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 L2 norm. 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 kNN query result may change over a period of time due to changes in locations of the query point and/or data points. Formally, the k-nearest neighbor (kNN) query is defined as follows.

[1]  Yufei Tao,et al.  Continuous Nearest Neighbor Search , 2002, VLDB.

[2]  Kyriakos Mouratidis,et al.  Conceptual partitioning: an efficient method for continuous nearest neighbor monitoring , 2005, SIGMOD '05.

[3]  Kyriakos Mouratidis,et al.  Continuous nearest neighbor monitoring in road networks , 2006, VLDB.

[4]  Lars Kulik,et al.  A Formal Model of Obfuscation and Negotiation for Location Privacy , 2005, Pervasive.

[5]  Jimeng Sun,et al.  The TPR*-Tree: An Optimized Spatio-Temporal Access Method for Predictive Queries , 2003, VLDB.

[6]  Walid G. Aref,et al.  SEA-CNN: scalable processing of continuous k-nearest neighbor queries in spatio-temporal databases , 2005, 21st International Conference on Data Engineering (ICDE'05).

[7]  Chi-Yin Chow,et al.  Enabling Private Continuous Queries for Revealed User Locations , 2007, SSTD.

[8]  Christian S. Jensen,et al.  Indexing of moving objects for location-based services , 2002, Proceedings 18th International Conference on Data Engineering.

[9]  Kyriakos Mouratidis,et al.  A threshold-based algorithm for continuous monitoring of k nearest neighbors , 2005, IEEE Transactions on Knowledge and Data Engineering.

[10]  Yufei Tao,et al.  Time-parameterized queries in spatio-temporal databases , 2002, SIGMOD '02.

[11]  Nick Roussopoulos,et al.  K-Nearest Neighbor Search for Moving Query Point , 2001, SSTD.

[12]  Wang Yi,et al.  Processing Moving kNN Queries Using Influential Neighbor Sets , 2014, Proc. VLDB Endow..

[13]  Hanan Samet,et al.  Maintenance of K-nn and spatial join queries on continuously moving points , 2006, TODS.

[14]  Gang Chen,et al.  Continuous nearest-neighbor search in the presence of obstacles , 2011, TODS.

[15]  Xiaohui Yu,et al.  Monitoring k-nearest neighbor queries over moving objects , 2005, 21st International Conference on Data Engineering (ICDE'05).

[16]  Sunil Prabhakar,et al.  Querying imprecise data in moving object environments , 2003, Proceedings 19th International Conference on Data Engineering (Cat. No.03CH37405).

[17]  Atsuyuki Okabe,et al.  Spatial Tessellations: Concepts and Applications of Voronoi Diagrams , 1992, Wiley Series in Probability and Mathematical Statistics.

[18]  Ling Liu,et al.  MobiEyes: Distributed Processing of Continuously Moving Queries on Moving Objects in a Mobile System , 2004, EDBT.

[19]  Kyriakos Mouratidis.  Continuous Monitoring of Spatial Queries , 2009, Encyclopedia of Database Systems.

[20]  Hans-Peter Kriegel,et al.  Probabilistic Nearest Neighbor Queries on Uncertain Moving Object Trajectories , 2013, Proc. VLDB Endow..

[21]  Yufei Tao,et al.  Location-based spatial queries , 2003, SIGMOD '03.

[22]  Marco Gruteser,et al.  USENIX Association , 1992 .

[23]  Jianliang Xu,et al.  A generic framework for monitoring continuous spatial queries over moving objects , 2005, SIGMOD '05.

[24]  Lars Kulik,et al.  Analysis and evaluation of V*-kNN: an efficient algorithm for moving kNN queries , 2010, The VLDB Journal.

[25]  Franz Aurenhammer,et al.  Voronoi diagrams—a survey of a fundamental geometric data structure , 1991, CSUR.