Micro-zone analysis involves use of data fusion and data mining techniques in order to understand the relative impact of many different variables. Data Fusion requires the ability to combine or “fuse” date from multiple data sources. Data mining involves the application of sophisticated algorithms such as Neural Networks and Decision Trees, to describe micro-zone behavior and predict future values based upon past values. One of the difficulties encountered in developing generic time series or other data mining techniques for micro-zone analysis is the wide variability of the data sets available for analysis. This presents challenges all the way from the data gathering stage to results presentation. This paper presents an architecture designed and used to facilitate the collection of disparate data sets well suited for data fusion and data mining. Results show this architecture provides a flexible, dynamic framework for the capture and storage of a myriad of dissimilar data sets and can serve as a foundation from which to build a complete data fusion architecture.
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