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dc.contributor.authorYao, Bin
dc.contributor.authorChen, Zhongpu
dc.contributor.authorGao, Xiaofeng
dc.contributor.authorShang, Shuo
dc.contributor.authorMa, Shuai
dc.contributor.authorGuo, Minyi
dc.date.accessioned2018-12-31T14:13:20Z
dc.date.available2018-12-31T14:13:20Z
dc.date.issued2018-10-25
dc.identifier.citationYao B, Chen Z, Gao X, Shang S, Ma S, et al. (2018) Flexible Aggregate Nearest Neighbor Queries in Road Networks. 2018 IEEE 34th International Conference on Data Engineering (ICDE). Available: http://dx.doi.org/10.1109/ICDE.2018.00074.
dc.identifier.doi10.1109/ICDE.2018.00074
dc.identifier.urihttp://hdl.handle.net/10754/630703
dc.description.abstractAggregate 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.
dc.description.sponsorshipNational Basic Research Program (973 Program, No.2015CB352403)
dc.publisherInstitute of Electrical and Electronics Engineers (IEEE)
dc.relation.urlhttps://ieeexplore.ieee.org/document/8509295
dc.subjectANN
dc.subjectFANN
dc.subjectkNN
dc.subjectroad networks
dc.titleFlexible Aggregate Nearest Neighbor Queries in Road Networks
dc.typeConference Paper
dc.contributor.departmentComputer Science Program
dc.identifier.journal2018 IEEE 34th International Conference on Data Engineering (ICDE)
dc.conference.date2018-04-16 to 2018-04-19
dc.conference.name34th IEEE International Conference on Data Engineering, ICDE 2018
dc.conference.locationParis, FRA
dc.contributor.institutionGuangdong Key Laboratory of Big Data Analysis and Processing, , China
dc.contributor.institutionDepartment of Computer Science and Engineering, Shanghai Jiao Tong University, , China
dc.contributor.institutionSKLSDE Lab, Beihang University, , China
dc.contributor.institutionBeijing Advanced Innovation Center for Big Data and Brain Computing, Beijing, , China
kaust.personMa, Shuai
dc.date.published-online2018-10-25
dc.date.published-print2018-04


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