TY - GEN

T1 - Optimal-nearest-neighbor queries

AU - Yunjun, Gao

AU - Jing, Zhang

AU - Gencai, Chen

AU - Qing, Li

AU - Shen, Liu

AU - Chun, Chen

PY - 2008/10/1

Y1 - 2008/10/1

N2 - Given two sets DA and DB of multidimensional objects, a spatial region R, and a critical distance dc, an optimal-nearestneighbor (ONN) query retrieves outside R, the object in D B with maximum optimality. Let CAR (Sp, p) be the cardinality of the subset Sp of objects in DA which locate within R and are enclosed by the vicinity circle centered at p with radius dc. Then, an object o is said to be better than another one o′ if (i) CAR (So, o) > CAR (S0′ o′), or (ii) when CAR (So, o) = CAR (So′, o′) the sum of the weighted distance from each object in So to o is smaller than the sum of the weighted distance between every object in So′: and o′. This type of queries is quite useful in many decision making applications. In this paper, we formalize the ONN query, develop the optimality metric, and propose several algorithms for finding optimal nearest neighbors efficiently. Our techniques assume that both DA and DB are indexed by R-trees. Extensive experiments demonstrate the efficiency and scalability of our proposed algorithms using both real and synthetic datasets.

AB - Given two sets DA and DB of multidimensional objects, a spatial region R, and a critical distance dc, an optimal-nearestneighbor (ONN) query retrieves outside R, the object in D B with maximum optimality. Let CAR (Sp, p) be the cardinality of the subset Sp of objects in DA which locate within R and are enclosed by the vicinity circle centered at p with radius dc. Then, an object o is said to be better than another one o′ if (i) CAR (So, o) > CAR (S0′ o′), or (ii) when CAR (So, o) = CAR (So′, o′) the sum of the weighted distance from each object in So to o is smaller than the sum of the weighted distance between every object in So′: and o′. This type of queries is quite useful in many decision making applications. In this paper, we formalize the ONN query, develop the optimality metric, and propose several algorithms for finding optimal nearest neighbors efficiently. Our techniques assume that both DA and DB are indexed by R-trees. Extensive experiments demonstrate the efficiency and scalability of our proposed algorithms using both real and synthetic datasets.

UR - http://www.scopus.com/inward/record.url?scp=52649149532&partnerID=8YFLogxK

U2 - 10.1109/ICDE.2008.4497587

DO - 10.1109/ICDE.2008.4497587

M3 - Conference article published in proceeding or book

AN - SCOPUS:52649149532

SN - 9781424418374

T3 - Proceedings - International Conference on Data Engineering

SP - 1454

EP - 1456

BT - Proceedings of the 2008 IEEE 24th International Conference on Data Engineering, ICDE'08

T2 - 2008 IEEE 24th International Conference on Data Engineering, ICDE'08

Y2 - 7 April 2008 through 12 April 2008

ER -