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Multidimensional Access Methods
, 1998
"... Search operations in databases require special support at the physical level. This is true for conventional databases as well as spatial databases, where typical search operations include the point query (find all objects that contain a given search point) and the region query (find all objects that ..."
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Cited by 665 (3 self)
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Search operations in databases require special support at the physical level. This is true for conventional databases as well as spatial databases, where typical search operations include the point query (find all objects that contain a given search point) and the region query (find all objects that overlap a given search region).
Image retrieval: Current techniques, promising directions and open issues
 Journal of Visual Communication and Image Representation
, 1999
"... This paper provides a comprehensive survey of the technical achievements in the research area of image retrieval, especially contentbased image retrieval, an area that has been so active and prosperous in the past few years. The survey includes 100+ papers covering the research aspects of image fea ..."
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Cited by 464 (14 self)
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This paper provides a comprehensive survey of the technical achievements in the research area of image retrieval, especially contentbased image retrieval, an area that has been so active and prosperous in the past few years. The survey includes 100+ papers covering the research aspects of image feature representation and extraction, multidimensional indexing, and system design, three of the fundamental bases of contentbased image retrieval. Furthermore, based on the stateoftheart technology available now and the demand from realworld applications, open research issues are identified and future promising research directions are suggested. C ○ 1999 Academic Press 1.
Geometric Range Searching and Its Relatives
 CONTEMPORARY MATHEMATICS
"... ... process a set S of points in so that the points of S lying inside a query R region can be reported or counted quickly. Wesurvey the known techniques and data structures for range searching and describe their application to other related searching problems. ..."
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Cited by 267 (43 self)
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... process a set S of points in so that the points of S lying inside a query R region can be reported or counted quickly. Wesurvey the known techniques and data structures for range searching and describe their application to other related searching problems.
On Packing Rtrees
 In ACM CIKM
, 1993
"... – main idea; file structure – algorithms: insertion/split – deletion – search: range, nn, spatial joins – performance analysis – variations (packed; hilbert;...) 15721 Copyright: C. Faloutsos (2001) 2 Problem • Given a collection of geometric objects (points, lines, polygons,...) • organize them on ..."
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Cited by 247 (16 self)
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– main idea; file structure – algorithms: insertion/split – deletion – search: range, nn, spatial joins – performance analysis – variations (packed; hilbert;...) 15721 Copyright: C. Faloutsos (2001) 2 Problem • Given a collection of geometric objects (points, lines, polygons,...) • organize them on disk, to answer spatial queries (range, nn, etc) 15721 Copyright: C. Faloutsos (2001) 3 1 (Who cares?)
The TVtree  an index structure for highdimensional data
 VLDB Journal
, 1994
"... We propose a file structure to index highdimensionality data, typically, points in some feature space. The idea is to use only a few of the features, utilizing additional features whenever the additional discriminatory power is absolutely necessary. We present in detail the design of our tree struc ..."
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Cited by 211 (7 self)
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We propose a file structure to index highdimensionality data, typically, points in some feature space. The idea is to use only a few of the features, utilizing additional features whenever the additional discriminatory power is absolutely necessary. We present in detail the design of our tree structure and the associated algorithms that handle such `varying length' feature vectors. Finally we report simulation results, comparing the proposed structure with the R tree, which is one of the most successful methods for lowdimensionality spaces. The results illustrate the superiority of our method, with up to 80% savings in disk accesses. Type of Contribution: New Index Structure, for highdimensionality feature spaces. Algorithms and performance measurements. Keywords: Spatial Index, Similarity Retrieval, Query by Content 1 Introduction Many applications require enhanced indexing, capable of performing similarity searching on several, nontraditional (`exotic') data types. The targ...
An Introduction to Spatial Database Systems
 THE VLDB JOURNAL
, 1994
"... We propose a definition of a spatial database system as a database system that offers spatial data types in its data model and query language, and supports ..."
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Cited by 208 (9 self)
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We propose a definition of a spatial database system as a database system that offers spatial data types in its data model and query language, and supports
Hilbert Rtree: An improved Rtree using fractals
, 1994
"... We propose a new Rtree structure that outperforms all the older ones. The heart of the idea is to facilitate the deferred splitting approach in Rtrees. This is done by proposing an ordering on the Rtree nodes. This ordering has to be 'good', in the sense that it should group 'simil ..."
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Cited by 208 (11 self)
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We propose a new Rtree structure that outperforms all the older ones. The heart of the idea is to facilitate the deferred splitting approach in Rtrees. This is done by proposing an ordering on the Rtree nodes. This ordering has to be 'good', in the sense that it should group 'similar' data rectangles together, to minimize the area and perimeter of the resulting minimum bounding rectangles (MBRs). Following [19] we have chosen the socalled '2Dc' method, which sorts rectangles according to the Hilbert value of the center of the rectangles. Given the ordering, every node has a welldefined set of sibling nodes; thus, we can use deferred splitting. By adjusting the split policy, the Hilbert Rtree can achieve as high utilization as desired. To the contrary, the R tree has no control over the space utilization, typically achieving up to 70%. We designed the manipulation algorithms in detail, and we did a full implementation of the Hilbert Rtree. Our experiments show that the '2to...
An asymptotically optimal multiversion Btree
, 1996
"... In a variety of applications, we need to keep track of the development of a data set over time. For maintaining and querying these multiversion data efficiently, external storage structures are an absolute necessity. We propose a multiversion Btree that supports insertions and deletions of data ite ..."
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Cited by 180 (9 self)
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In a variety of applications, we need to keep track of the development of a data set over time. For maintaining and querying these multiversion data efficiently, external storage structures are an absolute necessity. We propose a multiversion Btree that supports insertions and deletions of data items at the current version and range queries and exact match queries for any version, current or past. Our multiversion Btree is asymptotically optimal in the sense that the time and space bounds are asymptotically the same as those of the (singleversion) Btree in the worst case. The technique we present for transforming a (singleversion) Btree into a multiversion Btree is quite general: it applies to a number of hierarchical external access structures with certain properties directly, and it can be modified for others.
The Hybrid Tree: An Index Structure for High Dimensional Feature Spaces
 In Proceedings of ICDE’99
, 1999
"... Feature based similarity search is emerging as an important search paradigm in database systems. The technique used is to map the data items as points into a high dimensional feature space which is indexed using a multidimensional data structure. Similarity search then corresponds to a range search ..."
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Cited by 113 (12 self)
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Feature based similarity search is emerging as an important search paradigm in database systems. The technique used is to map the data items as points into a high dimensional feature space which is indexed using a multidimensional data structure. Similarity search then corresponds to a range search over the data structure. Although several data structures have been proposed for feature indexing, none of them is known to scale beyond 1015 dimensional spaces. This paper introduces the hybrid tree – a multidimensional data structure for indexing high dimensional feature spaces. Unlike other multidimensional data structures, the hybrid tree cannot be classified as either a pure data partitioning (DP) index structure (e.g., Rtree, SStree, SRtree) or a pure space partitioning (SP) one (e.g., KDBtree, hBtree); rather, it “combines ” positive aspects of the two types of index structures a single data structure to achieve search performance more scalable to high dimensionalities than either of the above techniques (hence, the name “hybrid”). Furthermore, unlike many data structures (e.g., distance based index structures like SStree, SRtree), the hybrid tree can support queries based on arbitrary distance functions. Our experiments on “real” high dimensional large size feature databases demonstrate that the hybrid tree scales well to high dimensionality and large database sizes. It significantly outperforms both purely DPbased and SPbased index mechanisms as well as linear scan at all dimensionalities for large sized databases. 1.
Topological Relations in the World of Minimum Bounding Rectangles: A Study with RTrees
, 1995
"... Recent developments in spatial relations have led to their use in numerous applications involving spatial databases. This paper is concerned with the retrieval of topological relations in Minimum Bounding Rectanglebased data structures. We study the topological information that Minimum Bounding Rec ..."
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Cited by 112 (36 self)
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Recent developments in spatial relations have led to their use in numerous applications involving spatial databases. This paper is concerned with the retrieval of topological relations in Minimum Bounding Rectanglebased data structures. We study the topological information that Minimum Bounding Rectangles convey about the actual objects they enclose, using the concept of projections. Then we apply the results to Rtrees and their variations, Rtrees and R*trees in order to minimise disk accesses for queries involving topological relations. We also investigate queries that involve complex spatial conditions in the form of disjunctions and conjunctions and we discuss possible extensions.