The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences. Vol. XXXVII. Part B4. Beijing 2008 
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Traditional commercial RDBMS or ORDBMS systems and the 
SQL standards (such as SQL2 and SQL-99) typically support 
only simple data types and BLOB (Binary Large Objects). None 
of those standard data types meets the management 
requirements of geoimagery and raster gridded data. Even 
though the geoimage data can be directly stored as BLOB’s in 
the databases, there is no standard operations developed to 
manipulate them inside the database system, and the standard 
SQL query language doesn’t work the same way as for other 
simple data types. SQL/MM defines a data type SIStilllmage 
to store and manage still 2D images (ISO, 2001). However it 
stores images in one of the standard image file formats, which 
are best used to store only smaller images and are not 
specifically designed to support geoimagery and geospatial 
raster data types. Scalability and performance are concerns for 
both the simple BLOB approach and the SI_StillImage data 
type. 
Geospatial imagery and raster data are typically huge in size 
and have many special metadata associated with them, such as 
coordinate system and georeferencing information. The 
operations on them are also different from other standard data 
types. In order to meet those special requirements, we propose 
an enterprise database-centric approach. This approach has 
uniquely a database-centric focus and uses server-based image 
processing concepts. By database-centric it means the raster 
data are stored and managed inside the database natively and 
the management and processing functionalities are implemented 
and embedded inside the database and closely and securely 
associated with the raster data itself. It’s basically an 
enhancement of the RDBMS from inside. 
More specifically, we think it should consist of three major 
components: a new native database data type for storing raster 
datasets, a server-side image processing and raster operation 
engine, and a standard user-friendly interface. It’s designed to 
work in a client-server environment as well as in any multi-tier 
architecture. 
The native object data type in this approach is specifically 
designed so that it can be used similarly as other standard 
database data types. The data model is generic for most raster 
data types, including geoimagery, so that each image can be 
stored as an object in any relational table. The specific format of 
the object type fits well into the enterprise RDBMS so that it’s 
truly scalable and performant. For example, it allows flexible 
user-specified blocking, which means each image stored can be 
unlimited in size and adaptable to various applications. One 
database table can contain virtually unlimited number of images 
and various internal spatial indexing mechanisms enable fast 
metadata query and raster data retrieval. 
This approach emphasizes a server-side image processing and 
raster operation engine. By doing that it offers true security for 
the data because the data no longer needs to be retrieved and 
loaded into a middleware or client through an insecure network 
and processed in an unmanaged computer memory. The 
processing engine is also closest to the data and so runs faster 
by avoiding data transferring cost. The processes can be run 
concurrently and deployed onto many powerful servers to 
reduce the burden on the desktop image processing systems. 
The processing engine can be coupled with middleware and 
client-side processing systems to fully leverage the power of 
enterprise distributed computing systems. 
The approach offers a single data format and a SQL or PL/SQL 
API, which dramatically improves usability and simplifies data 
access. Usability is one of the key drivers behind this database 
centric approach. SQL is the standard for enterprises and 
enterprise application developers are most familiar with it. By 
storing and managing the data inside the database, offering 
various indexing and query capabilities, and providing many 
basic processing operations through an easy-to-use and standard 
interface, this approach allows non-geoimaging experts easily 
integrate geospatial data with enterprise data, quickly leverage 
geospatial technologies, and deploy powerful IT resources so 
that the geoimagery and related information can be quickly 
delivered, distributed and used by different enterprises and mass 
consumers. 
Oracle GeoRaster, an enterprise database management system 
for geospatial raster datasets, was designed based on this 
approach. To prove the concept of such a native database 
centric approach, part of the design and some key benefits of 
Oracle GeoRaster are further described in the floowing sections 
of this paper. Some tests and research using the Oracle 
GeoRaster technology were conducted and are partially 
presented as well. 
In the tests we used Oracle Database lOg Release 1, which was 
installed on Asianux 1.0 Service Pack 1. The Linux server has 
4x 1G RAM, 4x 2.4GHz CPU, and lx 72G internal hard disk. 
Network Appliance NearStore R200 system was used for 
database storage. It is a disk-based nearline storage system and 
provides near-primary storage performance at near-tape storage 
costs. The NetApp Storage consists of 16 disks (14 data disks + 
2 parity disks, each disk is 292GB) combined into one global 
disk by RAID4. The test dataset includes 50 digital Color Ortho 
Images, courtesy of the Office of MassGIS, Commonwealth of 
Massachusetts Executive Office of Environmental Affairs. 
These 50 images cover the greater Boston area and can be 
seamlessly mosaicked into one large image. Each image has 
8000 rows, 8000 columns and 3 bands and has a size of 183MB 
stored in TIFF format. 
3. THE NATIVE RASTER DATA TYPE AND THE 
SCALABILITY 
As described above, the first key component of this database 
centric approach is a new native raster data type, which is called 
the GeoRaster data type in Oracle 1 Og and 11 g databases 
(Oracle, 2004; Xie, 2008). Oracle GeoRaster defines a 
component-based logically layered multidimensional raster data 
model. A raster data object consists of raster cell data and 
associated metadata. The raster cell data is a multidimensional 
matrix of raster cells. Each cell stores a value, referred to as the 
cell value. The number of bits used to store the cell value is 
called the cell depth. The matrix has a number of dimensions, a 
cell depth, and a size for each dimension. As a multi 
dimensional matrix, the core data can be blocked and 
compressed for optimal storage, retrieval and processing. In the 
GeoRaster data model, all associated information (other than 
the raster cell matrix) for the raster object is stored as 
“metadata”, which include raster information, spatial reference 
system information, date and time information, layer 
information, and spatial extent (footprint) etc. 
More specifically, a raster data (an image or a grid) is stored in 
Oracle as an object of the SDOGEORASTER data type, called 
the GeoRaster object. This object type is the core data type for 
users and it stores all metadata and necessary information for 
indexing and raster data query. The type is defined as below: