A Proposed Data Fusion Architecture for Micro-Zone Analysis and Data Mining

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Data Fusion requires the ability to combine or “fuse” date from multiple data sources. Time Series Analysis is a data mining technique used to predict future values from a data set based upon past values. Unlike other data mining techniques, however, Time Series places special emphasis on periodicity and how seasonal and other time-based factors tend to affect trends over time. One of the difficulties encountered in developing generic time series techniques 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 ... continued below

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McCarthy, Kevin & Manic, Milos August 1, 2012.

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Data Fusion requires the ability to combine or “fuse” date from multiple data sources. Time Series Analysis is a data mining technique used to predict future values from a data set based upon past values. Unlike other data mining techniques, however, Time Series places special emphasis on periodicity and how seasonal and other time-based factors tend to affect trends over time. One of the difficulties encountered in developing generic time series techniques 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 to Time Series analysis as well as other predictive data mining techniques. 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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  • 5th International Symposium on Resilient Control Systems,Salt Lake City, Utah,08/14/2012,08/16/2012

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  • Report No.: INL/CON-12-26599
  • Grant Number: DE-AC07-05ID14517
  • Office of Scientific & Technical Information Report Number: 1055985
  • Archival Resource Key: ark:/67531/metadc829630

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  • August 1, 2012

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  • May 19, 2016, 9:45 a.m.

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  • Nov. 30, 2016, 7:01 p.m.

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McCarthy, Kevin & Manic, Milos. A Proposed Data Fusion Architecture for Micro-Zone Analysis and Data Mining, article, August 1, 2012; Idaho Falls, Idaho. (digital.library.unt.edu/ark:/67531/metadc829630/: accessed October 15, 2018), University of North Texas Libraries, Digital Library, digital.library.unt.edu; crediting UNT Libraries Government Documents Department.