Optimal redundancy in spatial database systems

Gaede, Volker

doi:10.1007/3-540-60159-7_7

Cited by 22 publications

(12 citation statements)

References 9 publications

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“…This approach, which has become popular with the B-tree, is based on the assumption that most searches are I/O-bound rather than CPU-bound-an assumption that is not always true in spatial data management, however. In applications where objects have complex shapes, the refinement step can incur major CPU costs and change the balance with I/O [Gaede 1995b;Hoel and Samet 1995]. Of course, one should keep the minimization of the number of disk accesses in mind as one design goal.…”

Section: Multidimensional Access Methodsmentioning

confidence: 99%

“…More Peano regions obviously yield more accuracy, but they also increase the size and complexity of the approximation. As pointed out by Orenstein [1989b], there are two possibly conflicting objectives: the number of Peano regions to approximate the object should be small, since this results in fewer index entries; and the accuracy of the approximation should be high, since this reduces the expected number of false drops [Orenstein 1989a, b;Gaede 1995b]. Objects are thus paged in from secondary memory, only to find out that they do not satisfy the search predicate.…”

Section: Multidimensional Access Methodsmentioning

confidence: 99%

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Multidimensional access methods

1998

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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). More than ten years of spatial database research have resulted in a great variety of multidimensional access methods to support such operations. We give an overview of that work. After a brief survey of spatial data management in general, we first present the class of point access methods, which are used to search sets of points in two or more dimensions. The second part of the paper is devoted to spatial access methods to handle extended objects, such as rectangles or polyhedra. We conclude with a discussion of theoretical and experimental results concerning the relative performance of various approaches.

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Section: Multidimensional Access Methodsmentioning

confidence: 99%

Section: Multidimensional Access Methodsmentioning

confidence: 99%

Multidimensional access methods

1998

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“…The concept of fractal dimension is also widely used in the domain of spatial databases, where the complexity of stored polygons is modeled [Gae95,FG96]. These approaches are of minor importance for point databases.…”

Section: Cost Models For High-dimensional Index Structuresmentioning

confidence: 99%

Searching in high-dimensional spaces

Böhm¹,

Berchtold²,

2001

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During the last decade, multimedia databases have become increasingly important in many application areas such as medicine, CAD, geography, or molecular biology. An important research issue in the field of multimedia databases is the content based retrieval of similar multimedia objects such as images, text, and videos. However, in contrast to searching data in a relational database, a content based retrieval requires the search of similar objects as a basic functionality of the database system. Most of the approaches addressing similarity search use a so-called feature transformation which transforms important properties of the multimedia objects into high-dimensional points (feature vectors). Thus, the similarity search is transformed into a search of points in the feature space which are close to a given query point in the high-dimensional feature space. Query Processing in high-dimensional spaces has therefore been a very active research area over the last few years. A number of new index structures and algorithms have been proposed. It has been shown that the new index structures considerably improve the performance in querying large multimedia databases. Based on recent tutorials [BK98, BK 00], in this survey we provide an overview of the current state-of-the-art in querying multimedia databases, describing the index structures and algorithms for an efficient query processing in high-dimensional spaces. We identify the problems of processing queries in high-dimensional space, and we provide an overview of the proposed approaches to overcome these problems. Indexing Multimedia DatabasesMultimedia databases are of high importance in many application areas such as geography, CAD, medicine, or molecular biology. Depending on the application, the multimedia databases need to have different properties and need to support different types of queries. In contrast to traditional database applications, where point, range, and partial match queries are very important, multimedia databases require a search for all objects in the database which are similar (or complementary) to a given search object. In the following, we describe the notion of similarity queries and the feature-based approach to process those queries in multimedia databases in more detail.

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“…Please note also that we are focussing on the representation of spatial objects which means that the objects have extent (e.g., Arman and Aggarwal [1993], Samet [1990aSamet [ , 1990b, Suetens et al [1992]) rather than being merely points. The representation of multidimensional points has been much studied in the database literature (e.g., Böhm et al [2001], Gaede [1995], and Samet [1990b]). …”

Section: Introductionmentioning

confidence: 99%

Object-based and image-based object representations

Samet

2004

ACM Comput. Surv.

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An overview is presented of object-based and image-based representations of objects by their interiors. The representations are distinguished by the manner in which they can be used to answer two fundamental queries in database applications: (1) Feature query: given an object, determine its constituent cells (i.e., their locations in space). (2) Location query: given a cell (i.e., a location in space), determine the identity of the object (or objects) of which it is a member as well as the remaining constituent cells of the object (or objects). Regardless of the representation that is used, the generation of responses to the feature and location queries is facilitated by building an index (i.e., the result of a sort) either on the objects or on their locations in space, and implementing it using an access structure that correlates the objects with the locations. Assuming the presence of an access structure, implicit (i.e., image-based) representations are described that are good for finding the objects associated with a particular location or cell (i.e., the location query), while requiring that all cells be examined when determining the locations associated with a particular object (i.e., the feature query). In contrast, explicit (i.e., object-based) representations are good for the feature query, while requiring that all objects be examined when trying to respond to the location query. The goal is to be able to answer both types of queries with one representation and without possibly having to examine every cell. Representations are presented that achieve this goal by imposing containment hierarchies on either space (i.e., the cells in the space in which the objects are found), or objects. In the former case, space is aggregated into successively larger-sized chunks (i.e., blocks), while in the latter, objects are aggregated into successively larger groups (in terms of the number of objects that they contain). The former is applicable to image-based interior-based representations of which the space pyramid is an example. The latter is applicable to object-based interior-based representations of which the R-tree is an example. The actual mechanics of many of these representations are demonstrated in the VASCO JAVA applets found at

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Optimal redundancy in spatial database systems

Cited by 22 publications

References 9 publications

Multidimensional access methods

Multidimensional access methods

Searching in high-dimensional spaces

Object-based and image-based object representations

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