Zhexuan Song and Nick Roussopoulos. K-Nearest Neighbor Search for Moving Query Point. In SSTD, 2001.  Home/Search   Document Not in Database   Summary   Related Articles   Check

....of location aware applications like intelligent traffic management, mobile communications, sensor based surveillance systems, etc. Typically the location of a moving object is represented as a function of time and the database stores the function parameters [2] 1] 17] 9] 22] 21] 16] [24], 15] 27] 23] 10] This results into a tractable update load. The system is updated only when an object changes any of its moving parameters (e.g. speed, direction, etc) The alternative of storing the object s continuously changing location is practically infeasible since it would ....

....will be in area A, ten minutes from now [10] This work was partially supported by NSF grants IIS 9907477, EIA9983445, IIS 0220148 and Career Award 0133825. 2] 17] 9] 22] 21] 20] nearest neighbor queries: Find the closest object(s) to a given location within the next five minutes [24], etc. The answer to such queries is based on the knowledge about the object movements at the time the query is issued [25] 26] In this paper we present a framework for answering density based queries in moving object databases. An area is dense if the number of moving objects it contains is ....

Z. Song and N. Roussopoulos. K-nearest neighbor search for moving query point. In Proc. of the SSTD, pages 79--96, 2001.

....years due to the emergence and importance of location aware applications like intelligent trac management, mobile communications, sensor based surveillance systems, etc. Typically, the location of a moving object is represented as a function of time and the database stores the function parameters [2, 1, 17, 9, 22, 21, 16, 24, 15, 27, 23, 10]. This results into a tractable update load. The system is updated only when an object changes any of its moving parameters (e.g. speed, direction, etc) The alternative of storing the object s continuously changing location is practically infeasible since it would correspond to one update per ....

....interesting queries about the locations of the moving objects in the future. Examples include range queries: nd which objects will be in area A, 10 minutes from now [10, 2, 17, 9, 22, 21, 20] nearest neighbor queries: nd the closest object(s) to a given location within the next 5 minutes [24], etc. The answer to such queries is based on the knowledge about the object movements at the time the query is issued [25, 26] In this paper we present a framework for answering density based queries in moving object databases. An area is dense if the number of moving objects it contains is ....

Z. Song and N. Roussopoulos. K-nearest neighbor search for moving query point. In Proc. of the SSTD, pages 79-96, 2001.

....branch and bound algorithms on R trees [BKSS90] for static objects) and TPR trees [SJLL00] for dynamic objects) In the worst case, these algorithms perform O(N B) I Os (i.e. the complexity of a simple sequential scan) where N is the number of objects in the dataset. All the other attempts [SR01, TPS02, BJKS02] addressing variations of the problem, incur the same complexity. On the other hand, there is a significant amount of theoretical results on conventional spatial queries for static and moving objects. For orthogonal range search on static points, Kanth and Singh [KS99] prove that the best ....

Song, Z., Roussopoulos, N. K-Nearest Neighbor Search for Moving Query Point. SSTD, 2001.

....formulae for the expected size of the validity regions. Section 6 experimentally evaluates the proposed techniques and Section 7 concludes the paper with a discussion for future work. 2. RELATED WORK The first spatial query processing techniques for mobile computing were proposed in [ZL01] and [SR01], both dealing with moving nearest neighbor queries on static data. Zheng and Lee [ZL01] pre compute and store in an R tree the Voronoi diagram of the dataset. When a nearest neighbor query arrives at the server, the Voronoi diagram is used to efficiently compute the nearest neighbor (e.g. point ....

....a low value may cause false misses. Furthermore, the method only deals with single nearest neighbors, as the retrieval of k neighbors would require order k Voronoi diagrams (for all possible values of k) which are complicated and incur large space overhead. The technique of Song and Roussopoulos [SR01] does not assume Voronoi diagrams and can be used for any number of neighbors. When a k nearest neighbor query q arrives, the server computes and returns to the client a number m k of neighbors (using existing algorithms such as [RKV95, HS99] Let dist(k) and dist(m) be the distances of the ....

[Article contains additional citation context not shown here]

Song, Z., Roussopoulos, N. K-Nearest Neighbor Search for Moving Query Point. SSTD, 2001.

....the change C of the result after T . An example of TPNN is to report (i) the nearest station s, ii) when s will cease to be the nearest (given the user s moving direction and speed) and (iii) the new nearest station after the expiry of s. The concept of TP is extended to the continuous query in [55, 37, 60, 59], which is another general concept applicable to all traditional queries, and aims at continuously tracking the result changes until certain conditions are satisfied. A continuous WQ, for instance, may return the aircrafts within 10 miles from flight UA183 now, and continuously update this ....

Z. Song and N. Roussopoulos. K-nearest neighbor search for moving query point. SSTD, 2001.

....neighbor queries for non moving points [12, 23, 25] but we are not aware of any algorithms for moving points. While much work has been conducted on algorithms for nearest neighbor queries, we are aware of only one work that has explored algorithms for a moving query point and static data points [22] and of no solutions for moving data and query points in two or higher dimensional space. This paper proposes an algorithm that efficiently computes RNN queries for a query point during a specified time interval assuming the query and data points are continuously moving in the plane. As a ....

....for each time point during that time interval (cf. the problem formulation in Section 2.1) Moreover, this solution cannot be straightforwardly extended to the twodimensional case, where the trajectories of the points become lines in three dimensional space. Most recently, Song and Roussopoulos [22] have proposed a solution for finding the k nearest neighbors for a moving query point. However, the data points are assumed to be static. In addition, in contrast to our approach, time is not assumed to be continuous a periodical sampling technique is used instead. When computing the result set ....

Z. Song and N. Roussopoulos. K-Nearest Neighbor Search for Moving Query Point. In Proceedings of the 7th International Symposium on Spatial and Temporal Databases, pp. 79--96, 2001.

....the first ones to identify the significance of CNN in spatiotemporal database systems. In [SWCD97] they describe modeling methods and query languages for the expression of such queries, but do not discuss access or processing methods. The first algorithm for CNN query processing, proposed in [SR01], employs sampling to compute the result. In particular, several point NN queries (using an Rtree on the point set P) are repeatedly performed at predefined sample points of the query line, using the results at previous sample points to obtain tight search bounds. This approach suffers from the ....

Song, Z., Roussopoulos, N. K-Nearest Neighbor Search for Moving Query Point. SSTD, 2001.

....whereas in our work it is the result of interactions among all moving objects in the system. Moreover, Pfoser et al. focus on querying the past rather than the future, which is the focus of our work. Nearest neighbor queries have been of significant interest recently [16, 2, 6] Song et al. [20] propose several different algorithms to answer k nearest neighbors query (kNN) in the context of moving objects. Those algorithms can be combined into one algorithm to give a better result. The answer they are searching for, however, is static information such as nearest hospitals or restaurants. ....

Z. Song and N. Roussopoulos. K-Nearest Neighbor Search for Moving Query Point. In Int. Symp. on Spatial and Temporal Databases, 2001.

....5 we present some results from the experimental evaluation of our techniques, supporting their e ectiveness. In Sect. 6 we conclude, presenting directions of future research. 2 Related Work The problem of indexing and querying mobile objects in database systems has been studied in the literature [25, 8, 19, 14, 15, 18, 24]. Most of this work uses a multidimensional index structure (R Tree and its family [5, 2, 22] Quadtree [21] or hB tree [10] For temporal objects special indexing techniques have been proposed such as multi version index structures, e.g. Time Split B tree (TSBtree) 11] multi version B tree ....

....value changes explicitly with an update. Recent work has explored indexing for dynamic properties (which may change without explicit update) Research has focused on indexing and query processing over mobile data [25, 8, 19, 14, 15] Most of this work deals with spatio temporal range queries. [24] deals with nearest neighbor queries. Our work in [14, 15] classi es selection queries, including spatio temporal range and nearest neighbor queries on both temporal and spatial dimensions. Algorithms for these types of queries are presented using Native Space Indexing (NSI) in which indexing is ....

[Article contains additional citation context not shown here]

Z. Song and N. Roussopoulos. K-nearest neighbor search for moving query point. In SSTD, 2001.

....neighbor queries for non moving points [11, 22, 24] but we are not aware of any algorithms for moving points. While much work has been conducted on algorithms for nearest neighbor queries, we are aware of only one work that has explored algorithms for a moving query point and static data points [21] and of no solutions for moving data and query points in two or higher dimensional space. This paper proposes an algorithm that efficiently computes RNN queries for a query point during a specified time interval assuming the query and data points are continuously moving in the plane. As a solution ....

....for each time point during that time interval (cf. the problem formulation in Section 2.1) Moreover, this solution cannot be straightforwardly extended to the two dimensional case, where the trajectories of the points become lines in three dimensional space. Most recently, Song and Roussopoulos [21] have proposed a solution for finding the nearest neighbors for a moving query point. However, the data points are assumed to be static. In addition, in contrast to our approach, time is not assumed to be continuous periodical sampling technique is used instead. The time period is divided ....

Z. Song and N. Roussopoulos. K-Nearest Neighbor Search for Moving Query Point. In Proceedings of the 7th International Symposium on Spatial and Temporal Databases, pp. 79--96, 2001.

....neighbor queries for non moving points [12, 23, 25] but we are not aware of any algorithms for moving points. While much work has been conducted on algorithms for nearest neighbor queries, we are aware of only one work that has explored algorithms for a moving query point and static data points [22] and of no solutions for moving data and query points in two or higher dimensional space. This paper proposes an algorithm that efficiently computes RNN queries for a query point during a specified time interval assuming the query and data points are continuously moving in the plane. As a solution ....

....as points move, its topological structure changes only when certain discrete events occur. The authors show a nontrivial upper bound of the number of such events. They also provide an algorithm to maintain such continuously changing Voronoi diagrams. Most recently, Song and Roussopoulos [22] have proposed a solution for finding the nearest neighbors for a moving query point. However, the data points are assumed to be static. In addition, in contrast to our approach, time is not assumed to be continuous periodical sampling technique is used instead. The time period is divided by ....

Z. Song and N. Roussopoulos. K-Nearest Neighbor Search for Moving Query Point. In Proceedings of the 7th International Symposium on Spatial and Temporal Databases, pp. 79--96, 2001.

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Zhexuan Song and Nick Roussopoulos. K-Nearest Neighbor Search for Moving Query Point. In SSTD, 2001.

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Z. Song and N. Roussopoulos. K-Nearest Neighbor Search for Moving Query Point. In SSTD, 2001.

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Z. Song and N. Roussopoulos. K-Nearest Neighbor Search for Moving Query Point. In Proceedings of the 7th International Symposium on Spatial and Temporal Databases, pp. 79--96, 2001.

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Z. Song and N. Roussopoulos. K-Nearest Neighbor Search for Moving Query Point. In SSTD, 2001.

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Z. Song and N. Roussopoulos. K-Nearest Neighbor Search for Moving Query Point. In SSTD, 2001.

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Z. Song and N. Roussopoulos. k-nearest neighbor search for moving query point. In Proc. of Sym. on Spatial and Temporal Databases, pages 79--96, 2001.

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Z. Song and N. Roussopoulos. K-Nearest Neighbor Search for Moving Query Point. In Proceedings of the 7th Intern. Symp. SSTD, pages 79--96, Redondo Beach, CA, July 2001.

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Z. Song and N. Roussopoulos. K-Nearest Neighbor Search for Moving Query Point. In Proc. SSTD, pp. 79--96, 2001.

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Z. Song and N. Roussopoulos. K-Nearest Neighbor Search for Moving Query Point. In SSTD, 2001.

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Z. Song and N. Roussopoulos, K-Nearest Neighbor Search for Moving Query Point. Proc. of the 7th Intl. Symp. on Spatial and Temporal Databases (SSTD), 2001, 79-96.

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Z.Song and N.Roussopoulos. K-nearest neighbor search for moving query point. In In Proc. SSTD 2001.

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Song, Z., Roussopoulos, N. K-Nearest Neighbor Search for Moving Query Point. SSTD, 2001.

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