TY - GEN
T1 - Flexible Aggregate Nearest Neighbor Queries in Road Networks
AU - Yao, Bin
AU - Chen, Zhongpu
AU - Gao, Xiaofeng
AU - Shang, Shuo
AU - Ma, Shuai
AU - Guo, Minyi
N1 - KAUST Repository Item: Exported on 2020-10-01
Acknowledgements: National Basic Research Program (973 Program, No.2015CB352403)
PY - 2018/10/25
Y1 - 2018/10/25
N2 - Aggregate nearest neighbor (ANN) query has been studied in both the Euclidean space and road networks. The flexible aggregate nearest neighbor (FANN) problem further generalizes ANN by introducing an extra flexibility. Given a set of data points P, a set of query points Q, and a user-defined flexibility parameter φ that ranges in (0, 1], an FA N N query returns the best candidate from P, which minimizes the aggregate (usually max or sum) distance to any φ |Q| objects in Q. In this paper, we focus on the problem in road networks (denoted as FANNR), and present a series of universal (i.e., suitable for both max and sum) algorithms to answer FANNR queries in road networks, including a Dijkstra-based algorithm enumerating P, a queue-based approach that processes data points from-near-To-far, and a framework that combines Incremental Euclidean Restriction (IER) and kNN. We also propose a specific exact solution to max-FANNR and a specific approximate solution to sum-FANNR which can return a near-optimal result with a guaranteed constant-factor approximation. These specific algorithms are easy to implement and can achieve excellent performance in some scenarios. Besides, we further extend the FANNR to k-FANNR, and successfully adapt most of the proposed algorithms to answer k-FANNR queries. We conduct a comprehensive experimental evaluation for the proposed algorithms on real road networks to demonstrate their superior efficiency and high quality.
AB - Aggregate nearest neighbor (ANN) query has been studied in both the Euclidean space and road networks. The flexible aggregate nearest neighbor (FANN) problem further generalizes ANN by introducing an extra flexibility. Given a set of data points P, a set of query points Q, and a user-defined flexibility parameter φ that ranges in (0, 1], an FA N N query returns the best candidate from P, which minimizes the aggregate (usually max or sum) distance to any φ |Q| objects in Q. In this paper, we focus on the problem in road networks (denoted as FANNR), and present a series of universal (i.e., suitable for both max and sum) algorithms to answer FANNR queries in road networks, including a Dijkstra-based algorithm enumerating P, a queue-based approach that processes data points from-near-To-far, and a framework that combines Incremental Euclidean Restriction (IER) and kNN. We also propose a specific exact solution to max-FANNR and a specific approximate solution to sum-FANNR which can return a near-optimal result with a guaranteed constant-factor approximation. These specific algorithms are easy to implement and can achieve excellent performance in some scenarios. Besides, we further extend the FANNR to k-FANNR, and successfully adapt most of the proposed algorithms to answer k-FANNR queries. We conduct a comprehensive experimental evaluation for the proposed algorithms on real road networks to demonstrate their superior efficiency and high quality.
UR - http://hdl.handle.net/10754/630703
UR - https://ieeexplore.ieee.org/document/8509295
UR - http://www.scopus.com/inward/record.url?scp=85057132717&partnerID=8YFLogxK
U2 - 10.1109/ICDE.2018.00074
DO - 10.1109/ICDE.2018.00074
M3 - Conference contribution
SN - 9781538655207
SP - 761
EP - 772
BT - 2018 IEEE 34th International Conference on Data Engineering (ICDE)
PB - Institute of Electrical and Electronics Engineers (IEEE)
ER -