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883
Similarity search in high dimensions via hashing
, 1999
"... The nearest or nearneighbor query problems arise in a large variety of database applications, usually in the context of similarity searching. Of late, there has been increasing interest in building search/index structures for performing similarity search over highdimensional data, e.g., image dat ..."
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Cited by 415 (12 self)
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The nearest or nearneighbor query problems arise in a large variety of database applications, usually in the context of similarity searching. Of late, there has been increasing interest in building search/index structures for performing similarity search over highdimensional data, e.g., image databases, document collections, timeseries databases, and genome databases. Unfortunately, all known techniques for solving this problem fall prey to the \curse of dimensionality. " That is, the data structures scale poorly with data dimensionality; in fact, if the number of dimensions exceeds 10 to 20, searching in kd trees and related structures involves the inspection of a large fraction of the database, thereby doing no better than bruteforce linear search. It has been suggested that since the selection of features and the choice of a distance metric in typical applications is rather heuristic, determining an approximate nearest neighbor should su ce for most practical purposes. In this paper, we examine a novel scheme for approximate similarity search based on hashing. The basic idea is to hash the points
Anonymous usage of locationbased services through spatial and temporal cloaking
, 2003
"... and dissemination of a user's or object's position [1, 2].There is an immense interest in exploiting this positional data through locationbased services (LBS) [3, 4, 5, 6].For instance, LBSs could tailor their functionality to the user's current location, or vehicle movement data wouldimprove traff ..."
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Cited by 348 (14 self)
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and dissemination of a user's or object's position [1, 2].There is an immense interest in exploiting this positional data through locationbased services (LBS) [3, 4, 5, 6].For instance, LBSs could tailor their functionality to the user's current location, or vehicle movement data wouldimprove traffic forecasting and road planning.
Treemaps: a spacefilling approach to the visualization of hierarchical information structures
 Proc. 2nd International Visualization Conference 1991. IEEE
, 1991
"... This paper describes a novel methodfor the visualization of hierarchically structured information. The TreeMap visualization technique makes 100 % use of the available display space, mapping the full hierarchy onto a rectangular region in a spacefilling manner. This efficient use of space allows v ..."
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Cited by 346 (24 self)
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This paper describes a novel methodfor the visualization of hierarchically structured information. The TreeMap visualization technique makes 100 % use of the available display space, mapping the full hierarchy onto a rectangular region in a spacefilling manner. This efficient use of space allows very large hierarchies to be displayed in their entirety and facilitates the presentation of semantic information. 1
Constraint Query Languages
, 1992
"... We investigate the relationship between programming with constraints and database query languages. We show that efficient, declarative database programming can be combined with efficient constraint solving. The key intuition is that the generalization of a ground fact, or tuple, is a conjunction ..."
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Cited by 338 (35 self)
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We investigate the relationship between programming with constraints and database query languages. We show that efficient, declarative database programming can be combined with efficient constraint solving. The key intuition is that the generalization of a ground fact, or tuple, is a conjunction of constraints over a small number of variables. We describe the basic Constraint Query Language design principles and illustrate them with four classes of constraints: real polynomial inequalities, dense linear order inequalities, equalities over an infinite domain, and boolean equalities. For the analysis, we use quantifier elimination techniques from logic and the concept of data complexity from database theory. This framework is applicable to managing spatial data and can be combined with existing multidimensional searching algorithms and data structures.
Indexing the Positions of Continuously Moving Objects
, 2000
"... The coming years will witness dramatic advances in wireless communications as well as positioning technologies. As a result, tracking the changing positions of objects capable of continuous movement is becoming increasingly feasible and necessary. The present paper proposes a novel, R # tree base ..."
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Cited by 317 (18 self)
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The coming years will witness dramatic advances in wireless communications as well as positioning technologies. As a result, tracking the changing positions of objects capable of continuous movement is becoming increasingly feasible and necessary. The present paper proposes a novel, R # tree based indexing technique that supports the efficient querying of the current and projected future positions of such moving objects. The technique is capable of indexing objects moving in one, two, and threedimensional space. Update algorithms enable the index to accommodate a dynamic data set, where objects may appear and disappear, and where changes occur in the anticipated positions of existing objects. A comprehensive performance study is reported.
Similarity Indexing with the SStree
 In Proceedings of the 12th International Conference on Data Engineering
, 1996
"... jain0ece.ucsd.edu ..."
Distance Browsing in Spatial Databases
, 1999
"... Two different techniques of browsing through a collection of spatial objects stored in an Rtree spatial data structure on the basis of their distances from an arbitrary spatial query object are compared. The conventional approach is one that makes use of a knearest neighbor algorithm where k is kn ..."
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Cited by 291 (19 self)
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Two different techniques of browsing through a collection of spatial objects stored in an Rtree spatial data structure on the basis of their distances from an arbitrary spatial query object are compared. The conventional approach is one that makes use of a knearest neighbor algorithm where k is known prior to the invocation of the algorithm. Thus if m#kneighbors are needed, the knearest neighbor algorithm needs to be reinvoked for m neighbors, thereby possibly performing some redundant computations. The second approach is incremental in the sense that having obtained the k nearest neighbors, the k +1 st neighbor can be obtained without having to calculate the k +1nearest neighbors from scratch. The incremental approach finds use when processing complex queries where one of the conditions involves spatial proximity (e.g., the nearest city to Chicago with population greater than a million), in which case a query engine can make use of a pipelined strategy. A general incremental nearest neighbor algorithm is presented that is applicable to a large class of hierarchical spatial data structures. This algorithm is adapted to the Rtree and its performance is compared to an existing knearest neighbor algorithm for Rtrees [45]. Experiments show that the incremental nearest neighbor algorithm significantly outperforms the knearest neighbor algorithm for distance browsing queries in a spatial database that uses the Rtree as a spatial index. Moreover, the incremental nearest neighbor algorithm also usually outperforms the knearest neighbor algorithm when applied to the knearest neighbor problem for the Rtree, although the improvement is not nearly as large as for distance browsing queries. In fact, we prove informally that, at any step in its execution, the incremental...
Spatial Data Structures
, 1995
"... An overview is presented of the use of spatial data structures in spatial databases. The focus is on hierarchical data structures, including a number of variants of quadtrees, which sort the data with respect to the space occupied by it. Suchtechniques are known as spatial indexing methods. Hierarch ..."
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Cited by 287 (13 self)
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An overview is presented of the use of spatial data structures in spatial databases. The focus is on hierarchical data structures, including a number of variants of quadtrees, which sort the data with respect to the space occupied by it. Suchtechniques are known as spatial indexing methods. Hierarchical data structures are based on the principle of recursive decomposition. They are attractive because they are compact and depending on the nature of the data they save space as well as time and also facilitate operations such as search. Examples are given of the use of these data structures in the representation of different data types such as regions, points, rectangles, lines, and volumes.
Octrees for faster isosurface generation
 IEEE TRANSACTIONS ON MEDICAL IMAGING
, 2000
"... The large size of many volume data sets often prevents visualization algorithms from providing interactive rendering. The use of hierarchical data structures can ameliorate this problem by storing summary information to prevent useless exploration of regions of little or no current interest within ..."
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Cited by 274 (3 self)
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The large size of many volume data sets often prevents visualization algorithms from providing interactive rendering. The use of hierarchical data structures can ameliorate this problem by storing summary information to prevent useless exploration of regions of little or no current interest within the volume. This paper discusses research into the use of the octree hierarchical data structure when the regions of current interest can vary during the application, and are not known a priori. Octrees are well suited to the sixsided cell structure of many volumes. A new spaceefficient design is introduced for octree representations of volumes whose resolutions are not conveniently a power of two; octrees following this design are called branchonneed octrees (BONOs). Also, a caching method is described that essentially passes information between octree neighbors whose visitation times may be quite different, then discards it when its useful life is over. Using the application of octrees to isosurface generation as a focus, space and time comparisons for octreebased versus more traditional "marching" methods are presented.
Modeling and Querying Moving Objects
, 1997
"... In this paper we propose a data model for representing moving objects in database systems. It is called the Moving Objects SpatioTemporal (MOST) data model. We also propose Future Temporal Logic (FTL) as the query language for the MOST model, and devise an algorithm for processing FTL queries in MO ..."
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Cited by 254 (18 self)
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In this paper we propose a data model for representing moving objects in database systems. It is called the Moving Objects SpatioTemporal (MOST) data model. We also propose Future Temporal Logic (FTL) as the query language for the MOST model, and devise an algorithm for processing FTL queries in MOST. 1. Introduction Existing database management systems (DBMS's) are not well equipped to handle continuously changing data, such as the position of moving objects. The reason for this is that in databases, data is assumed to be constant unless it is explicitly modified. For example, if the salary field is 30K, then this salary is assumed to hold (i.e. 30K is returned in response to queries) until explicitly updated. Thus, in order to represent moving objects (e.g. cars) in a database, and answer queries about their position (e.g., How far is the car with license plate RWW860 from the nearest hospital?) the car's position has to be continuously updated. This is unsatisfactory since either ...