Dynamic Approximate Multiplicatively-Weighted Nearest Neighbors

Boris Aronov, Matthew J. Katz

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review


We describe a dynamic data structure for approximate nearest neighbor (ANN) queries with respect to multiplicatively weighted distances with additive offsets. Queries take polylogarithmic time, while the cost of updates is amortized polylogarithmic. The data structure requires near-linear space and construction time. The approach works not only for the Euclidean norm, but for other norms in Rd, for any fixed d. We employ our ANN data structure to construct a faster dynamic structure for approximate SINR queries, ensuring polylogarithmic query and polylogarithmic amortized update for the case of non-uniform power transmitters, thus closing a gap in previous state of the art. To obtain the latter result, we needed a data structure for dynamic approximate halfplane range counting in the plane. Since we could not find such a data structure in the literature, we also show how to dynamize one of the known static data structures.

Original languageEnglish
Title of host publication18th Scandinavian Symposium and Workshops on Algorithm Theory, SWAT 2022
EditorsArtur Czumaj, Qin Xin
PublisherSchloss Dagstuhl- Leibniz-Zentrum fur Informatik GmbH, Dagstuhl Publishing
ISBN (Electronic)9783959772365
StatePublished - 1 Jun 2022
Event18th Scandinavian Symposium and Workshops on Algorithm Theory, SWAT 2022 - Torshavn, Faroe Islands
Duration: 27 Jun 202229 Jun 2022

Publication series

NameLeibniz International Proceedings in Informatics, LIPIcs
ISSN (Print)1868-8969


Conference18th Scandinavian Symposium and Workshops on Algorithm Theory, SWAT 2022
Country/TerritoryFaroe Islands


  • Approximate nearest neighbors
  • Dynamic data structures
  • Nearest neighbor queries
  • Nearest neighbors
  • SINR queries
  • Weighted nearest neighbors

ASJC Scopus subject areas

  • Software


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