Nearest neighbor and reverse nearest neighbor queries for moving objects

By Rimantas Benetis, Christian S. Jensen, Gytis Karciauskas, Simonas Šaltenis. Proceedings - International Database Engineering and Applications Symposium, 2002

295 citations recorded by the source corpus.

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

View Paper: https://www.semanticscholar.org/paper/b95f887eee7050f228d6d5813798af48b2950507

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Computer Vision and Pattern Recognition

1. Mohamed F. Mokbel
2. Walid G. Aref
3. Dimitris Papadias
4. Kyriakos Mouratidis
5. Xiaopeng Xiong
6. Marios Hadjieleftheriou
7. Xiaohui Yu
8. Nick Koudas

In Ocean Engineering

1. Mohamed F. Mokbel
2. Walid G. Aref
3. Dimitris Papadias
4. Kyriakos Mouratidis
5. Marios Hadjieleftheriou
6. Xiaohui Yu
7. Nick Koudas
8. Ken Q. Pu

In Signal Processing

1. Mohamed F. Mokbel
2. Walid G. Aref
3. Xiaopeng Xiong
4. Dimitris Papadias
5. Kyriakos Mouratidis
6. Marios Hadjieleftheriou
7. Nick Koudas
8. Xiaohui Yu

Citations in the corpus

• Monitoring k-nearest neighbor queries over moving objects (2005, methodology): “Other existing methods that work in two or higher dimensional spaces utilize the TPR-tree (time-parameterized R-tree)[15] or its variants as the supporting index structure [1] [19] [13] [7].”
• The TPR*-Tree: An Optimized Spatio-Temporal Access Method for Predictive Queries (2003, background): “Although spatio-temporal indexes are crucial for the efficient processing of conventional and novel query types ([TP02, BJKS02]), the existing structures are either purely theoretical, or based on traditional spatial access methods with limited provision for moving objects.”
• SEA-CNN: scalable processing of continuous k-nearest neighbor queries in spatio-temporal databases (2005, methodology): “For spatio-temporal databases, a direct extension of traditional techniques is to use branch and bound techniques for TPR-tree-like structures [1, 16].”
• Reverse kNN Search in Arbitrary Dimensionality (2004, background): “Other versions of the problem include (i) continuous RNN [ BJKS02 ], where P contains linearly moving objects with fixed velocities, and the goal is to retrieve all RNNs of q for a future interval; (ii) bichromatic RNN [SRAA01] where, given a set Q of queries, the goal is to find the objects p∈ P th”
• Nearest Neighbor Search on Moving Object Trajectories (2005, methodology): “[1] presented efficient solutions for NN and RNN (Reverse Nearest Neighbor) queries for moving objects.”
• SINA: scalable incremental processing of continuous queries in spatio-temporal databases (2004, background): “In general, spatio-temporal queries can be evaluated using a spatio-temporal access method [2].”
• On Trip Planning Queries in Spatial Databases (2005, background): “Most research has concentrated on traditional spatial quer ies and their variants, namely range queries [18], nearest neighbors [15, 19, 29], continu ous nearest neighbors [5, 37, 41], group nearest neighbors [26], reverse nearest neighbo rs [22], etc.”
• Aggregate nearest neighbor queries in spatial databases (2005, background): “The .rst NN algorithm for R-trees [Roussopoulos et al. 1995] searches the tree in a depth-.rst (DF) manner, recursively visiting the node with the mini­mum mindist from q.For example, in Figure 2, DF accesses the root, followed Fig.”