This paper presents a nearest user-specified group (NUG) search which called a clustered NN problem. Given a set of data points P and a query point q, NUG search finds the nearest subset c ⊂ P (|c| ≥ k) from q (called user-specified group) that satisfies given conditions. Motivated by the brute-force approach for NUG search requires O(|P|2) computational cost, we propose a faster algorithm to handle NUG problem with in-memory processing. We first define clustered objects above k as a user-specified group and the NUG search problem. Moreover, the proposed solution converts a NUG search problem to a graph formulation problem, and reduces processing cost with geometric-based heuristics. Our experimental results show that the efficiency and effectiveness of our proposed approach outperforms the conventional one.
[1]
Kian-Lee Tan,et al.
Nearest group queries
,
2013,
SSDBM.
[2]
Flip Korn,et al.
Influence sets based on reverse nearest neighbor queries
,
2000,
SIGMOD 2000.
[3]
Dong-Wan Choi,et al.
Nearest neighborhood search in spatial databases
,
2015,
2015 IEEE 31st International Conference on Data Engineering.
[4]
Nick Roussopoulos,et al.
Nearest neighbor queries
,
1995,
SIGMOD '95.
[5]
Kyriakos Mouratidis,et al.
Group nearest neighbor queries
,
2004,
Proceedings. 20th International Conference on Data Engineering.