Book Image

Applying and Extending Oracle Spatial

By : Siva Ravada, Simon Greener
Book Image

Applying and Extending Oracle Spatial

By: Siva Ravada, Simon Greener

Overview of this book

Spatial applications should be developed in the same way that users develop other database applications: by starting with an integrated data model in which the SDO_GEOMETRY objects are just another attribute describing entities and by using as many of the database features as possible for managing the data. If a task can be done using a database feature like replication, then it should be done using the standard replication technology instead of inventing a new procedure for replicating spatial data. Sometimes solving a business problem using a PL/SQL function can be more powerful, accessible, and easier to use than trying to use external software. Because Oracle Spatial's offerings are standards compliant, this book shows you how Oracle Spatial technology can be used to build cross-vendor database solutions. Applying and Extending Oracle Spatial shows you the clever things that can be done not just with Oracle Spatial on its own, but in combination with other database technologies. This is a great resource book that will convince you to purchase other Oracle technology books on non-spatial specialist technologies because you will finally see that "spatial is not special: it is a small, fun, and clever part of a much larger whole".
Table of Contents (20 chapters)
Applying and Extending Oracle Spatial
About the Authors
About the Reviewers
Table Comparing Simple Feature Access/SQL and SQL/MM–Spatial

Creating spatial autocorrelation via clustering

When spatial data is loaded into a table, data is usually organized depending on the order of the incoming rows. This ordering of the data on disk has a direct impact on the performance of the spatial queries. Consider a simple SDO_ANYINTERACT query that retrieves all the data inside a rectangular box. After the spatial query is performed, the rows that satisfy the result are retrieved from the table. If all of these rows are spread over different data blocks, the cost of the query increases, as many blocks have to be fetched to form the result set. If data in the blocks can be organized in such a way to minimize the number of blocks fetched for each query, the query performance would improve. This improvement will be greater for queries that fetch a large number of rows for each query. For example, in web mapping applications, small scale maps show less detail and large scale maps show more detail. As the scale goes from small to large, more...