Full text: The 3rd ISPRS Workshop on Dynamic and Multi-Dimensional GIS & the 10th Annual Conference of CPGIS on Geoinformatics

ISPRS, Vol.34, Part 2W2, “Dynamic and Multi-Dimensional GIS”, Bangkok, May 23-25, 2001 
oriented multi-value spatial data model, using multi-dimension 
analytical approach, we analyze, compare and extract the hidden 
information from spatial data, and to realize the integration of 
data oriented and model oriented analytical approaches. Data 
warehouse means the database can incessantly update data. 
Data warehouse is a new technology used in data storage, 
management and processing from 90’s. It is a management 
supported, decision-making, theme oriented, changed by time 
and long lasting data collection. The main task of data warehouse 
is make procession of standardization, filter, matching, refining, 
mark time, guaranteed quality for source data of different types, 
different structures, different storage formats, different contents in 
distributed databases which located in different places and 
companies. Then integrates, divides, summarize, aggregate, 
forecast, deduct, translate, transform, and image these data of 
distributed database which have different features and different 
format based on need of special task. At last, we make modeling, 
aggregation, adjusting, affirmation for data warehouse and build 
structure query function, and etc. 
Spatial data warehouse includes the abstraction of real world, the 
storage management and data reorganization of collected data, 
and the tool of data-mining, extraction, and analysis based on 
spatial data warehouse. In general, spatial data warehouse is 
composed by four parts: data source, database, spatial data 
warehouse data storage system and spatial data warehouse 
analytical tools. Spatial data warehouse is a special form of data 
warehouse; it is one of the key technologies of Web-GIS. This 
paper mainly studies some key technologies to build infinitely 
variable map scale oriented spatial data warehouse, and the 
function of spatial data index in it. 
2.3 Storage and Management of Internet-based Distributed 
Geographic Data Warehouse 
Mass, multi-source and multi-dimension spatial data brings 
forward a new problem about the management and application of 
spatial data. Traditional database has too many problems to deal 
with integration spatial data in decision, theme, time variation and 
permanence. Facing multiple, heterogeneous features and 
spatio-temporal features of spatial data, spatial data warehouse 
provides efficient approach to manage them. 
Spatial data warehouse technology of Digital Earth provides firm 
basis for building of macro-sophisticated decision support system. 
We can simplify the design and development of system in logic 
and application by using it to build subsystems or functional 
system in Digital Earth, and make it very clear in logic. Therefore, 
we can provide feasible data management scheme for system 
application in Digital Earth. 
2.4 Seamless Integration of Multi-source Data (SIMS) 
The rapid development and extensive application of GIS 
accumulate mass data source. They were stored into different 
data format, and these differences make it very inconvenient to 
use the data. Seamless Integration of Multi-source Data 
technology realizes a special data access mechanism. It is not 
only provides the ability to access different format data directly, 
but also make GIS software has composite analytical function in 
different data source. 
SIMS is an advanced spatial data integrate technology that do 
not need data format transform, and access multi-format data 
directly. SIMS features are listed below: 
• Direct accessing of multi-format data. This is the basic 
function of SIMS. Because of avoiding transformation of 
data format, it is so convenient to use different format data 
• Format independent data integration. When GIS user using 
data, they may not care the storage format of data, realize 
the format independent data integration. 
• Position independent data integration. If we use large 
relational database (e.p. Oracle and SQL Server) to store 
spatial data, the data can be stored in Net Server, even 
Web Server. If we use file to store spatial data, the data 
usually located in local server. Accessing data through 
SIMS, we may not care not only the storage format of data, 
but also the storage position of data. User can operate 
remote data conveniently as local data. 
• Composite analysis of multi-source data. SIMS also allows 
composite spatial analysis of multi-source data. For 
example, user can overlap a land using data set of Arc/Info 
Coverage format on an administrative divisions data set 
stored in SDE. 
2.5 Spatial Database Index 
In digital Earth, mass geographic data change with time, and 
appear diversity in spatio-temporal feature. The information 
generalization of infinitely variable map scale spatial data also 
make these mass data appear diversity with the continuous 
change of scale. Especially in infinitely variable map scale 
oriented spatial data warehouse, we much maintain the union of 
all scales and all themes data set. Thus, there are mass data. 
Traditional spatial data index is based on the static index of 
hierarchical model, and these indexes are all derived from 
traditional database index. This method can only improve the 
efficiency of data searching. Thus, we must use new approach to 
build spatial data index. 
This new approach should have dynamic and complete features. 
Dynamic means we can auto generate index for time dimension, 
geographic dimension and theme dimension based on the 
change of granularity in spatial data warehouse. Complete is to 
build special spatial relations of spatial data based on spatial data 
index, it mainly include topological relationship, geometrical 
relationship and ordinal relation. Dynamic spatial data index 
provides better support for online analysis and datamining, 
especially provides a new approach to information generalization 
of infinitely variable map scale. 
2.6 Spatial Data Engine 
Because Digital Earth mainly related to multi-dimension 
spatio-temporal data, the traditional data warehouse and 
datamining technology cannot satisfy this need. We should 
organize and manage data unitedly through spatial data engine 
based on spatial database, data warehouse and datamining to 
provides high performed searching method and improves 
analyze and aid decision ability. 
Spatial data engine can support spatial data warehouse in spatial 
data organization and data searching. Through research of 
spatial data engine, we can improve searching speed to mass 
spatial information, optimize searching result in infinitely variable 
map scale information generalization, and return searching result 
to end user through a friendly interface. 
Concept model of infinitely variable map scale oriented spatial 
data warehouse is described in figure 1.

Note to user

Dear user,

In response to current developments in the web technology used by the Goobi viewer, the software no longer supports your browser.

Please use one of the following browsers to display this page correctly.

Thank you.