P. Yu, "On High Dimensional Indexing of Uncertain Data," Proc. IEEE 24th Int'l Conf. Data Eng. (ICDE), 2008.
[13] Y. Ma, D.V. Kalashnikov, and S. Mehrotra, "Toward Managing Uncertain Spatial Information for Situational Awareness Applications," IEEE Trans. Knowledge and Data Eng., vol. 20, no. 10, pp. 1408-1423, Oct. 2008.
[14] H. Kimura, S. Madden, and S.B. Zdonik, "Upi: A Primary Index for Uncertain Databases," Proc. VLDB Endowment, vol. 3, pp. 630-637, 2010.
[15] R.A. Finkel and J.L. Bentley, "Quad Trees: A Data Structure for Retrieval on Composite Keys," Acta Informatica, vol. 4, pp. 1-9, 1974.
[16] G.R. Hjaltason and H. Samet, "Speeding up Construction of Pmr Quadtree-Based Spatial Indexes," The Int'l J. Very Large Data Bases, vol. 11, pp. 109-137, 2002.
[17] C. Faloutsos, "Gray Codes for Partial Match and Range Queries," IEEE Trans. Software Eng., vol. 14, no. 10, pp. 1381-1393, Oct. 1988.
[18] G. Beskales, M.A. Soliman, and I.F. Ilyas, "Efficient Search for the Top-K Probable Nearest Neighbors in Uncertain Databases," Proc. VLDB Endowment, vol. 1, no. 1, pp. 326-339, 2008.
[19] M.A. Soliman, I.F. Ilyas, and K.C. Chang, "Top-$k$ Query Processing in Uncertain Databases," Proc. IEEE 23rd Int'l Conf. Data Eng. (ICDE), 2007.
[20] M. Hua, J. Pei, W. Zhang, and X. Lin, "Ranking Queries on Uncertain Data: A Probabilistic Threshold Approach," Proc. ACM SIGMOD Int'l Conf. Management of Data, 2008.
[21] X. Zhang and J. Chomicki, "On the Semantics and Evaluation of Top-K Queries in Probabilistic Databases," Proc. IEEE 24th Int'l Conf. Data Eng., 2008.
[22] G. Cormode, F. Li, and K. Yi, "Semantics of Ranking Queries for Probabilistic Data and Expected Ranks," Proc. IEEE 25th Int'l Conf. Data Eng., 2009.
[23] J. Li, B. Saha, and A. Deshpande, "A Unified Approach to Ranking in Probabilistic Databases," The Int'l J. Very Large Data Bases, vol. 20, no. 2, pp. 249-275, 2011.
[24] Y. Zhang, X. Lin, G. Zhu, W. Zhang, and Q. Lin, "Efficient Rank Based KNN Query Processing over Uncertain Data," Proc. IEEE 26th Int'l Conf. Data Eng. (ICDE), 2010.
[25] S. Börzsönyi, D. Kossmann, and K. Stocker, "The Skyline Operator," Proc. 17th Int'l Conf. Data Eng. (ICDE), 2001.
28 ms
(Ver 2.0)

Marketing Automation Platform Marketing Automation Tool