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dc.contributor.authorChen, Zhongpu
dc.contributor.authorYao, Bin
dc.contributor.authorWang, Zhi-Jie
dc.contributor.authorGao, Xiaofeng
dc.contributor.authorShang, Shuo
dc.contributor.authorMa, Shuai
dc.contributor.authorGuo, Minyi
dc.date.accessioned2021-11-24T11:47:30Z
dc.date.available2021-11-24T11:47:30Z
dc.date.issued2020-02-25
dc.date.submitted2018-07-19
dc.identifier.citationChen, Z., Yao, B., Wang, Z.-J., Gao, X., Shang, S., Ma, S., & Guo, M. (2021). Flexible Aggregate Nearest Neighbor Queries and its Keyword-Aware Variant on Road Networks. IEEE Transactions on Knowledge and Data Engineering, 33(12), 3701–3715. doi:10.1109/tkde.2020.2975998
dc.identifier.issn1558-2191
dc.identifier.issn1041-4347
dc.identifier.doi10.1109/TKDE.2020.2975998
dc.identifier.urihttp://hdl.handle.net/10754/673759
dc.description.abstractAggregate nearest neighbor (Ann) query in both the euclidean space and road networks has been extensively studied, and the flexible aggregate nearest neighbor (Fann) problem further generalizes Ann by introducing an extra flexibility parameter \phi φ that ranges in (0, 1] (0,1]. In this article, we focus on Fann on road networks, denoted as Fann-\mathcal {R} R, and its keyword-aware variant, denoted as KFann-\mathcal {R} R. To solve these problems, we propose a series of universal (i.e., suitable for both max and sum) algorithms, including a Dijkstra-based algorithm that enumerates P P instead of \phi |Q|φ|Q|-combinations of Q Q, a queue-based approach that processes data points from-near-to-far, and a framework that combines incremental euclidean restriction (IER) and k kNN. We also propose a specific exact solution to max-Fann-\mathcal {R} R and a constant-factor ratio approximate solution to sum-Fann-\mathcal {R} R. These specific algorithms are easy to implement and can achieve excellent performance in some scenarios. Besides, we further extend this problem to top-k k and multiple Fann-\mathcal {R} R (resp., KFann-\mathcal {R} R) queries. We conduct a comprehensive experimental evaluation for the proposed algorithms on real datasets to demonstrate their superior efficiency and high quality.
dc.description.sponsorshipThis work (Bin Yao) was supported by the NSFC (U1636210)
dc.publisherInstitute of Electrical and Electronics Engineers (IEEE)
dc.relation.urlhttps://ieeexplore.ieee.org/document/9007415/
dc.rights(c) 2021 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, including reprinting/ republishing this material for advertising or promotional purposes, creating new collective works for resale or redistribution to servers or lists, or reuse of any copyrighted components of this work in other works.
dc.subjectRoad networks
dc.subjectindexing
dc.subjectspatial-keyword databases
dc.titleFlexible Aggregate Nearest Neighbor Queries and its Keyword-Aware Variant on Road Networks
dc.typeArticle
dc.contributor.departmentComputer, Electrical and Mathematical Science and Engineering (CEMSE) Division
dc.identifier.journalIEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING
dc.identifier.wosutWOS:000714713100004
dc.eprint.versionPost-print
dc.contributor.institutionDepartment of Computer Science and Engineering, Shanghai Jiao Tong University, Shanghai, 200240, China
dc.contributor.institutionCollege of Computer Science, Chongqing University, Chongqing, 400044, China
dc.contributor.institutionSchool of Data and Computer Science, Sun Yat-Sen University, Guangzhou 510275, China
dc.contributor.institutionSchool of Computer Science and Engineering, Beihang University, Beijing, China
dc.identifier.volume33
dc.identifier.issue12
dc.identifier.pages3701-3715
kaust.personShang, Shuo
dc.date.accepted2020-02-14
dc.identifier.eid2-s2.0-85118933952
dc.date.published-online2020-02-25
dc.date.published-print2021-12-01


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