Similarity-based visual exploration of very large georeferenced multidimensional datasets
Authors: 
Roger Peralta-Aranibar, Cicero A. L. Pahins, Joao L. D. Comba, Erick Gomez-NietoAuthors Info & Claims
Roger Peralta-Aranibar, Cicero A. L. Pahins, Joao L. D. Comba, Erick Gomez-NietoAuthors Info & ClaimsPages 683 - 686
Abstract
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.
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- Similarity-based visual exploration of very large georeferenced multidimensional datasets
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April 2019
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Published: 08 April 2019
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