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Advances in Spatial Databases: 6th International Symposium, SSD’99 Hong Kong, China, July 20—23, 1999 Proceedings Author: Ralf Hartmut Güting, Dimitris Papadias, Fred Lochovsky Published by Springer Berlin Heidelberg ISBN: 978-3-540-66247-1 DOI: 10.1007/3-540-48482-5 Table of Contents: Topological Queries Research Issues in Spatio-temporal Database Systems Looking Both Ways: SSD 1999 ± 10 Generalizing Graphs Using Amalgamation and Selection Data Structures for Simplicial Multi-complexes Spatial Indexing with a Scale Dimension XZ-Ordering: A Space-Filling Curve for Objects with Spatial Extension GBI: A Generalized R-Tree Bulk-Insertion Strategy Capturing the Uncertainty of Moving-Object Representations Dynamic Spatial Clustering for Intelligent Mobile Information Sharing and Dissemination On the Generation of Spatiotemporal Datasets Efficient Polygon Amalgamation Methods for Spatial OLAP and Spatial Data Mining Efficiently Matching Proximity Relationships in Spatial Databases 3D Shape Histograms for Similarity Search and Classification in Spatial Databases Multi-way Spatial Joins Using R-Trees: Methodology and Performance Evaluation Algorithms for Joining R-Trees and Linear Region Quadtrees Algorithms for Performing Polygonal Map Overlay and Spatial Join on Massive Data Sets A Performance Evaluation of Spatial Join Processing Strategies Abduction and Deduction in Geologic Hypermaps Uncertainty Management for Spatial Datain Databases: Fuzzy Spatial Data Types

Advances in Spatial Databases: 6th International Symposium, SSD’99 Hong Kong, China, July 20—23, 1999 Proceedings Author: Ralf Hartmut Güting, Dimitris Papadias, Fred Lochovsky Published by Springer Berlin Heidelberg ISBN: 978-3-540-66247-1 DOI: 10.1007/3-540-48482-5 Table of Contents: Topological Queries Research Issues in Spatio-temporal Database Systems Looking Both Ways: SSD 1999 ± 10 Generalizing Graphs Using Amalgamation and Selection Data Structures for Simplicial Multi-complexes Spatial Indexing with a Scale Dimension XZ-Ordering: A Space-Filling Curve for Objects with Spatial Extension GBI: A Generalized R-Tree Bulk-Insertion Strategy Capturing the Uncertainty of Moving-Object Representations Dynamic Spatial Clustering for Intelligent Mobile Information Sharing and Dissemination On the Generation of Spatiotemporal Datasets Efficient Polygon Amalgamation Methods for Spatial OLAP and Spatial Data Mining Efficiently Matching Proximity Relationships in Spatial Databases 3D Shape Histograms for Similarity Search and Classification in Spatial Databases Multi-way Spatial Joins Using R-Trees: Methodology and Performance Evaluation Algorithms for Joining R-Trees and Linear Region Quadtrees Algorithms for Performing Polygonal Map Overlay and Spatial Join on Massive Data Sets A Performance Evaluation of Spatial Join Processing Strategies Abduction and Deduction in Geologic Hypermaps Uncertainty Management for Spatial Datain Databases: Fuzzy Spatial Data Types

Advances in Spatial Databases: 6th International Symposium, SSD’99 Hong Kong, China, July 20—23, 1999 Proceedings Author: Ralf Hartmut Güting, Dimitris Papadias, Fred Lochovsky Published by Springer Berlin Heidelberg ISBN: 978-3-540-66247-1 DOI: 10.1007/3-540-48482-5 Table of Contents: Topological Queries Research Issues in Spatio-temporal Database Systems Looking Both Ways: SSD 1999 ± 10 Generalizing Graphs Using Amalgamation and Selection Data Structures for Simplicial Multi-complexes Spatial Indexing with a Scale Dimension XZ-Ordering: A Space-Filling Curve for Objects with Spatial Extension GBI: A Generalized R-Tree Bulk-Insertion Strategy Capturing the Uncertainty of Moving-Object Representations Dynamic Spatial Clustering for Intelligent Mobile Information Sharing and Dissemination On the Generation of Spatiotemporal Datasets Efficient Polygon Amalgamation Methods for Spatial OLAP and Spatial Data Mining Efficiently Matching Proximity Relationships in Spatial Databases 3D Shape Histograms for Similarity Search and Classification in Spatial Databases Multi-way Spatial Joins Using R-Trees: Methodology and Performance Evaluation Algorithms for Joining R-Trees and Linear Region Quadtrees Algorithms for Performing Polygonal Map Overlay and Spatial Join on Massive Data Sets A Performance Evaluation of Spatial Join Processing Strategies Abduction and Deduction in Geologic Hypermaps Uncertainty Management for Spatial Datain Databases: Fuzzy Spatial Data Types

Advances in Spatial Databases: 6th International Symposium, SSD’99 Hong Kong, China, July 20—23, 1999 Proceedings Author: Ralf Hartmut Güting, Dimitris Papadias, Fred Lochovsky Published by Springer Berlin Heidelberg ISBN: 978-3-540-66247-1 DOI: 10.1007/3-540-48482-5 Table of Contents: Topological Queries Research Issues in Spatio-temporal Database Systems Looking Both Ways: SSD 1999 ± 10 Generalizing Graphs Using Amalgamation and Selection Data Structures for Simplicial Multi-complexes Spatial Indexing with a Scale Dimension XZ-Ordering: A Space-Filling Curve for Objects with Spatial Extension GBI: A Generalized R-Tree Bulk-Insertion Strategy Capturing the Uncertainty of Moving-Object Representations Dynamic Spatial Clustering for Intelligent Mobile Information Sharing and Dissemination On the Generation of Spatiotemporal Datasets Efficient Polygon Amalgamation Methods for Spatial OLAP and Spatial Data Mining Efficiently Matching Proximity Relationships in Spatial Databases 3D Shape Histograms for Similarity Search and Classification in Spatial Databases Multi-way Spatial Joins Using R-Trees: Methodology and Performance Evaluation Algorithms for Joining R-Trees and Linear Region Quadtrees Algorithms for Performing Polygonal Map Overlay and Spatial Join on Massive Data Sets A Performance Evaluation of Spatial Join Processing Strategies Abduction and Deduction in Geologic Hypermaps Uncertainty Management for Spatial Datain Databases: Fuzzy Spatial Data Types

Advances in Spatial Databases: 6th International Symposium, SSD’99 Hong Kong, China, July 20—23, 1999 Proceedings Author: Ralf Hartmut Güting, Dimitris Papadias, Fred Lochovsky Published by Springer Berlin Heidelberg ISBN: 978-3-540-66247-1 DOI: 10.1007/3-540-48482-5 Table of Contents: Topological Queries Research Issues in Spatio-temporal Database Systems Looking Both Ways: SSD 1999 ± 10 Generalizing Graphs Using Amalgamation and Selection Data Structures for Simplicial Multi-complexes Spatial Indexing with a Scale Dimension XZ-Ordering: A Space-Filling Curve for Objects with Spatial Extension GBI: A Generalized R-Tree Bulk-Insertion Strategy Capturing the Uncertainty of Moving-Object Representations Dynamic Spatial Clustering for Intelligent Mobile Information Sharing and Dissemination On the Generation of Spatiotemporal Datasets Efficient Polygon Amalgamation Methods for Spatial OLAP and Spatial Data Mining Efficiently Matching Proximity Relationships in Spatial Databases 3D Shape Histograms for Similarity Search and Classification in Spatial Databases Multi-way Spatial Joins Using R-Trees: Methodology and Performance Evaluation Algorithms for Joining R-Trees and Linear Region Quadtrees Algorithms for Performing Polygonal Map Overlay and Spatial Join on Massive Data Sets A Performance Evaluation of Spatial Join Processing Strategies Abduction and Deduction in Geologic Hypermaps Uncertainty Management for Spatial Datain Databases: Fuzzy Spatial Data Types

Advances in Spatial Databases: 6th International Symposium, SSD’99 Hong Kong, China, July 20—23, 1999 Proceedings Author: Ralf Hartmut Güting, Dimitris Papadias, Fred Lochovsky Published by Springer Berlin Heidelberg ISBN: 978-3-540-66247-1 DOI: 10.1007/3-540-48482-5 Table of Contents: Topological Queries Research Issues in Spatio-temporal Database Systems Looking Both Ways: SSD 1999 ± 10 Generalizing Graphs Using Amalgamation and Selection Data Structures for Simplicial Multi-complexes Spatial Indexing with a Scale Dimension XZ-Ordering: A Space-Filling Curve for Objects with Spatial Extension GBI: A Generalized R-Tree Bulk-Insertion Strategy Capturing the Uncertainty of Moving-Object Representations Dynamic Spatial Clustering for Intelligent Mobile Information Sharing and Dissemination On the Generation of Spatiotemporal Datasets Efficient Polygon Amalgamation Methods for Spatial OLAP and Spatial Data Mining Efficiently Matching Proximity Relationships in Spatial Databases 3D Shape Histograms for Similarity Search and Classification in Spatial Databases Multi-way Spatial Joins Using R-Trees: Methodology and Performance Evaluation Algorithms for Joining R-Trees and Linear Region Quadtrees Algorithms for Performing Polygonal Map Overlay and Spatial Join on Massive Data Sets A Performance Evaluation of Spatial Join Processing Strategies Abduction and Deduction in Geologic Hypermaps Uncertainty Management for Spatial Datain Databases: Fuzzy Spatial Data Types

Advances in Spatial Databases: 6th International Symposium, SSD’99 Hong Kong, China, July 20—23, 1999 Proceedings Author: Ralf Hartmut Güting, Dimitris Papadias, Fred Lochovsky Published by Springer Berlin Heidelberg ISBN: 978-3-540-66247-1 DOI: 10.1007/3-540-48482-5 Table of Contents: Topological Queries Research Issues in Spatio-temporal Database Systems Looking Both Ways: SSD 1999 ± 10 Generalizing Graphs Using Amalgamation and Selection Data Structures for Simplicial Multi-complexes Spatial Indexing with a Scale Dimension XZ-Ordering: A Space-Filling Curve for Objects with Spatial Extension GBI: A Generalized R-Tree Bulk-Insertion Strategy Capturing the Uncertainty of Moving-Object Representations Dynamic Spatial Clustering for Intelligent Mobile Information Sharing and Dissemination On the Generation of Spatiotemporal Datasets Efficient Polygon Amalgamation Methods for Spatial OLAP and Spatial Data Mining Efficiently Matching Proximity Relationships in Spatial Databases 3D Shape Histograms for Similarity Search and Classification in Spatial Databases Multi-way Spatial Joins Using R-Trees: Methodology and Performance Evaluation Algorithms for Joining R-Trees and Linear Region Quadtrees Algorithms for Performing Polygonal Map Overlay and Spatial Join on Massive Data Sets A Performance Evaluation of Spatial Join Processing Strategies Abduction and Deduction in Geologic Hypermaps Uncertainty Management for Spatial Datain Databases: Fuzzy Spatial Data Types

Advances in Spatial Databases: 6th International Symposium, SSD’99 Hong Kong, China, July 20—23, 1999 Proceedings Author: Ralf Hartmut Güting, Dimitris Papadias, Fred Lochovsky Published by Springer Berlin Heidelberg ISBN: 978-3-540-66247-1 DOI: 10.1007/3-540-48482-5 Table of Contents: Topological Queries Research Issues in Spatio-temporal Database Systems Looking Both Ways: SSD 1999 ± 10 Generalizing Graphs Using Amalgamation and Selection Data Structures for Simplicial Multi-complexes Spatial Indexing with a Scale Dimension XZ-Ordering: A Space-Filling Curve for Objects with Spatial Extension GBI: A Generalized R-Tree Bulk-Insertion Strategy Capturing the Uncertainty of Moving-Object Representations Dynamic Spatial Clustering for Intelligent Mobile Information Sharing and Dissemination On the Generation of Spatiotemporal Datasets Efficient Polygon Amalgamation Methods for Spatial OLAP and Spatial Data Mining Efficiently Matching Proximity Relationships in Spatial Databases 3D Shape Histograms for Similarity Search and Classification in Spatial Databases Multi-way Spatial Joins Using R-Trees: Methodology and Performance Evaluation Algorithms for Joining R-Trees and Linear Region Quadtrees Algorithms for Performing Polygonal Map Overlay and Spatial Join on Massive Data Sets A Performance Evaluation of Spatial Join Processing Strategies Abduction and Deduction in Geologic Hypermaps Uncertainty Management for Spatial Datain Databases: Fuzzy Spatial Data Types

Over the past decade there have been significant advances in bringing parallel computing and new database management systems to a wider audience. Through a number of efforts such as the National Strategic Computing Initiative (NSCI), there has been a push to merge these Big Data and Scientific Computing communities to a single computational platform. At the Massachusetts Institute of Technology, Lincoln Laboratory, we have been developing HPC and database technologies to address a number of scientific problems including biomedical processing. In this article, we briefly describe these technologies and how we have used them in the past. We are interested in learning more about the needs of clinical pathologists as we continue to develop these technologies.

Over the past decade there have been significant advances in bringing parallel computing and new database management systems to a wider audience. Through a number of efforts such as the National Strategic Computing Initiative (NSCI), there has been a push to merge these Big Data and Scientific Computing communities to a single computational platform. At the Massachusetts Institute of Technology, Lincoln Laboratory, we have been developing HPC and database technologies to address a number of scientific problems including biomedical processing. In this article, we briefly describe these technologies and how we have used them in the past. We are interested in learning more about the needs of clinical pathologists as we continue to develop these technologies.

Over the past decade there have been significant advances in bringing parallel computing and new database management systems to a wider audience. Through a number of efforts such as the National Strategic Computing Initiative (NSCI), there has been a push to merge these Big Data and Scientific Computing communities to a single computational platform. At the Massachusetts Institute of Technology, Lincoln Laboratory, we have been developing HPC and database technologies to address a number of scientific problems including biomedical processing. In this article, we briefly describe these technologies and how we have used them in the past. We are interested in learning more about the needs of clinical pathologists as we continue to develop these technologies.

Over the past decade there have been significant advances in bringing parallel computing and new database management systems to a wider audience. Through a number of efforts such as the National Strategic Computing Initiative (NSCI), there has been a push to merge these Big Data and Scientific Computing communities to a single computational platform. At the Massachusetts Institute of Technology, Lincoln Laboratory, we have been developing HPC and database technologies to address a number of scientific problems including biomedical processing. In this article, we briefly describe these technologies and how we have used them in the past. We are interested in learning more about the needs of clinical pathologists as we continue to develop these technologies.

Over the past decade there have been significant advances in bringing parallel computing and new database management systems to a wider audience. Through a number of efforts such as the National Strategic Computing Initiative (NSCI), there has been a push to merge these Big Data and Scientific Computing communities to a single computational platform. At the Massachusetts Institute of Technology, Lincoln Laboratory, we have been developing HPC and database technologies to address a number of scientific problems including biomedical processing. In this article, we briefly describe these technologies and how we have used them in the past. We are interested in learning more about the needs of clinical pathologists as we continue to develop these technologies.

Over the past decade there have been significant advances in bringing parallel computing and new database management systems to a wider audience. Through a number of efforts such as the National Strategic Computing Initiative (NSCI), there has been a push to merge these Big Data and Scientific Computing communities to a single computational platform. At the Massachusetts Institute of Technology, Lincoln Laboratory, we have been developing HPC and database technologies to address a number of scientific problems including biomedical processing. In this article, we briefly describe these technologies and how we have used them in the past. We are interested in learning more about the needs of clinical pathologists as we continue to develop these technologies.

Over the past decade there have been significant advances in bringing parallel computing and new database management systems to a wider audience. Through a number of efforts such as the National Strategic Computing Initiative (NSCI), there has been a push to merge these Big Data and Scientific Computing communities to a single computational platform. At the Massachusetts Institute of Technology, Lincoln Laboratory, we have been developing HPC and database technologies to address a number of scientific problems including biomedical processing. In this article, we briefly describe these technologies and how we have used them in the past. We are interested in learning more about the needs of clinical pathologists as we continue to develop these technologies.

Over the past decade there have been significant advances in bringing parallel computing and new database management systems to a wider audience. Through a number of efforts such as the National Strategic Computing Initiative (NSCI), there has been a push to merge these Big Data and Scientific Computing communities to a single computational platform. At the Massachusetts Institute of Technology, Lincoln Laboratory, we have been developing HPC and database technologies to address a number of scientific problems including biomedical processing. In this article, we briefly describe these technologies and how we have used them in the past. We are interested in learning more about the needs of clinical pathologists as we continue to develop these technologies.

Over the past decade there have been significant advances in bringing parallel computing and new database management systems to a wider audience. Through a number of efforts such as the National Strategic Computing Initiative (NSCI), there has been a push to merge these Big Data and Scientific Computing communities to a single computational platform. At the Massachusetts Institute of Technology, Lincoln Laboratory, we have been developing HPC and database technologies to address a number of scientific problems including biomedical processing. In this article, we briefly describe these technologies and how we have used them in the past. We are interested in learning more about the needs of clinical pathologists as we continue to develop these technologies.

xi, 369 p. : 24 cm

xi, 369 p. : 24 cm

xi, 369 p. : 24 cm

Author: Published by ISBN: DOI: Table of Contents:

Author: Published by ISBN: DOI: Table of Contents: