k-nearest reliable neighbor search in crowdsourced LBSs

Hong Jun Jang, Byoungwook Kim, Soon Young Jung

Research output: Contribution to journalArticle

Abstract

To improve the quality of spatial information in a location-based services (LBS), crowdsourced LBS (cLBS) applications that receive additional information such as the visit time of static spatial objects from users have appeared. In this paper, we propose a new type of nearest neighbor (NN) query called the k-nearest reliable neighbor (kNRN) query, which searches for objects that are likely to exist. Suppose that in cLBSs, the user wants to find a restaurant that is likely to exist and is close to the user. In such a case, a kNRN query is highly recommended. In this paper, we formally define a data model in cLBSs and define reliable objects and a kNRN problem. As a brute-force approach to this problem in a massive dataset that has large computational and I/O costs, we propose a 3DR-tree-based baseline algorithm, 2DR-tree-based incremental algorithm, and an a3DR-tree-based branch-and-bound algorithm for kNRN queries. A performance study is conducted on both synthetic and real datasets. Our experimental results show the efficiency of our proposed methods.

Original languageEnglish
Article numbere4097
JournalInternational Journal of Communication Systems
DOIs
Publication statusAccepted/In press - 2019 Jan 1

Keywords

  • k-nearest reliable neighbor query
  • location-based services
  • nearest neighbor query
  • spatial databases
  • spatio-temporal databases

ASJC Scopus subject areas

  • Computer Networks and Communications
  • Electrical and Electronic Engineering

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