Similarity-based visual exploration of very large georeferenced multidimensional datasets

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Gonzales G.F.
Salirrosas A.
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Association for Computing Machinery
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Big data visualization is a main task for data analysis. Due to its complexity in terms of volume and variety, very large datasets are unable to be queried for similarities among entries in traditional Database Management Systems. In this paper, we propose an effective approach for indexing millions of elements with the purpose of performing single and multiple visual similarity queries on multidimensional data associated with geographical locations. Our approach makes use of Z-Curve algorithm to map into 1D space considering similarities between data. Additionally, we present a set of results using real data of different sources and we analyze the insights obtained from the interactive exploration.
This work was supported by grant 234-2015-FONDECYT (Master Program) from Cienciactiva of the National Council for Science, Technology and Technological Innovation (CONCYTEC-PERU) and the Coordenação de Aperfeiçoamento de Pessoal de Nível Superior - Brasil (CAPES) - Finance Code 001.
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