Fast algorithms for spatial and multidimensional joins

  • 133 Pages
  • 3.57 MB
  • English
University of Toronto, Dept. of Computer Science , Toronto
StatementNikos Koudas.
The Physical Object
Pagination133 p.
ID Numbers
Open LibraryOL18452754M
ISBN 100612352102

R-trees have been successfully applied to boost the performance of a variety of operations involving multidimensional objects, including spatial joins Brinkhoo et al.

Huang et al. Samet H Multidimensional data structures for spatial applications Algorithms and theory of computation handbook, () Dhesi A and Kar P Random Projection Trees revisited Proceedings of the 23rd International Conference on Neural Information Processing Systems - Volume 1, (). Samet H Multidimensional data structures for spatial applications Algorithms and theory of computation handbook, () Nichols G and Wyman C Multiresolution splatting for indirect illumination Proceedings of the symposium on Interactive 3D graphics and games, ().

Ran et al. [34] revisited He's algorithm after seven years under modern GPU Fast algorithms for spatial and multidimensional joins book and achieved up to 20X speedup over the CPU-based join algorithms and [35] developed an fast Equi-Join.

Details Fast algorithms for spatial and multidimensional joins PDF

Simple Fast Algorithms for the Editing Distance between Trees and Related Problems. Related Databases. A Multidimensional Sequence Approach to Measuring Tree Similarity. Fast Algorithms for Comparison of Similar Unordered Trees. Algorithms and Computation, Cited by: 2 EXTERNAL MEMORY ALGORITHMS, I/O EFFICIENCY, AND DATABASES.

A good introduction on external memory algorithms and data structures is my book on the subject. Aggarwal and J. Vitter. ``The Input/Output Complexity of Sorting and Related Problems,'' Communications of the ACM, 31(9), September   The R-tree and its variations are commonly cited multidimensional access methods that can be used for answering such queries.

Although, the related algorithms work well for low-dimensional data spaces, their performance degrades as the number of dimensions increases (dimensionality curse).

In order to obtain accept- able response time in high Cited by: The spatial types and operations can be made a part of an object-relational query language such as SQL3. The performance enhancement provided by these systems includes a multidimensional spatial index and algorithms for spatial access methods, spatial range queries, and spatial by: Among spatial information applications, SpatialHadoop is one of the most important systems for researchers.

Description Fast algorithms for spatial and multidimensional joins PDF

Broad analyses prove that SpatialHadoop outperforms the traditional Hadoop in managing distinctive spatial information operations. This paper presents a Two Dimensional Priority R-Tree (2DPR-Tree) as a new partitioning technique in by: 3. Algorithms for Query Processing and Optimization of Spatial Operations 1.

Algorithms for Spatial Joins and Spatial Query Processing and Optimization -Natasha Mandal 2. Applications of Spatial Queries O Spatial Database Systems O Geographical Information Systems O Urban Planning O CAD/CAM systems O Image Databases 3.

Nievergelt, J. (): 7+-2 criteria for assessing and comparing spatial data structures; Symposium on the Design and Implementation of Large Spatial Databases, Santa Barbara, Lecture Notes in Computer Science, Vol.Springer-Verlag, Berlin, 3–28 Google ScholarCited by: The K-Closest-Pairs Query (K-CPQ), a type of distance join in spatial databases, discovers the K pairs of objects formed from two different datasets with the K smallest distances.

Recently, branch-and-bound algorithms based on R-trees have been developed in order to answer K-CPQs query optimization purposes, analytical models are needed to estimate the processing cost of a Cited by: Zilio, Daniel, Physical Database Design Decision Algorithms and Concurrent Reorganization for Parallel Database Systems () Koudas, Nikolaos, Fast Algorithms for Spatial and Multidimensional Joins () Faloutsos, Michalis, The Greedy, The Naive, and The Optimal Multicast Routing: From Theory to Internet Protocols ().

Introduction. The term “Spatial Database” refers to a database that stores data for phenomena on, above or below the earth's surface, or in general, various kinds of multidimensional entities of modern life (e.g.

the layout of a VLSI design).In other words, a spatial database is a database system with the ability to handle geometric, geographic, or spatial data (i.e. data related to Cited by: Such geometric algorithms are building blocks of spatial queries on spatial data streams.

They are also capable of processing non-spatial streams. For example, flows in an IP network may be modeled as intervals ([start time, end time]) in a 1-d space, or user document downloads on a peer-to-peer network can be mapped into a high dimensional.

The relative importance of each of these settings is dictated by the state of computer technology and its economics.

For example, the relative latencies of the entire hardware stack (e.g., of network communication, disk accesses, RAM accesses, cache accesses, etc.) shifts over time, ultimately affecting the best choice of algorithm for a problem.

The three-volume set, LNCSLNCSand LNCSconstitutes the refereed proceedings of the International Conference on Computational Science and Its Applications, ICCSAheld in Montreal, Canada, in May The three volumes present more than papers and span the whole range of computational science from foundational issues in computer science and mathematics.

Spatial Data Structures and Algorithms: Spatial data basically consists of objects that are made up of lines, points, surfaces, etc. The l package of SciPy can compute Voronoi diagrams, triangulations, etc using the Qhull library. It also consists of KDTree implementations for Author: Wajiha Urooj.

Cyrus Shahabi, Mohammad R. Kolahdouzan, and Maytham Safar, Alternative Strategies for Performing Spatial Joins on Web Sources, Knowledge and Information Systems (KAIS) by Springer-Verlag, Volume 6, Number 3, pp.ISSN: (Paper) (Online), May 3.

Data Clustering Algorithms. Data Clustering: A Review, A. Jain, M.N. Murthy and P.J. Flynn, ACM Computing Reviews, Nov 2. On Line Clustering, Athman Bouguettaya, IEEE Transaction on Knowledge and Data Engineering Volume 8, No. 2, April 3. Similarity Searching in Medical Image Databases, Euripides G.M.

Petrakis and Christos Faloutsos, IEEE Transaction on Knowledge and. Indexing is a way to optimize the performance of a database by minimizing the number of disk accesses required when a query is processed. It is a data structure technique which is used to quickly locate and access the data in a database.

Indexes are created using a few database columns. The first column is the Search key that contains a copy of /5.

claimed as trademarks. Where those designations appear in this book, and the publisher was aware of a trademark claim, the designations have been printed in initial caps or all caps. Library of Congress Cataloging-in-Publication Data Elmasri, Ramez.

Fundamentals of database systems / Ramez Elmasri, Shamkant B. Navathe.—6th ed. Size: 8MB. Sorting in space: Multidimensional, spatial, and metric data structures for applications in spatial databases, geographic information systems (gis), and location-based services.

In Proceedings of the 29th IEEE International Conference on Data Engineering, pagesBrisbane, Australia, April R-trees are tree data structures used for spatial access methods, i.e., for indexing multi-dimensional information such as geographical coordinates, rectangles or R-tree was proposed by Antonin Guttman in and has found significant use in both theoretical and applied contexts.

A common real-world usage for an R-tree might be to store spatial objects such as restaurant Invented: Get this from a library. Advances in spatial databases: 5th International Symposium, SSD '97, Berlin, Germany, Julyproceedings.

[Michel O Scholl; Agnès Voisard;] -- This book constitutes the refereed proceedings of the Fifth International Symposium on Spatial Databases, SSD '97, held in Berlin, Germany, in July The 18 revised full papers presented were.

Duan R and Pettie S Fast algorithms for (max, min)-matrix multiplication and bottleneck shortest paths Proceedings of the twentieth annual ACM-SIAM symposium on Discrete algorithms, () Grahne G, Thomo A and Wadge W () Preferential Regular Path Queries, Fundamenta Informaticae,(), Online publication date: Jan CS A (Advanced Algorithms for Internet Applications) News Flash Administrivia Sign-up Course Overview Topics Schedule Reading List.

News Flash. Midterm Exam We have prepared a take-home midterm exam which is due in class on Wednesday, Nov The exam is available here in postscript and pdf formats.

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Administrivia. Hanan Samet is a Distinguished University Professor of computer science. He leads a number of research projects on the use of hierarchical data structures for database applications involving multimedia data such as spatial and image databases.

SciPy guarantees fast, accurate, and easy-to-code solutions to your numerical and scientific computing applications. Learning SciPy for Numerical and Scientific Computing unveils secrets to some of the most critical mathematical and scientific computing problems.

Package Latest Version Doc Dev License linux osx win noarch Summary; _r-mutex: BSD: X: X: X: A mutex package to ensure environment exclusivity between Anaconda R and MRO. Genetic Algorithms is one of the most popular evolutionary algorithms that have been used for this purpose [19, 61, 15].

Inspired from the principles of natural selection and survival of the fittest, a GA starts with a population of random problem solutions, called by: 3.6 COMPUTATIONAL GEOMETRY. A major issue is how to efficiently manipulate massive amounts of spatial data stored on disk in multidimensional spatial indexes Most spatial join algorithms either assume the existence of a spatial index structure that is traversed during the join process, or solve the problem by sorting, partitioning, or on.

Image Enhancement in the Spatial Domain: Introduction to Spatial and Frequency Methods, Basic Gray Level Transformations, Histogram Equalization, Histogram Processing, Local Enhancement, Image Subtraction, Image Averaging, Basics of Spatial Filtering, Smoothing Spatial Filters, Sharpening Spatial Filters.