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doi: 10.6062/jcis.2009.01.02.0021(Free PDF)

Thalita B. Veronese, Reinaldo R. Rosa, Nandamudi L. Vijaykumar and Mauricio J. A. Bolzan

Analysis of information from multiple data sources obtained through high resolution instrumental measurements has become a fundamental task in all scientific areas. The development of expert methods able to treat such multi-source data systems, with both large variability and measurement extension, is a key for studying complex scientific phenomena, especially those related to systemic analysis in space and environmental sciences. In this paper, we propose a time series generalization introducing the concept of generalized numerical lattice, which represents a discrete sequence of temporal measures for a given variable. In this novel representation approach each generalized numerical lattice brings post-analytical data information. We define a generalized numerical lattice £ as a set of three coefficients(?, ?` , µp), representing the following data properties: dimensionality, size and post-analytical parameters, respectively. From this generalization, any multi-source database can be reduced to a closed set of classified time series in spatio-temporal generalized dimensions. As a case study, we show a preliminary application in space science data, highlighting the possibility of a real time analysis expert system to be developed in a future work.

Multivariate time series, complex data systems, data representation, data integration, data modeling, data mining, systemic analysis.

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