Continuous evaluation of monochromatic and bichromatic reverse nearest neighbors

James M. Kang, Mohamed Mokbel, Shashi Shekhar, Tian Xia, Donghui Zhang

Research output: Chapter in Book/Report/Conference proceedingConference contribution

83 Citations (Scopus)

Abstract

This paper presents a novel algorithm for Incremental and General Evaluation of continuous Reverse Nearest neighbor queries (IGERN, for short). The IGERN algorithm is general as it is applicable for both the monochromatic and bichromatic reverse nearest neighbor queries. The incremental aspect of IGERN is achieved through determining only a small set of objects to be monitored. While previous algorithms for monochromatic queries rely mainly on monitoring six pie regions, IGERN takes a radical approach by monitoring only a single region around the query object. The IGERN algorithm clearly outperforms the state-of-theart algorithms in monochromatic queries. In addition, the IGERN algorithm presents the first attempt for continuous evaluation of bichromatic reverse nearest neighbor queries. The computational complexity of IGERN is presented in comparison to the state-of-the-art algorithms in the monochromatic case and to the use of Voronoi diagrams for the bichromatic case. In addition, the correctness of IGERN in both the monochromatic and bichromatic cases are proved. Extensive experimental analysis shows that IGERN is efficient, is scalable, and outperforms previous techniques for continuous reverse nearest neighbor queries.

Original languageEnglish
Title of host publication23rd International Conference on Data Engineering, ICDE 2007
Pages806-815
Number of pages10
DOIs
Publication statusPublished - 24 Sep 2007
Externally publishedYes
Event23rd International Conference on Data Engineering, ICDE 2007 - Istanbul, Turkey
Duration: 15 Apr 200720 Apr 2007

Other

Other23rd International Conference on Data Engineering, ICDE 2007
CountryTurkey
CityIstanbul
Period15/4/0720/4/07

Fingerprint

Monitoring
Computational complexity

ASJC Scopus subject areas

  • Software
  • Signal Processing
  • Information Systems

Cite this

Kang, J. M., Mokbel, M., Shekhar, S., Xia, T., & Zhang, D. (2007). Continuous evaluation of monochromatic and bichromatic reverse nearest neighbors. In 23rd International Conference on Data Engineering, ICDE 2007 (pp. 806-815). [4221729] https://doi.org/10.1109/ICDE.2007.367926

Continuous evaluation of monochromatic and bichromatic reverse nearest neighbors. / Kang, James M.; Mokbel, Mohamed; Shekhar, Shashi; Xia, Tian; Zhang, Donghui.

23rd International Conference on Data Engineering, ICDE 2007. 2007. p. 806-815 4221729.

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Kang, JM, Mokbel, M, Shekhar, S, Xia, T & Zhang, D 2007, Continuous evaluation of monochromatic and bichromatic reverse nearest neighbors. in 23rd International Conference on Data Engineering, ICDE 2007., 4221729, pp. 806-815, 23rd International Conference on Data Engineering, ICDE 2007, Istanbul, Turkey, 15/4/07. https://doi.org/10.1109/ICDE.2007.367926
Kang JM, Mokbel M, Shekhar S, Xia T, Zhang D. Continuous evaluation of monochromatic and bichromatic reverse nearest neighbors. In 23rd International Conference on Data Engineering, ICDE 2007. 2007. p. 806-815. 4221729 https://doi.org/10.1109/ICDE.2007.367926
Kang, James M. ; Mokbel, Mohamed ; Shekhar, Shashi ; Xia, Tian ; Zhang, Donghui. / Continuous evaluation of monochromatic and bichromatic reverse nearest neighbors. 23rd International Conference on Data Engineering, ICDE 2007. 2007. pp. 806-815
@inproceedings{d02a836fb9684849adb905befb82e6fc,
title = "Continuous evaluation of monochromatic and bichromatic reverse nearest neighbors",
abstract = "This paper presents a novel algorithm for Incremental and General Evaluation of continuous Reverse Nearest neighbor queries (IGERN, for short). The IGERN algorithm is general as it is applicable for both the monochromatic and bichromatic reverse nearest neighbor queries. The incremental aspect of IGERN is achieved through determining only a small set of objects to be monitored. While previous algorithms for monochromatic queries rely mainly on monitoring six pie regions, IGERN takes a radical approach by monitoring only a single region around the query object. The IGERN algorithm clearly outperforms the state-of-theart algorithms in monochromatic queries. In addition, the IGERN algorithm presents the first attempt for continuous evaluation of bichromatic reverse nearest neighbor queries. The computational complexity of IGERN is presented in comparison to the state-of-the-art algorithms in the monochromatic case and to the use of Voronoi diagrams for the bichromatic case. In addition, the correctness of IGERN in both the monochromatic and bichromatic cases are proved. Extensive experimental analysis shows that IGERN is efficient, is scalable, and outperforms previous techniques for continuous reverse nearest neighbor queries.",
author = "Kang, {James M.} and Mohamed Mokbel and Shashi Shekhar and Tian Xia and Donghui Zhang",
year = "2007",
month = "9",
day = "24",
doi = "10.1109/ICDE.2007.367926",
language = "English",
isbn = "1424408032",
pages = "806--815",
booktitle = "23rd International Conference on Data Engineering, ICDE 2007",

}

TY - GEN

T1 - Continuous evaluation of monochromatic and bichromatic reverse nearest neighbors

AU - Kang, James M.

AU - Mokbel, Mohamed

AU - Shekhar, Shashi

AU - Xia, Tian

AU - Zhang, Donghui

PY - 2007/9/24

Y1 - 2007/9/24

N2 - This paper presents a novel algorithm for Incremental and General Evaluation of continuous Reverse Nearest neighbor queries (IGERN, for short). The IGERN algorithm is general as it is applicable for both the monochromatic and bichromatic reverse nearest neighbor queries. The incremental aspect of IGERN is achieved through determining only a small set of objects to be monitored. While previous algorithms for monochromatic queries rely mainly on monitoring six pie regions, IGERN takes a radical approach by monitoring only a single region around the query object. The IGERN algorithm clearly outperforms the state-of-theart algorithms in monochromatic queries. In addition, the IGERN algorithm presents the first attempt for continuous evaluation of bichromatic reverse nearest neighbor queries. The computational complexity of IGERN is presented in comparison to the state-of-the-art algorithms in the monochromatic case and to the use of Voronoi diagrams for the bichromatic case. In addition, the correctness of IGERN in both the monochromatic and bichromatic cases are proved. Extensive experimental analysis shows that IGERN is efficient, is scalable, and outperforms previous techniques for continuous reverse nearest neighbor queries.

AB - This paper presents a novel algorithm for Incremental and General Evaluation of continuous Reverse Nearest neighbor queries (IGERN, for short). The IGERN algorithm is general as it is applicable for both the monochromatic and bichromatic reverse nearest neighbor queries. The incremental aspect of IGERN is achieved through determining only a small set of objects to be monitored. While previous algorithms for monochromatic queries rely mainly on monitoring six pie regions, IGERN takes a radical approach by monitoring only a single region around the query object. The IGERN algorithm clearly outperforms the state-of-theart algorithms in monochromatic queries. In addition, the IGERN algorithm presents the first attempt for continuous evaluation of bichromatic reverse nearest neighbor queries. The computational complexity of IGERN is presented in comparison to the state-of-the-art algorithms in the monochromatic case and to the use of Voronoi diagrams for the bichromatic case. In addition, the correctness of IGERN in both the monochromatic and bichromatic cases are proved. Extensive experimental analysis shows that IGERN is efficient, is scalable, and outperforms previous techniques for continuous reverse nearest neighbor queries.

UR - http://www.scopus.com/inward/record.url?scp=34548748056&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=34548748056&partnerID=8YFLogxK

U2 - 10.1109/ICDE.2007.367926

DO - 10.1109/ICDE.2007.367926

M3 - Conference contribution

AN - SCOPUS:34548748056

SN - 1424408032

SN - 9781424408030

SP - 806

EP - 815

BT - 23rd International Conference on Data Engineering, ICDE 2007

ER -