k-nearest reliable neighbor search in crowdsourced LBSs
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Jang, Hong-Jun | - |
dc.contributor.author | Kim, Byoungwook | - |
dc.contributor.author | Jung, Soon-Young | - |
dc.date.accessioned | 2022-11-04T22:42:12Z | - |
dc.date.available | 2022-11-04T22:42:12Z | - |
dc.date.created | 2022-11-04 | - |
dc.date.issued | 2021-01-25 | - |
dc.identifier.issn | 1074-5351 | - |
dc.identifier.uri | https://scholar.korea.ac.kr/handle/2021.sw.korea/144717 | - |
dc.description.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. | - |
dc.language | English | - |
dc.language.iso | en | - |
dc.publisher | WILEY | - |
dc.subject | SPATIAL DATA | - |
dc.subject | QUERIES | - |
dc.title | k-nearest reliable neighbor search in crowdsourced LBSs | - |
dc.type | Article | - |
dc.contributor.affiliatedAuthor | Jung, Soon-Young | - |
dc.identifier.doi | 10.1002/dac.4097 | - |
dc.identifier.scopusid | 2-s2.0-85071504147 | - |
dc.identifier.wosid | 000599928100015 | - |
dc.identifier.bibliographicCitation | INTERNATIONAL JOURNAL OF COMMUNICATION SYSTEMS, v.34, no.2 | - |
dc.relation.isPartOf | INTERNATIONAL JOURNAL OF COMMUNICATION SYSTEMS | - |
dc.citation.title | INTERNATIONAL JOURNAL OF COMMUNICATION SYSTEMS | - |
dc.citation.volume | 34 | - |
dc.citation.number | 2 | - |
dc.type.rims | ART | - |
dc.type.docType | Article | - |
dc.description.journalClass | 1 | - |
dc.description.journalRegisteredClass | scie | - |
dc.description.journalRegisteredClass | scopus | - |
dc.relation.journalResearchArea | Engineering | - |
dc.relation.journalResearchArea | Telecommunications | - |
dc.relation.journalWebOfScienceCategory | Engineering, Electrical & Electronic | - |
dc.relation.journalWebOfScienceCategory | Telecommunications | - |
dc.subject.keywordPlus | SPATIAL DATA | - |
dc.subject.keywordPlus | QUERIES | - |
dc.subject.keywordAuthor | k& | - |
dc.subject.keywordAuthor | #8208 | - |
dc.subject.keywordAuthor | nearest reliable neighbor query | - |
dc.subject.keywordAuthor | location& | - |
dc.subject.keywordAuthor | #8208 | - |
dc.subject.keywordAuthor | based services | - |
dc.subject.keywordAuthor | nearest neighbor query | - |
dc.subject.keywordAuthor | spatial databases | - |
dc.subject.keywordAuthor | spatio& | - |
dc.subject.keywordAuthor | #8208 | - |
dc.subject.keywordAuthor | temporal databases | - |
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