research-article

Reverse nearest neighbor search with a non-spatial aspect

Authors:

Jeong-hoon Park,

Chin-Wan Chung,

U KangAuthors Info & Claims

Information Systems, Volume 54, Issue C

Pages 92 - 112

https://doi.org/10.1016/j.is.2015.06.010

Published: 01 December 2015 Publication History

Abstract

With the recent surge in the use of the location-based service (LBS), the importance of spatial database queries has increased. The reverse nearest neighbor (RNN) search is one of the most popular spatial database queries. In most previous studies, the spatial distance is used for measuring the distance between objects. However, as the demands of users of the LBSs are becoming more complex, considering only the spatial factor as a distance measure is not sufficient. For example, through a hotel finding service, users want to choose a hotel considering not only the spatial distance, but also the non-spatial aspect of the hotel such as the quality which can be represented by the number of stars. Therefore, services that consider both spatial and non-spatial factors in measuring the distance are more useful for users. In such a case, techniques proposed in the previous studies cannot be used since the distance measure is different. In this paper, we propose an efficient method for the RNN search in which a distance measure involves both the spatial distance and the non-spatial aspect of an object. We conduct extensive experiments on a large dataset to evaluate the efficiency of the proposed method. The experimental results show that the proposed method is significantly efficient and scalable. HighlightsWe firstly address the reverse nearest neighbor search with a non spatial aspect.We propose an effective method to filter out items before performing the RNN search.An efficient algorithm for our problem, based on a 2-layered structure, is proposed.Experiments using synthetic/real datasets show our method is efficient and scalable.

References

[1]

F. Korn, S. Muthukrishnan, Influence sets based on reverse nearest neighbor queries, in: Proceedings of the 2000 ACM SIGMOD International Conference on Management of Data, SIGMOD'00, ACM, New York, NY, USA, 2000, pp. 201-212.

[2]

C. Yang, K.-I. Lin, An index structure for efficient reverse nearest neighbor queries, in: Proceedings of the 17th International Conference on Data Engineering, IEEE Computer Society, Washington, DC, USA, 2001, pp. 485-492. URL {http://dl.acm.org/citation.cfm?id=645484.656392}.

[3]

K.-I. Lin, M. Nolen, C. Yang, Applying bulk insertion techniques for dynamic reverse nearest neighbor problems, in: 2003 Proceedings of Seventh International Database Engineering and Applications Symposium, 2003, pp. 290-297.

[4]

I. Stanoi, D. Agrawal, A.E. Abbadi, Reverse nearest neighbor queries for dynamic databases, in: In ACM SIGMOD Workshop on Research Issues in Data Mining and Knowledge Discovery, 2000, pp. 44-53.

[5]

Y. Tao, D. Papadias, X. Lian, Reverse knn search in arbitrary dimensionality, in: Proceedings of the Thirtieth International Conference on Very Large Data Bases, vol. 30, VLDB'04, VLDB Endowment, 2004, pp. 744-755.

[6]

W. Wu, F. Yang, C.-Y. Chan, K.-L. Tan, Finch: evaluating reverse k-nearest-neighbor queries on location data, Proc. VLDB Endow. 1 (1) (2008) 1056-1067.

Digital Library

[7]

R. Benetis, C.S. Jensen, G. Karciauskas, S. Saltenis, Nearest neighbor and reverse nearest neighbor queries for moving objects, in: Proceedings of the 2002 International Symposium on Database Engineering & Applications, IDEAS'02, IEEE Computer Society, Washington, DC, USA, 2002, pp. 44-53.

Digital Library

[8]

T. Xia, D. Zhang, Continuous reverse nearest neighbor monitoring, in: Proceedings of the 22nd International Conference on Data Engineering, ICDE'06, IEEE Computer Society, Washington, DC, USA, 2006, p. 77.

[9]

J. Kang, M. Mokbel, S. Shekhar, T. Xia, D. Zhang, Continuous evaluation of monochromatic and bichromatic reverse nearest neighbors, in: IEEE 23rd International Conference on Data Engineering, 2007, ICDE 2007, 2007, pp. 806-815.

[10]

W. Wu, F. Yang, C.Y. Chan, K.-L. Tan, Continuous reverse k-nearest-neighbor monitoring, in: Proceedings of the Ninth International Conference on Mobile Data Management, MDM'08, IEEE Computer Society, Washington, DC, USA, 2008, pp. 132-139.

Digital Library

[11]

M.A. Cheema, X. Lin, Y. Zhang, W. Wang, W. Zhang, Lazy updates: an efficient technique to continuously monitoring reverse knn, Proc. VLDB Endow. 2 (1) (2009) 1138-1149.

Digital Library

[12]

J. Lu, Y. Lu, G. Cong, Reverse spatial and textual k nearest neighbor search, in: Proceedings of the 2011 ACM SIGMOD International Conference on Management of Data, SIGMOD'11, ACM, New York, NY, USA, 2011, pp. 349-360.

Digital Library

[13]

Y. Tao, M.L. Yiu, N. Mamoulis, Reverse nearest neighbor search in metric spaces, IEEE Trans. Knowl. Data Eng., 18 (2006) 1239-1252.

Digital Library

[14]

A. Singh, H. Ferhatosmanoglu, A.c. Tosun, High dimensional reverse nearest neighbor queries, in: Proceedings of the Twelfth International Conference on Information and Knowledge Management, CIKM'03, ACM, New York, NY, USA, 2003, pp. 91-98.

Digital Library

[15]

M.L. Yiu, D. Papadias, N. Mamoulis, Y. Tao, Reverse nearest neighbors in large graphs, IEEE Trans. Knowl. Data Eng., 18 (2006) 540-553.

Digital Library

[16]

A. Vlachou, C. Doulkeridis, Y. Kotidis, K. Norvag, Reverse top-k queries, in: 2010 IEEE 26th International Conference on Data Engineering (ICDE), 2010, pp. 365-376.

[17]

A. Vlachou, C. Doulkeridis, K. Nørvåg, Y. Kotidis, Branch-and-bound algorithm for reverse top-k queries, in: Proceedings of the 2013 ACM SIGMOD International Conference on Management of Data, SIGMOD'13, ACM, New York, NY, USA, 2013, pp. 481-492.

Digital Library

[18]

A. Vlachou, C. Doulkeridis, K. Nørvåg, Monitoring reverse top-k queries over mobile devices, in: Proceedings of the 10th ACM International Workshop on Data Engineering for Wireless and Mobile Access, MobiDE'11, ACM, New York, NY, USA, 2011, pp. 17-24.

Digital Library

[19]

I. De Felipe, V. Hristidis, N. Rishe, Keyword search on spatial databases, in: Proceedings of the 2008 IEEE 24th International Conference on Data Engineering, ICDE'08, IEEE Computer Society, Washington, DC, USA, 2008, pp. 656-665.

Digital Library

[20]

G. Cong, C.S. Jensen, D. Wu, Efficient retrieval of the top-k most relevant spatial web objects, Proc. VLDB Endow. 2 (1) (2009) 337-348.

Digital Library

[21]

X. Cao, G. Cong, C.S. Jensen, Retrieving top-k prestige-based relevant spatial web objects, Proc. VLDB Endow. 3(1-2) (2010) 373-384.

Digital Library

[22]

A. Gyttman, R-trees. a dynamic index structure for spatial searching, in: Proceedings of the SIGMOD, 1984, pp. 47-57.

[23]

V. Kostakos, Is the crowd's wisdom biased? A quantitative analysis of three online communities, in: Proceedings of the 2009 International Conference on Computational Science and Engineering, vol. 04, CSE'09, IEEE Computer Society, Washington, DC, USA, 2009, pp. 251-255.

Digital Library

[24]

E.W. Weisstein, Square line picking. from mathworld-a wolfram web resource, {http://mathworld.wolfram.com/SquareLinePicking.html}.

[25]

H. Samet, Foundations of Multidimensional and Metric Data Structures (The Morgan Kaufmann Series in Computer Graphics and Geometric Modeling), Morgan Kaufmann Publishers Inc., San Francisco, CA, USA, 2005.

[26]

S. Nutanong, E.H. Jacox, H. Samet, An incremental Hausdorff distance calculation algorithm, Proc. VLDB Endow. 4 (8) (2011) 506-517.

Digital Library

Cited By

Ahmed PEldawy AHristidis VTsotras V(2022)Reverse spatial top-k keyword queriesThe VLDB Journal — The International Journal on Very Large Data Bases10.1007/s00778-022-00759-932:3(501-524)Online publication date: 25-Jul-2022
https://dl.acm.org/doi/10.1007/s00778-022-00759-9
Yang SCheema MLin XZhang YZhang W(2017)Reverse k nearest neighbors queries and spatial reverse top-k queriesThe VLDB Journal — The International Journal on Very Large Data Bases10.1007/s00778-016-0445-226:2(151-176)Online publication date: 1-Apr-2017
https://dl.acm.org/doi/10.1007/s00778-016-0445-2

Reverse nearest neighbor search with a non-spatial aspect
1. Information systems
  1. Information systems applications

Recommendations

Reverse k-nearest neighbor search in the presence of obstacles

In this paper, we study a new form of reverse nearest neighbor (RNN) queries, i.e., obstructed reverse nearest neighbor (ORNN) search. It considers the impact of obstacles on the distance between objects, which is ignored by the existing work on RNN ...
Ranked Reverse Nearest Neighbor Search

Given a set of data points P and a query point q in a multidimensional space, Reverse Nearest Neighbor (RNN) query finds data points in P whose nearest neighbors are q. Reverse k-Nearest Neighbor (RkNN) query (where k ≥ 1) generalizes RNN query to find ...
A new approach for maximizing bichromatic reverse nearest neighbor search

Maximizing bichromatic reverse nearest neighbor (MaxBRNN) is a variant of bichromatic reverse nearest neighbor (BRNN). The purpose of the MaxBRNN problem is to find an optimal region that maximizes the size of BRNNs. This problem has lots of real ...

Comments

Information & Contributors

Information

Published In

cover image Information Systems

Information Systems Volume 54, Issue C

December 2015

356 pages

ISSN:0306-4379

Issue’s Table of Contents

Copyright © Elsevier Ltd.

Publisher

Elsevier Science Ltd.

United Kingdom

Publication History

Published: 01 December 2015

Author Tags

Qualifiers

Research-article

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

2
Total Citations
View Citations
0
Total Downloads

Downloads (Last 12 months)0
Downloads (Last 6 weeks)0

Reflects downloads up to 28 Jan 2025

Other Metrics

View Author Metrics

Citations

Cited By

Ahmed PEldawy AHristidis VTsotras V(2022)Reverse spatial top-k keyword queriesThe VLDB Journal — The International Journal on Very Large Data Bases10.1007/s00778-022-00759-932:3(501-524)Online publication date: 25-Jul-2022
https://dl.acm.org/doi/10.1007/s00778-022-00759-9
Yang SCheema MLin XZhang YZhang W(2017)Reverse k nearest neighbors queries and spatial reverse top-k queriesThe VLDB Journal — The International Journal on Very Large Data Bases10.1007/s00778-016-0445-226:2(151-176)Online publication date: 1-Apr-2017
https://dl.acm.org/doi/10.1007/s00778-016-0445-2

View Options

View options

Figures

Tables

Media

View Issue’s Table of Contents