ISPRS. Vol.34, Part 2W2. ^Dynamic and Multi-Dimensional GISL Bangkok May 23-25 2001_
ISPRS, Vol.34,
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Server-side Client-side
Fig.3: Client-Server 3D Internet GIS
3.2 Storing large data set and Internet Query analysis
The multiple and large amount of building data storing in server
database require the data management to be powerful and
efficient. The traditional RDBMS has several weaknesses in
managing 3D geometry and attribute data, especially in hybrid
data structure. Now, some commercial databases such as
Oracle 8i can handle Object-Relation data and provide binary
large object (BLOB) type, permitting image and spatial data
management. They can also construct lager Internet database
and provide many data interface to access the database
Considering the entire data of all buildings such as millions of
thousands buildings in a city, it is impossible to keep all the
data in memory of a workstation for visualization and analysis,
so a well-organized data structure and fast data retrieval
mechanism have to be provided
In order to efficiently manage 3D building data in database, the
BLOB can be extended to store building model. For the
purpose of query and analysis, the operation of writing and
reading BLOB from database must be defined, at the same
time, the index of BLOB also must be provided to fasten the
data retrieval. The R-tree structure is proved to be a useful
index. Compared with other spatial data structure such as
Quad-trees or Oct-trees, it is obvious that R-tree is much better
suited for the organization of overlapping object, the rectangle
bounding boxes of 2D or 3D CAD model of building,
buildingblocks and large urban units (Gruber, 1998). According
to the R-tree, the block of divide building can be a district or
several buildings For the purpose of visualization, different
abstract layer of LC model data and R-tree index require to be
stored in database. According to LC model, the 3D building
object can be depicted as follow:
BuildingObject=( Layers, BodyObjectArray, RoofObjectArray.
R-tree index).
BodyObjectArray=( Bodyl ,Body2, ...Bodyn).
RoofObjectArray=( Roof 1,Roof2,.....Roofn).
Body=( PointArray, Height), Roof=(BoundaryArray,
FeaturePointArray).
Using the R-tree index to organize the building data on
server-side, the JDBC is used to implement the query the
building attribute and SQL analysis. Using JDBC-ODBC bridge,
the user can communicate between the client-side and the
server-side to query the building attribute and some query
analysis (See Figure 4). In order to improve the efficiency of
query on Internet, the middleware on Web server, which is
used to connect the Geodatabase, is needed. Figure 5 shows
the result of the query of building attribute
3. EXPERIi
A system prot
developed, a
model througt
construct the
index at datat
client-side do\
while the city
The user can
