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        <identifier>oai:drops-oai.dagstuhl.de:16416</identifier>
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          <dc:title>In-Range Farthest Point Queries and Related Problem in High Dimensions</dc:title>
          <dc:creator>Huang, Ziyun</dc:creator>
          <dc:creator>Xu, Jinhui</dc:creator>
          <dc:subject>Farthest Point Query</dc:subject>
          <dc:subject>Range Aggregate Query</dc:subject>
          <dc:subject>Minimum Enclosing Ball</dc:subject>
          <dc:subject>Approximation</dc:subject>
          <dc:subject>High Dimensional Space</dc:subject>
          <dc:description>Range-aggregate query is an important type of queries with numerous applications. It aims to obtain some structural information (defined by an aggregate function F(⋅)) of the points (from a point set P) inside a given query range B. In this paper, we study the range-aggregate query problem in high dimensional space for two aggregate functions: (1) F(P ∩ B) is the farthest point in P ∩ B to a query point q in ℝ^d and (2) F(P ∩ B) is the minimum enclosing ball (MEB) of P ∩ B. For problem (1), called In-Range Farthest Point (IFP) Query, we develop a bi-criteria approximation scheme: For any ε &gt; 0 that specifies the approximation ratio of the farthest distance and any γ &gt; 0 that measures the "fuzziness" of the query range, we show that it is possible to pre-process P into a data structure of size Õ_{ε,γ}(dn^{1+ρ}) in Õ_{ε,γ}(dn^{1+ρ}) time such that given any ℝ^d query ball B and query point q, it outputs in Õ_{ε,γ}(dn^ρ) time a point p that is a (1-ε)-approximation of the farthest point to q among all points lying in a (1+γ)-expansion B(1+γ) of B, where 0 &lt; ρ &lt; 1 is a constant depending on ε and γ and the hidden constants in big-O notations depend only on ε, γ and Polylog(nd). For problem (2), we show that the IFP result can be applied to develop query scheme with similar time and space complexities to achieve a (1+ε)-approximation for MEB. To the best of our knowledge, these are the first theoretical results on such high dimensional range-aggregate query problems. Our results are based on several new techniques, such as multi-scale construction and ball difference range query, which are interesting in their own rights and could be potentially used to solve other range-aggregate problems in high dimensional space.</dc:description>
          <dc:publisher>Schloss Dagstuhl – Leibniz-Zentrum für Informatik</dc:publisher>
          <dc:contributor>Ziyun Huang and Jinhui Xu</dc:contributor>
          <dc:date>2022</dc:date>
          <dc:relation>Is Part Of LIPIcs, Volume 229, 49th International Colloquium on Automata, Languages, and Programming (ICALP 2022)</dc:relation>
          <dc:type>InProceedings</dc:type>
          <dc:type>Text</dc:type>
          <dc:type>doc-type:ResearchArticle</dc:type>
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          <dc:identifier>doi:10.4230/LIPIcs.ICALP.2022.75</dc:identifier>
          <dc:identifier>urn:nbn:de:0030-drops-164167</dc:identifier>
          <dc:identifier>https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.ICALP.2022.75</dc:identifier>
          <dc:language>eng</dc:language>
          <dc:rights>https://creativecommons.org/licenses/by/4.0/legalcode</dc:rights>
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