Nearest base-neighbor search on spatial datasets
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Jang, Hong-Jun | - |
dc.contributor.author | Hyun, Kyeong-Seok | - |
dc.contributor.author | Chung, Jaehwa | - |
dc.contributor.author | Jung, Soon-Young | - |
dc.date.accessioned | 2021-08-31T08:55:44Z | - |
dc.date.available | 2021-08-31T08:55:44Z | - |
dc.date.created | 2021-06-18 | - |
dc.date.issued | 2020-03 | - |
dc.identifier.issn | 0219-1377 | - |
dc.identifier.uri | https://scholar.korea.ac.kr/handle/2021.sw.korea/57525 | - |
dc.description.abstract | This paper presents a nearest base-neighbor (NBN) search that can be applied to a clustered nearest neighbor problem on spatial datasets with static properties. Given two sets of data points R and S, a query point q, distance threshold delta and cardinality threshold k, the NBN query retrieves a nearest point r (called the base-point) in R where more than k points in S are located within the distance delta. In this paper, we formally define a base-point and NBN problem. As the brute-force approach to this problem in massive datasets has large computational and I/O costs, we propose in-memory and external memory processing techniques for NBN queries. In particular, our proposed in-memory algorithms are used to minimize I/Os in the external memory algorithms. Furthermore, we devise a solution-based index, which we call the neighborhood-augmented grid, to dramatically reduce the search space. A performance study is conducted both on synthetic and real datasets. Our experimental results show the efficiency of our proposed approach. | - |
dc.language | English | - |
dc.language.iso | en | - |
dc.publisher | SPRINGER LONDON LTD | - |
dc.subject | QUERIES | - |
dc.title | Nearest base-neighbor search on spatial datasets | - |
dc.type | Article | - |
dc.contributor.affiliatedAuthor | Jung, Soon-Young | - |
dc.identifier.doi | 10.1007/s10115-019-01360-3 | - |
dc.identifier.scopusid | 2-s2.0-85069641510 | - |
dc.identifier.wosid | 000519573800002 | - |
dc.identifier.bibliographicCitation | KNOWLEDGE AND INFORMATION SYSTEMS, v.62, no.3, pp.867 - 897 | - |
dc.relation.isPartOf | KNOWLEDGE AND INFORMATION SYSTEMS | - |
dc.citation.title | KNOWLEDGE AND INFORMATION SYSTEMS | - |
dc.citation.volume | 62 | - |
dc.citation.number | 3 | - |
dc.citation.startPage | 867 | - |
dc.citation.endPage | 897 | - |
dc.type.rims | ART | - |
dc.type.docType | Article | - |
dc.description.journalClass | 1 | - |
dc.description.journalRegisteredClass | scie | - |
dc.description.journalRegisteredClass | scopus | - |
dc.relation.journalResearchArea | Computer Science | - |
dc.relation.journalWebOfScienceCategory | Computer Science, Artificial Intelligence | - |
dc.relation.journalWebOfScienceCategory | Computer Science, Information Systems | - |
dc.subject.keywordPlus | QUERIES | - |
dc.subject.keywordAuthor | Information technology | - |
dc.subject.keywordAuthor | k-nearest neighbor query | - |
dc.subject.keywordAuthor | Group version of nearest neighbor query | - |
dc.subject.keywordAuthor | Nearest base-neighbor query | - |
dc.subject.keywordAuthor | Spatial databases | - |
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