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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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