Abstract
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.
Similar content being viewed by others
Explore related subjects
Discover the latest articles, news and stories from top researchers in related subjects.References
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 5:30
Choi DW, Chung CW (2015) Nearest neighborhood search in spatial databases. In: Proceedings of International Conference on Data Engineering, IEEE, pp 699–710
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):295–308
Hjaltason GR, Samet H (1999) Distance browsing in spatial databases. ACM Trans Database Syst (TODS) 24(2):265–318
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):1–6
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 797–803
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 201–212
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):263–274
Papadias D, Tao Y, Mouratidis K, Hui CK (2005) Aggregate nearest neighbor queries in spatial databases. ACM Trans Database Syst (TODS) 30(2):529–576
Roussopoulos N, Kelly S, Vincent F (1995) Nearest neighbor queries. In: Proceedings of ACM SIGMOD International Conference on Management of Data, ACM, pp 71–79
Tiger Census Bureau. https://www.census.gov/geo/maps-data/data/tiger.html [Accessed 28 Aug 2017]
Yang SO, Kim SS (2009) Spatial query processing based on minimum bounding in wireless sensor networks. J Inf Process Syst 5(4):229–236
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 688–699
Zhang D, Chan CY, Tan KL (2013) Nearest group queries. In: Proceedings of the Conference on Scientific and Statistical Database Management, ACM, 7
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Jung, KH., Jang, HJ., Chung, J. et al. User-Qualified Group Search using Bidirectional Sweep Planes. J Ambient Intell Human Comput 9, 1259–1265 (2018). https://doi.org/10.1007/s12652-017-0596-z
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s12652-017-0596-z