Similarity-based visual exploration of very large georeferenced multidimensional datasets Peralta-Aranibar R. es_PE Comba J.L.D. es_PE Pahins C.A.L. es_PE Gomez-Nieto E. es_PE 2024-05-30T23:13:38Z 2024-05-30T23:13:38Z 2019
dc.description 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.
dc.description.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.
dc.description.sponsorship Consejo Nacional de Ciencia, Tecnología e Innovación Tecnológica - Concytec
dc.identifier.scopus 2-s2.0-85065639857
dc.language.iso eng
dc.publisher Association for Computing Machinery
dc.relation.ispartof Proceedings of the ACM Symposium on Applied Computing
dc.rights info:eu-repo/semantics/openAccess
dc.subject Visualization
dc.subject Effective approaches es_PE
dc.subject Geographic information es_PE
dc.subject Geographical locations es_PE
dc.subject Interactive exploration es_PE
dc.subject Interactive visualizations es_PE
dc.subject Multi-dimensional datasets es_PE
dc.subject Multidimensional data es_PE
dc.subject Similarity es_PE
dc.subject Data visualization es_PE
dc.title Similarity-based visual exploration of very large georeferenced multidimensional datasets
dc.type info:eu-repo/semantics/conferenceObject