Dynamic Nearest Neighbor Queries in Euclidean Space
Given a query point q and a set D of data points, a nearest neighbor (NN) query returns the data point p in D that minimizes the distance DIST(q,p), where the distance function DIST(,) is the L2norm. One important variant of this query type is kNN query, which returns k data points with the minimum...
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2017
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sg-smu-ink.sis_research-42192018-01-18T05:41:05Z Dynamic Nearest Neighbor Queries in Euclidean Space NUTANONG, Sarana ALI, Mohammed Eunus TANIN, Egemen MOURATIDIS, Kyriakos Given a query point q and a set D of data points, a nearest neighbor (NN) query returns the data point p in D that minimizes the distance DIST(q,p), where the distance function DIST(,) is the L2norm. One important variant of this query type is kNN query, which returns k data points with the minimum distances. When taking the temporal dimension into account, the k NN query result may change over a period of time due to changes in locations of the query point and/or data points. 2017-05-01T07:00:00Z text application/pdf https://ink.library.smu.edu.sg/sis_research/3217 info:doi/10.1007/978-3-319-23519-6_1558-1 https://ink.library.smu.edu.sg/context/sis_research/article/4219/viewcontent/GIS_DynamicNN_EncyclopediaGIS_2016.pdf http://creativecommons.org/licenses/by-nc-nd/4.0/ Research Collection School Of Computing and Information Systems eng Institutional Knowledge at Singapore Management University Nearest neighbor monitoring Temporal nearest neighbor query Databases and Information Systems Geographic Information Sciences |
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Nearest neighbor monitoring Temporal nearest neighbor query Databases and Information Systems Geographic Information Sciences |
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Nearest neighbor monitoring Temporal nearest neighbor query Databases and Information Systems Geographic Information Sciences NUTANONG, Sarana ALI, Mohammed Eunus TANIN, Egemen MOURATIDIS, Kyriakos Dynamic Nearest Neighbor Queries in Euclidean Space |
| description |
Given a query point q and a set D of data points, a nearest neighbor (NN) query returns the data point p in D that minimizes the distance DIST(q,p), where the distance function DIST(,) is the L2norm. One important variant of this query type is kNN query, which returns k data points with the minimum distances. When taking the temporal dimension into account, the k NN query result may change over a period of time due to changes in locations of the query point and/or data points. |
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text |
| author |
NUTANONG, Sarana ALI, Mohammed Eunus TANIN, Egemen MOURATIDIS, Kyriakos |
| author_facet |
NUTANONG, Sarana ALI, Mohammed Eunus TANIN, Egemen MOURATIDIS, Kyriakos |
| author_sort |
NUTANONG, Sarana |
| title |
Dynamic Nearest Neighbor Queries in Euclidean Space |
| title_short |
Dynamic Nearest Neighbor Queries in Euclidean Space |
| title_full |
Dynamic Nearest Neighbor Queries in Euclidean Space |
| title_fullStr |
Dynamic Nearest Neighbor Queries in Euclidean Space |
| title_full_unstemmed |
Dynamic Nearest Neighbor Queries in Euclidean Space |
| title_sort |
dynamic nearest neighbor queries in euclidean space |
| publisher |
Institutional Knowledge at Singapore Management University |
| publishDate |
2017 |
| url |
https://ink.library.smu.edu.sg/sis_research/3217 https://ink.library.smu.edu.sg/context/sis_research/article/4219/viewcontent/GIS_DynamicNN_EncyclopediaGIS_2016.pdf |
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