Apoala Project

Updated July 2

System Architecture
The system architecture for the Apoala project consists of the following three interrelated modules: a temporal GIS data management system known as Tempest, a data mining software package known as AutoClass, and a set of Geographic Visualization (geovisualization) tools developed in IBM Data Explorer and Tcl/TK. The goal of the database management system is quick retrieval and updating of very large data sets using parallel processing and other high-performance computing techniques. Data mining is generally the use of statistical techniques supporting unsupervised classification of very large data sets in an attempt to find patterns that would not otherwise be discernable. Geographic visualization techniques, in contrast to data mining, display data in a way that allows a domain expert to take advantage of the nature of human visual processes to find patterns.

The challenge that we are addressing is moving data from one module to another in a way that is simple and invisible to the user. For instance, a synoptic climatologist may examine data from daily weather station readings throughout the Susquehanna drainage basin when looking for patterns in high-intensity rainfall events. One possible approach to this problem would be to use data mining software to classify the data. In order to run this classification, the data would need to be preprocesses to select the appropriate subset. Once the classification has been run it can be visualized in order to understand what variables play a significant role in the classification, and where and when cases classified into a certain group are likely to occur. This may lead to the development of hypotheses that can be tested through the collection of other data, or can be explored through further examination of existing data.

In the example given above there is a very simple flow of data from the database to the data mining software, and finally to the visualization module. In reality the flow may be much more complex. A visualization of the original data set may lead to the extraction of a subset of data that is then mined. The mining process, which is iterative, may be interactively visualized, and the results may be stored as a final product or facilitate further visualization, database queries, or mining. The challenge, then, is creating a stable environment within which this complex interaction is, for the most part, invisible to the user so he or she may focus on interpreting the data.

The database management system that we are using in Apoala is based on the triad database model developed and implemented in previous research as a Temporal GIS system called Tempest.

Tempest provides extremely fast access to data using a number of tools including linear and cyclical queries.

It also provides a number of simple summary statistics for spatial and/or temporal subsets of the data.

Copyright © 1998-2000 Pennsylvania State University


Updated July 2

	System Architecture The system architecture for the Apoala project consists of the following three interrelated modules: a temporal GIS data management system known as Tempest, a data mining software package known as AutoClass, and a set of Geographic Visualization (geovisualization) tools developed in IBM Data Explorer and Tcl/TK. The goal of the database management system is quick retrieval and updating of very large data sets using parallel processing and other high-performance computing techniques. Data mining is generally the use of statistical techniques supporting unsupervised classification of very large data sets in an attempt to find patterns that would not otherwise be discernable. Geographic visualization techniques, in contrast to data mining, display data in a way that allows a domain expert to take advantage of the nature of human visual processes to find patterns. The challenge that we are addressing is moving data from one module to another in a way that is simple and invisible to the user. For instance, a synoptic climatologist may examine data from daily weather station readings throughout the Susquehanna drainage basin when looking for patterns in high-intensity rainfall events. One possible approach to this problem would be to use data mining software to classify the data. In order to run this classification, the data would need to be preprocesses to select the appropriate subset. Once the classification has been run it can be visualized in order to understand what variables play a significant role in the classification, and where and when cases classified into a certain group are likely to occur. This may lead to the development of hypotheses that can be tested through the collection of other data, or can be explored through further examination of existing data. In the example given above there is a very simple flow of data from the database to the data mining software, and finally to the visualization module. In reality the flow may be much more complex. A visualization of the original data set may lead to the extraction of a subset of data that is then mined. The mining process, which is iterative, may be interactively visualized, and the results may be stored as a final product or facilitate further visualization, database queries, or mining. The challenge, then, is creating a stable environment within which this complex interaction is, for the most part, invisible to the user so he or she may focus on interpreting the data. The database management system that we are using in Apoala is based on the triad database model developed and implemented in previous research as a Temporal GIS system called Tempest. Tempest provides extremely fast access to data using a number of tools including linear and cyclical queries. It also provides a number of simple summary statistics for spatial and/or temporal subsets of the data.


	Copyright © 1998-2000 Pennsylvania State University