Continuous Nearest Neighbor Search

By Yufei Tao, Dimitris Papadias, Qiongmao Shen. Very Large Data Bases Conference, 2002

511 citations recorded by the source corpus.

Subfields: Computer Vision and Pattern Recognition, Ocean Engineering, Signal Processing

View Paper: https://www.semanticscholar.org/paper/b1457d9e32316f4cb5d0d46f821c257706449114

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In Computer Vision and Pattern Recognition

In Ocean Engineering

In Signal Processing

Citations in the corpus

Rendering Physically Correct Raindrops on Windshields for Robustness Verification of Camera-based Object Recognition (2018, methodology): “Utilizing additional smart heuristics (which may be looked-up in the original paper [31]) a huge portion of the”
On Trip Planning Queries in Spatial Databases (2005, background): “Most research has concentrated on traditional spatial queries and their variants, namely range queries [20], nearest neighbors [15, 21, 31], continuous nearest neighbors [5, 39, 43], group nearest neighbors [28], reverse nearest neighbors [24], etc.”
SEA-CNN: scalable processing of continuous k-nearest neighbor queries in spatio-temporal databases (2005, background): “[28] and [29] are restricted to the case of moving queries over stationary objects.”
Enabling Fast Lazy Learning for Data Streams (2011, background): “A number of k-NN query methods on data streams have been proposed with focus on various types of data, such as relational data, uncertain data, semi-structured data, spatial data, fuzzy data, and time series data [18], [16].”
SINA: scalable incremental processing of continuous queries in spatio-temporal databases (2004, methodology): “, see [2, 23, 25, 32]), scalability, incremental evaluation, mutability of both objects and queries, and client overhead are examples of challenges that either are overlooked wholly or partially by previous approaches.”
The new Casper: query processing for location services without compromising privacy (2006, methodology): “These works find the exact set of objects as a query answer based on the linear nearest neighbor search algorithm [Tao et al. 2002].”
Preventing Location-Based Identity Inference in Anonymous Spatial Queries (2007, methodology): “Then, four continuous NN (CNN) queries [29], one for each side of the ASR, retrieve the remaining candidates.”
Location-based spatial queries (2003, methodology): “Given a query moving with steady velocity, [BJKS02, TPS02 ] return all nearest neighbor results (up to a future timestamp), i.e., the output is a set of tuples , where Ri is the set of nearest neighbors during future interval Ti. For this situation (i.e., steady client velocity), the concept of t”
Trajectory Data Mining (2015, methodology): “KNN queries retrieve the top-K trajectories with the minimum aggregate distance to a few points (entitled the KNN point query [Chen et al. 2010; Tao et al. 2002; Tang et al. 2011]) or a speciﬁc trajectory (entitled the KNN trajectory query [Yi et al. 1998; Agrawal et al. As depicted in Figure 9(b),…”