User-Qualified Group Search using Bidirectional Sweep Planes

  • Kyoung-Ho Jung
  • Hong-Jun Jang
  • Jaehwa Chung
  • Soon-Young JungEmail author
Original Research


In this paper, we propose a nearest user-qualified group (NUG) query that searches a group of objects to obtain a result. In detail, given a dataset P, query q, distance δ, and cardinality k, the NUG query returns the nearest group of objects from q, such that more than k objects within δ distance from the point, called a representative, are in the group. Although the NUG query has large spectrum of applications, an efficient processing algorithm for NUG queries has not been studied so far. Therefore, we propose the plane sweep-based incremental search algorithm and heuristic that stops the plane sweep early to reduce the search space. A performance study is conducted on both synthetic and real datasets and our experimental results show that the proposed algorithm can improve the query performance in a variety of conditions.


Nearest neighbor query Nearest user-qualified group query Spatial query processing 


  1. Aissi S, Gouider MS, Sboui T, Said LB (2015) A spatial data warehouse recommendation approach: conceptual framework and experimental evaluation. Hum Cent Computing Inf Sci 5:30CrossRefGoogle Scholar
  2. Choi DW, Chung CW (2015) Nearest neighborhood search in spatial databases. In: Proceedings of International Conference on Data Engineering, IEEE, pp 699–710Google Scholar
  3. Deng K, Sadiq SW, Zhou X, Xu H, Fung GPC, Lu Y (2012) On group nearest group query processing. IEEE Trans Knowl Data Eng (TKDE) 24(2):295–308CrossRefGoogle Scholar
  4. Hjaltason GR, Samet H (1999) Distance browsing in spatial databases. ACM Trans Database Syst (TODS) 24(2):265–318CrossRefGoogle Scholar
  5. Hong S, Chang J (2013) A new k-NN query processing algorithm based on multicasting-based cell expansion in location-based services. J Converg 4(4):1–6Google Scholar
  6. Jang HJ, Choi WS, Hyun KS, Lim T, Jung SY, Chung J (2015) In-memory processing for nearest user-specified group search. In: Park DS, Chao HC, Jeong YS, Park JH (Eds) Advances in Computer Science and Ubiquitous Computing, CSA & CUTE, Lecture Notes in Electrical Engineering, vol 373. Springer, Singapore, pp 797–803Google Scholar
  7. Korn F, Muthukrishnan S (2000) Influence sets based on reverse nearest neighbor queries. In: Proceedings of ACM SIGMOD International Conference on Management of Data, ACM, pp 201–212Google Scholar
  8. Li Y, Kim D, Shin BS (2016) Geohashed spatial index method for a location-aware WBAN Data monitoring system based on NoSQL. J Inf Process Syst 12(2):263–274Google Scholar
  9. Papadias D, Tao Y, Mouratidis K, Hui CK (2005) Aggregate nearest neighbor queries in spatial databases. ACM Trans Database Syst (TODS) 30(2):529–576CrossRefGoogle Scholar
  10. Roussopoulos N, Kelly S, Vincent F (1995) Nearest neighbor queries. In: Proceedings of ACM SIGMOD International Conference on Management of Data, ACM, pp 71–79Google Scholar
  11. Tiger Census Bureau. [Accessed 28 Aug 2017]
  12. Yang SO, Kim SS (2009) Spatial query processing based on minimum bounding in wireless sensor networks. J Inf Process Syst 5(4):229–236MathSciNetCrossRefGoogle Scholar
  13. Zhang D, Chee YM, Mondal A, Tung AKH, Kitsuregawa M (2009) Keyword search in spatial databases: Towards searching by document. In: Proceedings of International Conference on Data Engineering, IEEE, pp 688–699Google Scholar
  14. Zhang D, Chan CY, Tan KL (2013) Nearest group queries. In: Proceedings of the Conference on Scientific and Statistical Database Management, ACM, 7Google Scholar

Copyright information

© Springer-Verlag GmbH Germany 2017

Authors and Affiliations

  • Kyoung-Ho Jung
    • 1
  • Hong-Jun Jang
    • 1
  • Jaehwa Chung
    • 2
  • Soon-Young Jung
    • 1
    Email author
  1. 1.Department of Computer Science and EngineeringKorea UniversitySeoulSouth Korea
  2. 2.Department of Computer ScienceKorea National Open UniversitySeoulSouth Korea

Personalised recommendations