Spatial Databases: Technologies, Techniques and Trends

Spatial Databases: Technologies, Techniques and Trends

Yannis Manalopoulos, Apostolos Papadopoulos, Michael Gr. Vassilakopoulos

Copyright: © 2005 |Pages: 358

ISBN13: 9781591403876|ISBN10: 1591403871|EISBN13: 9781591403890|ISBN13 Softcover: 9781591403883

DOI: 10.4018/978-1-59140-387-6

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MLA

Manalopoulos, Yannis, et al., editors. Spatial Databases: Technologies, Techniques and Trends. IGI Global, 2005. https://doi.org/10.4018/978-1-59140-387-6

APA

Manalopoulos, Y., Papadopoulos, A., & Vassilakopoulos, M. G. (Eds.). (2005). Spatial Databases: Technologies, Techniques and Trends. IGI Global. https://doi.org/10.4018/978-1-59140-387-6

Chicago

Manalopoulos, Yannis, Apostolos Papadopoulos, and Michael Gr. Vassilakopoulos, eds. Spatial Databases: Technologies, Techniques and Trends. Hershey, PA: IGI Global, 2005. https://doi.org/10.4018/978-1-59140-387-6

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Spatial Databases: Technologies, Techniques and Trends introduces the reader to the world of spatial databases, and related subtopics. The broad range of topics covered within the chapters includes spatial data modeling, indexing of spatial and spatiotemporal objects, data mining and knowledge discovery in spatial and spatiotemporal management issues and query processing for moving objects. The reader will be able to get in touch with several important research issues the research community is dealing with today.

Covering fundamental aspects up to advanced material, Spatial Databases: Technologies, Techniques and Trends appeals to a broad computer science audience. Although perfect for specialists, each chapter is self contained, making it easy for non-specialists to grasp the main issues involved.

Table of Contents

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Front Materials

Title Page

Copyright Page

Preface

Acknowledgments

Chapters

Modelling and Systems

Chapter 1

Survey on Spatial Data Modelling Approaches (pages 1-22)

Jose R. Rios Viqueira, Nikos A. Lorentzos, Nieves R. Brisaboa

The chapter identifies properties that a spatial data model, dedicated to support spatial data for cartography, topography, cadastral and relevant applications, should satisfy. The properties concern the data types, data structures...

Chapter 2

Integrating Web Data and Geographic Knowledge into Spatial Databases (pages 23-48)

Alberto H.F. Laender, Karla A.V. Borges, Joyce C.P. Carvalho, Claudia B. Medeiros, Altigran S. de Silva, Clodoveu A. Davis Jr.

With the phenomenal growth of the World Wide Web, rich data sources on many different subjects have become available online. Some of these sources store daily facts that often involve textual geographic descriptions. These...

Indexing Techniques

Chapter 3

Object-Relational Spatial Indexing (pages 49-80)

Hans-Peter Kriegel, Martin Pfeifle, Marco Potke, Thomas Seidl, Jost Enderle

In order to generate efficient execution plans for queries comprising spatial data types and predicates, the database system has to be equipped with appropriate index structures, query processing methods and optimization rules....

Chapter 4

Quadtree-based Image Representation and Retrieval (pages 81-106)

Maude Manouvrier, Marta Rukoz, Geneviève Jomier

This chapter is a survey of quadtree uses in the image domain, from image representation to image storage and content-based retrieval. A quadtree is a spatial data structure built by a recursive decomposition of space into quadrants....

Chapter 5

Indexing Multi-Dimensional Trajectories for Similarity Queries (pages 107-129)

Michail Valachos, Marios Hadjieleftheriou, Eamonn Keogh, Dimitrios Gunopulos

With the abundance of low-cost storage devices, a plethora of applications that store and manage very large multi-dimensional trajectories (or time-series) datasets have emerged recently. Examples include traffic supervision systems...

Query Processing and Optimization

Chapter 6

Approximate Computation of Distance-Based Queries (pages 130-154)

Antonio Corral, Michael Vassilakopoulos

In spatial database applications the similarity or dissimilarity of complex objects is examined by performing distance-based queries (DBQs) on data of high dimensionality (a generalization of spatial data). The R-tree and its...

Chapter 7

Spatial Joins: Algorithms, Cost Models and Optimization Techniques (pages 155-185)

Nikos Mamoulis, Yannis Theodoridis, Dimitris Papadias

This chapter describes algorithms, cost models and optimization techniques for spatial joins. Joins are among the most common queries in Spatial Database Management Systems. Due to their importance and high processing cost, a number...

Moving Objects

Chapter 8

Applications of Moving Objects Databases (pages 186-203)

Ouri Wolfson, Eduardo Mena

Miniaturization of computing devices and advances in wireless communication and sensor technology are some of the forces propagating computing from the stationary desktop to the mobile outdoors. Some important classes of new...

Chapter 9

Simple and Incremental Nearest-Neighbor Search in Spatio-Temporal Databases (pages 204-224)

Katerina Raptopoulou, Apostolos N. Papadopoulos, Yannis Manolopoulos

The efficient processing of nearest-neighbor queries in databases of moving objects is considered very important for applications such as fleet management, traffic control, digital battlefields and more. Such applications have been...

Chapter 10

Management of Large Moving Objects Databases: Indexing, Benchmarking and Uncertainty in Movement Representation (pages 225-250)

Talel Abdessalem, Cédric du Mouza, José Moreira, Philippe Rigaux

This chapter deals with several important issues pertaining to the management of moving objects datasets in databases. The design of representative benchmarks is closely related to the formal characterization of the properties (that...

Data Mining

Chapter 11

Spatio-Temporal Prediction Using Data Mining Tools (pages 251-271)

Margaret H. Dunham, Nathaniel Ayewah, Zhigang Li, Kathryn Bean, Jie Huang

The spatio-temporal prediction problem requires that one or more future values be predicted for time series input data obtained from sensors at multiple physical locations. Examples of this type of problem include weather prediction...

Chapter 12

Mining in Spatio-Temporal Databases (pages 272-293)

Junmei Wang, Wynne Hsu, Mong Li Lee

Recent interest in spatio-temporal applications has been fueled by the need to discover and predict complex patterns that occur when we observe the behavior of objects in the three-dimensional space of time and spatial coordinates....

Chapter 13

Similarity Learning in GIS: An Overview of Definitions, Prerequisites and Challenges (pages 294-321)

Giorgos Mountrakis, Peggy Agouris, Anthony Stefanidis

In this chapter we review similarity learning in spatial databases. Traditional exact-match queries do not conform to the exploratory nature of GIS datasets. Non-adaptable query methods fail to capture the highly diverse needs...

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