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153
Nearest Neighbor and Reverse Nearest Neighbor Queries for Moving Objects
, 2001
"... With the proliferation of wireless communications and the rapid advances in technologies for tracking the positions of continuously moving objects, algorithms for efficiently answering queries about large numbers of moving objects increasingly are needed. One such query is the reverse nearest neighb ..."
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Cited by 120 (9 self)
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With the proliferation of wireless communications and the rapid advances in technologies for tracking the positions of continuously moving objects, algorithms for efficiently answering queries about large numbers of moving objects increasingly are needed. One such query is the reverse nearest neighbor (RNN) query that returns the objects that have a query object as their closest object. While algorithms have been proposed that compute RNN queries for nonmoving objects, there have been no proposals for answering RNN queries for continuously moving objects. Another such query is the nearest neighbor (NN) query, which has been studied extensively and in many contexts. Like the RNN query, the NN query has not been explored for moving query and data points. This paper proposes an algorithm for answering RNN queries for continuously moving points in the plane. As a part of the solution to this problem and as a separate contribution, an algorithm for answering NN queries for continuously moving points is also proposed. The results of performance experiments are reported.
Nearestneighbor searching and metric space dimensions
 In NearestNeighbor Methods for Learning and Vision: Theory and Practice
, 2006
"... Given a set S of n sites (points), and a distance measure d, the nearest neighbor searching problem is to build a data structure so that given a query point q, the site nearest to q can be found quickly. This paper gives a data structure for this problem; the data structure is built using the distan ..."
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Cited by 106 (0 self)
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Given a set S of n sites (points), and a distance measure d, the nearest neighbor searching problem is to build a data structure so that given a query point q, the site nearest to q can be found quickly. This paper gives a data structure for this problem; the data structure is built using the distance function as a “black box”. The structure is able to speed up nearest neighbor searching in a variety of settings, for example: points in lowdimensional or structured Euclidean space, strings under Hamming and edit distance, and bit vector data from an OCR application. The data structures are observed to need linear space, with a modest constant factor. The preprocessing time needed per site is observed to match the query time. The data structure can be viewed as an application of a “kdtree ” approach in the metric space setting, using Voronoi regions of a subset in place of axisaligned boxes. 1
Efficient Computation of Reverse Skyline Queries
, 2007
"... In this paper, for the first time, we introduce the concept of Reverse Skyline Queries. At first, we consider for a multidimensional data set P the problem of dynamic skyline queries according to a query point q. This kind of dynamic skyline corresponds to the skyline of a transformed data space whe ..."
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Cited by 59 (0 self)
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In this paper, for the first time, we introduce the concept of Reverse Skyline Queries. At first, we consider for a multidimensional data set P the problem of dynamic skyline queries according to a query point q. This kind of dynamic skyline corresponds to the skyline of a transformed data space where point q becomes the origin and all points of P are represented by their distance vector to q. The reverse skyline query returns the objects whose dynamic skyline contains the query object q. In order to compute the reverse skyline of an arbitrary query point, we first propose a Branch and Bound algorithm (called BBRS), which is an improved customization of the original BBS algorithm. Furthermore, we identify a super set of the reverse skyline that is used to bound the search space while computing the reverse skyline. To further reduce the computational cost of determining if a point belongs to the reverse skyline, we propose an enhanced algorithm (called RSSA) that is based on accurate precomputed approximations of the skylines. These approximations are used to identify whether a point belongs to the reverse skyline or not. Through extensive experiments with both realworld and synthetic datasets, we show that our algorithms can efficiently support reverse skyline queries. Our enhanced approach improves reversed skyline processing by up to an order of magnitude compared to the algorithm without the usage of precomputed approximations.
Reverse Nearest Neighbor Queries for Dynamic Databases
 In ACM SIGMOD Workshop on Research Issues in Data Mining and Knowledge Discovery
, 2000
"... In this paper we propose an algorithm for answering reverse nearest neighbor (RNN) queries, a problem formulated only recently. This class of queries is strongly related to that of nearest neighbor (NN) queries, although the two are not necessarily complementary. Unlike nearest neighbor queries, RNN ..."
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Cited by 53 (1 self)
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In this paper we propose an algorithm for answering reverse nearest neighbor (RNN) queries, a problem formulated only recently. This class of queries is strongly related to that of nearest neighbor (NN) queries, although the two are not necessarily complementary. Unlike nearest neighbor queries, RNN queries find the set of database points that have the query point as the nearest neighbor. There is no other proposal we are aware of, that provides an algorithmic approach to answer RNN queries. The earlier approach for RNN queries ([KM99]) is based on the precomputation of neighborhood information that is organized in terms of auxiliary data structures. It can be argued that the precomputation of the RNN information for all points in the database can be too restrictive. In the case of dynamic databases, insert and update operations are expensive and can lead to modifications of large parts of the auxiliary data structures. Also, answers to RNN queries for a set of data points depend on t...
Discovery of Influence Sets in Frequently Updated
 In VLDB
, 2001
"... An increasing number of organizations are currently working on ways to express and provide location information to services and applications. A location aware system knows the position of each component, and it is able to track devices through changes due to movement. ..."
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Cited by 51 (0 self)
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An increasing number of organizations are currently working on ways to express and provide location information to services and applications. A location aware system knows the position of each component, and it is able to track devices through changes due to movement.
Reverse Nearest Neighbor Aggregates Over Data Streams
, 2001
"... Reverse Nearest Neighbor (RNN) queries have been studied for finite, stored data sets and are of interest for decision support. ..."
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Cited by 46 (2 self)
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Reverse Nearest Neighbor (RNN) queries have been studied for finite, stored data sets and are of interest for decision support.
High Dimensional Reverse Nearest Neighbor Queries
 In CIKM
, 2003
"... Reverse Nearest Neighbor (RNN) queries are of particular interest in a wide range of applications such as decision support systems, profile based marketing, data streaming, document databases, and bioinformatics. The earlier approaches to solve this problem mostly deal with two dimensional data. How ..."
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Cited by 46 (0 self)
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Reverse Nearest Neighbor (RNN) queries are of particular interest in a wide range of applications such as decision support systems, profile based marketing, data streaming, document databases, and bioinformatics. The earlier approaches to solve this problem mostly deal with two dimensional data. However most of the above applications inherently involve high dimensions and high dimensional RNN problem is still unexplored. In this paper, we propose an approximate solution to answer RNN queries in high dimensions. Our approach kNN and RNN. It works in two phases. In the first phase the kNN of a query point is found and in the next phase they are further analyzed using a novel type of query Boolean Range Query (BRQ). Experimental results show that BRQ is much more e#cient than both NN and range queries, and can be e#ectively used to answer RNN queries. Performance is further improved by running multiple BRQ simultaneously. The proposed approach can also be used to answer other variants of RNN queries such as RNN of order k, bichromatic RNN, and Matching Query which has many applications of its own. Our technique can e#ciently answer NN, RNN, and its variants with approximately same number of I/O as running a NN query.
Constrained Nearest Neighbor Queries
 in SSTD
, 2001
"... In this paper we introduce the notion of constrained nearest neighbor queries (CNN) and propose a series of methods to answer them. This class of queries can be thought of as nearest neighbor queries with range constraints. Although both nearest neighbor and range queries have been analyzed exten ..."
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Cited by 44 (4 self)
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In this paper we introduce the notion of constrained nearest neighbor queries (CNN) and propose a series of methods to answer them. This class of queries can be thought of as nearest neighbor queries with range constraints. Although both nearest neighbor and range queries have been analyzed extensively in previous literature, the implications of constrained nearest neighbor queries have not been discussed. Due to their versatility, CNN queries are suitable to a wide range of applications from GIS systems to reverse nearest neighbor queries and multimedia applications.
PeertoPeer Spatial Queries in Sensor Networks
 IN PROCEEDINGS OF THE IEEE INTERNATIONAL CONFERENCE ON PEERTOPEER COMPUTING
, 2003
"... Sensor networks, that consist of potentially several thousands of nodes each with sensing (heat, sound, light, magnetism, etc.) and wireless communication capabilities, provide great opportunities for monitoring spatial information about a region of interest. Although spatial query execution has bee ..."
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Cited by 43 (2 self)
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Sensor networks, that consist of potentially several thousands of nodes each with sensing (heat, sound, light, magnetism, etc.) and wireless communication capabilities, provide great opportunities for monitoring spatial information about a region of interest. Although spatial query execution has been studied extensively in the context of database systems (e.g., indexing technologies), these solutions are not directly applicable in the context of sensor networks due to the decentralized nature of the sensor networks and the limited computational power and energy scarcity of individual sensor nodes. In this paper,
The optimallocation query
 In SSTD
, 2005
"... Abstract. We propose and solve the optimallocation query in spatial databases. Given a set S of sites, a set O of weighted objects, and a spatial region Q, the optimallocation query returns a location in Q with maximum influence. Here the influence of a location l is the total weight of its RNNs, ..."
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Cited by 40 (2 self)
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Abstract. We propose and solve the optimallocation query in spatial databases. Given a set S of sites, a set O of weighted objects, and a spatial region Q, the optimallocation query returns a location in Q with maximum influence. Here the influence of a location l is the total weight of its RNNs, i.e. the total weight of objects in O that are closer to l than to any site in S. This new query has practical applications, but is very challenging to solve. Existing work on computing RNNs assumes a single query location, and thus cannot be used to compute optimal locations. The reason is that there are infinite candidate locations in Q. If we check a finite set of candidate locations, the result can be inaccurate, i.e. the revealed location may not have maximum influence. This paper proposes three methods that accurately compute optimal locations. The first method uses a standard R*tree. To compute an optimal location, the method retrieves certain objects from the R*tree and sends them as a stream to a planesweep algorithm, which uses a new data structure called the aSBtree to ensure query efficiency. The second method is based on a new index structure called the OLtree, which novelly extends the kdBtree to store segmented rectangular records. The OLtree is only of theoretical usage for it is not space efficient. The most practical approach is based on a new index structure called the Virtual OLtree. These methods are theoretically and experimentally evaluated. 1