TY - GEN
T1 - Range-based obstructed nearest neighbor queries
AU - Zhu, Huaijie
AU - Yang, Xiaochun
AU - Wang, Bin
AU - Lee, Wang Chien
N1 - Funding Information:
This work is partially supported by the NSF of China for Outstanding Young Scholars under grant No. 61322208, the National Basic Research Program of China (973 Program) under grant No. 2012CB316201, the NSF of China for Key Program under grant No. 61532021, and the NSF of China under grant Nos. 61272178 and 61572122. We are grateful to the anonymous reviewers for their constructive comments on this paper. Xiaochun Yang is the corresponding author of this work.
Publisher Copyright:
© 2016 ACM.
PY - 2016/6/26
Y1 - 2016/6/26
N2 - In this paper, we study a novel variant of obstructed nearest neighbor queries, namely, range-based obstructed nearest neighbor (RONN) search. A natural generalization of continuous obstructed nearest-neighbor (CONN), an RONN query retrieves the obstructed nearest neighbor for every point in a specified range. To process RONN, we first propose a CONN-Based (CONNB) algorithm as our baseline, which reduces the RONN query into a range query and four CONN queries processed using an R-tree. To address the shortcomings of the CONNB algorithm, we then propose a new RONN by R-tree Filtering (RONN-RF) algorithm, which explores effective filtering, also using R-tree. Next, we propose a new index, called O-tree, dedicated for indexing objects in the obstructed space. The novelty of O-tree lies in the idea of dividing the obstructed space into nonobstructed subspaces, aiming to efficiently retrieve highly qualified candidates for RONN processing. We develop an O-tree construction algorithm and propose a space division scheme, called optimal obstacle balance (OOB) scheme, to address the tree balance problem. Accordingly, we propose an efficient algorithm, called RONN by O-tree Acceleration (RONN-OA), which exploits O-tree to accelerate query processing of RONN. In addition, we extend O-tree for indexing polygons. At last, we conduct a comprehensive performance evaluation using both real and synthetic datasets to validate our ideas and the proposed algorithms. The experimental result shows that the RONN-OA algorithm outperforms the two R-tree based algorithms significantly. Moreover, we show that the OOB scheme achieves the best tree balance in O-tree and outperforms two baseline schemes.
AB - In this paper, we study a novel variant of obstructed nearest neighbor queries, namely, range-based obstructed nearest neighbor (RONN) search. A natural generalization of continuous obstructed nearest-neighbor (CONN), an RONN query retrieves the obstructed nearest neighbor for every point in a specified range. To process RONN, we first propose a CONN-Based (CONNB) algorithm as our baseline, which reduces the RONN query into a range query and four CONN queries processed using an R-tree. To address the shortcomings of the CONNB algorithm, we then propose a new RONN by R-tree Filtering (RONN-RF) algorithm, which explores effective filtering, also using R-tree. Next, we propose a new index, called O-tree, dedicated for indexing objects in the obstructed space. The novelty of O-tree lies in the idea of dividing the obstructed space into nonobstructed subspaces, aiming to efficiently retrieve highly qualified candidates for RONN processing. We develop an O-tree construction algorithm and propose a space division scheme, called optimal obstacle balance (OOB) scheme, to address the tree balance problem. Accordingly, we propose an efficient algorithm, called RONN by O-tree Acceleration (RONN-OA), which exploits O-tree to accelerate query processing of RONN. In addition, we extend O-tree for indexing polygons. At last, we conduct a comprehensive performance evaluation using both real and synthetic datasets to validate our ideas and the proposed algorithms. The experimental result shows that the RONN-OA algorithm outperforms the two R-tree based algorithms significantly. Moreover, we show that the OOB scheme achieves the best tree balance in O-tree and outperforms two baseline schemes.
UR - http://www.scopus.com/inward/record.url?scp=84979704147&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84979704147&partnerID=8YFLogxK
U2 - 10.1145/2882903.2915234
DO - 10.1145/2882903.2915234
M3 - Conference contribution
AN - SCOPUS:84979704147
T3 - Proceedings of the ACM SIGMOD International Conference on Management of Data
SP - 2053
EP - 2068
BT - SIGMOD 2016 - Proceedings of the 2016 International Conference on Management of Data
PB - Association for Computing Machinery
T2 - 2016 ACM SIGMOD International Conference on Management of Data, SIGMOD 2016
Y2 - 26 June 2016 through 1 July 2016
ER -