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Hypersphere dominance: an optimal approach

Published: 18 June 2014 Publication History

Abstract

Hyperspheres are commonly used for representing uncertain objects (in uncertain databases) and for indexing spatial objects (in spatial databases). An interesting operator on hyperspheres called dominance is to decide for two given hyperspheres whether one dominates (or is closer than) the other wrt a given query hypersphere. In this paper, we propose an approach called Hyperbola which is optimal in the sense that it gives neither false positives nor false negatives and runs in linear time wrt the dimensionality. To the best of our knowledge, Hyperbola is the first optimal approach for the dominance problem on hyperespheres with any dimensionality. We also study an application of the dominance problem which relies on the dominance operator as the core component. We conducted extensive experiments on both real and synthetic datasets which verified our approaches.

References

[1]
T. Bernecker, T. Emrich, H.-P. Kriegel, M. Renz, S. Zanki, and A. Zufle. Efficient probabilistic reverse nearest neighbor query processing on uncertain data. In VLDB, 2011.
[2]
G. Beskales, M. A. Soliman, and I. F. IIyas. Efficient search for the top-k probable nearest neighbors in uncertain database. PVLDB, 2008.
[3]
G. Beskales, M. A. Soliman, and I. F. Ilyas. Efficient search for the top-k probable nearest neighbors in uncertain databases. In VLDB, 2008.
[4]
G. Bliss. Lectures on the calculus of variations. Chicago Univ. Press, 1947.
[5]
M. A. Cheema, X. Lin, W. Wang, W. Zhang, and J. Pei. Probabilistic reverse nearest neighbor queries on uncertain data. In TKDE, 2010.
[6]
J. Chen and R. Cheng. Efficient evaluation of imprecise location-dependent queries. In ICDE, 2007.
[7]
R. Cheng, L. Chen, J. Chen, and X. Xie. Evaluating probability threshold k-nearest neighbor queries over uncertain data. In EDBT, 2009.
[8]
R. Cheng, D. V. Kalashnikov, and S. Prabhakar. Querying imprecise data in moving object environments. TKDE, 2004.
[9]
P. Ciaccia, M. Patella, and P. Zezula. M-tree an efficient access method for similarity search in metric spaces. In VLDB, 1997.
[10]
T. cker Chiueh. Content-based image indexing. In VLDB, 1994.
[11]
E. Dellis and B. Seeger. Efficient computation of reverse skyline queries. In VLDB, 2007.
[12]
T. Emrich, F. Graf, H.-P. Kriegel, M. Schubert, and M. Thoma. Optimizing all-nearest-neighbor queries with trigonometric pruning. In Scientific and Statistical Database Management, 2010.
[13]
T. Emrich, H.-P. Kriegel, P. Kroger, M. Renz, and A. Zufle. Constrained reverse nearest neighbor search on mobile objects. In SIGSPATIAL, 2009.
[14]
T. Emrich, H.-P. Kriegel, P. Kroger, M. Renz, and A. Zufle. Boosting spatial pruning: on optimal pruning of mbrs. In SIGMOD, 2010.
[15]
G. Hjaltason and H. Samet. Distance browsing in spatial databases. TODS, 1999.
[16]
H. Hu and D. L. Lee. Range nearest neighbor query. In TKDE, 2006.
[17]
Irving and R. S. Integers, polynomials, and rings. Springer-Verlag, 2004.
[18]
N. Katayama and S. Satoh. The SR-tree: An index structure for high-dimensional nearest neighbor queries. In SIGMOD, 1997.
[19]
H.-P. Kriegel, P. Kunath, and M. Renz. Probabilistic nearest-neighbor query on uncertain objects. In DASFAA, 2007.
[20]
R. Kurniawati, J. Jin, and J. Shepherd. The SS+-tree: An improved index structure for similarity searches in a high-dimensional feature space. In Proc. 5th Storage and Retrieval for Image and Video Databases SPIE, 1997.
[21]
C. Li. Enabling data retrieval: By ranking and beyond. In Ph.D. Dissertation, University of Illinois at Urbana-Champaign, 2007.
[22]
X. Lian and L. Chen. Efficient processing of probabilistic reverse nearest neighbor queries over uncertain data. In VLDBJ, 2009.
[23]
X. Lian and L. Chen. Probabilistic inverse ranking queries over uncertain data. In DASFAA, 2009.
[24]
X. Lian and L. Chen. Top-k dominating queries in uncertain databases. In EDBT, 2009.
[25]
V. Ljosa and M. K. Singh. Apla: Indexing arbitrary probability distributions. In ICDE, 2007.
[26]
N. Roussopoulos, S. Kelley, and F. Vincent. Nearest neighbor queries. In Acm Sigmod Record, volume 24, pages 71--79, 1995.
[27]
M. Sharifzadeh and C. Shahabi. The spatial skyline queries. In VLDB, 2006.
[28]
Z. Song and N. Roussopoulos. K-nearest neighbor search for moving query point. In SSTD, 2001.
[29]
Y. Tao, D. Papadias, and X. Lian. Reverse knn search in arbitrary dimensionality. In VLDB, 2004.
[30]
Y. Tao, D. Papadias, and Q. Shen. Continuous nearest neighbor search. In VLDB, 2002.
[31]
D. White and R. Jain. Similarity indexing with the SS-tree. In icde, page 516. Published by the IEEE Computer Society, 1996.
[32]
X. Xie, R. Cheng, M. L. Yiu, L. Sun, and J. Chen. Uv-diagram: A voronoi diagram for uncertain spatial databases. In VLDBJ, 2012.
[33]
M. L. Yiu and N. Mamoulis. Efficient processing of top-k dominating queries on multi-dimensional data. In VLDB, 2007.
[34]
P. Zhang, R. Cheng, N. Mamoulis, M. Renz, A. Zufile, Y. Tang, and T. Emrich. Voronoi-based nearest neighbor search for multi-dimensional uncertain databases. In ICDE, 2013.

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cover image ACM Conferences
SIGMOD '14: Proceedings of the 2014 ACM SIGMOD International Conference on Management of Data
June 2014
1645 pages
ISBN:9781450323765
DOI:10.1145/2588555
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Published: 18 June 2014

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Author Tags

  1. hyperbola
  2. hypersphere dominance
  3. pruning

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SIGMOD '14 Paper Acceptance Rate 107 of 421 submissions, 25%;
Overall Acceptance Rate 785 of 4,003 submissions, 20%

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