Data analysis based on spatial and temporal relationships leads to new knowledge discovery in multi-database environments. As various and almost infinite relationships are potentially existing among heterogeneous databases, it is important to realize an objective-based dynamic data analysis environment with appropriate data collection from selected databases.

These databases are integrated within a meso database which combines the data from different databases into one redundant data store. Typically the data store consists of a number of data cubes. These cubes are recharged whenever micro data changes depending on a recharge policy. The meso database is then use for population of the analysis databases which contains data according to the analysis demands. After application of analysis or data mining functions the result presentation database is populated.

We develop a novel approach to data analysis by turning topsy-turvy the analysis task. The analysis task drives the features of the data collectors. These collectors are small databases which collect data within their interest profile. Data from the collector databases are then used for the presentation database.

The feature of this approach is to realize dynamic data integration and analysis among heterogeneous databases by computing spatial and temporal interrelationships objective-dependently, and such integration realizes to retrieve, analyse and extract new information generated with a viewpoint of spatial and temporal occurrences among legacy databases.