Visualization of similarity queries with trajectory estimation in complex data

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Paiva, Claudio Eduardo ; Malaquias Junior, Roseval Donisete ; Bueno, Renato ; Banissi, E ; Khosrow-Shahi, F ; Ursyn, A ; Bannatyne, MWM ; Pires, JM ; Datia, N ; Nazemi, K ; Kovalerchuk, B ; Counsell, J ; Agapiou, A ; Vrcelj, Z ; Chau, HW ; Li, MB ; Nagy, G ; Laing, R ; Francese, R ; Sarfraz, M ; Bouali, F ; Venturini, G ; Trutschl, M ; Cvek, U ; Muller, H ; Nakayama, M ; Temperini, M ; DiMascio, T ; Sciarrone, F ; Rossano, V ; Dorner, R ; Caruccio, L ; Vitiello, A ; Huang, WD ; Risi, M ; Erra, U ; Andonie, R ; Ahmad, MA ; Figueiras, A ; Cuzzocrea, A ; Mabakane, MS

• Tipo de documento: Artigo Científico
• Fonte: 2020 24TH INTERNATIONAL CONFERENCE INFORMATION VISUALISATION (IV 2020); v. N/A, p. 6-pg., 2020-01-01.

Resumo

The information in a database can change over time and requires specific support for operations that process the temporal characteristics of the data. New instances of objects present in the database are inserted frequently, requiring management methods capable of handling temporal information. This paper aims to extend the delimitation techniques for visualization of similarity queries of images using estimates of their trajectories. The study of temporal evolution in complex data can benefit several areas of knowledge, such as agriculture, medicine, civil engineering and others. The use of visualization to understand data changes over time improves the knowledge of the distribution of the dataset, enables the recognition of data patterns more quickly and favors the acquisition of new useful information. In this sense, the visualization of queries with trajectory estimation in a three-dimensional space allows to study how the data are organized in the estimated regions. To perform this visual analysis, we use images at different time points of the same object in the database to estimate what an image of that object would look like at a different time. (AU)

• Processo FAPESP: 16/17078-0 - Mineração, indexação e visualização de Big Data no contexto de sistemas de apoio à decisão clínica (MIVisBD)
• Beneficiário: Agma Juci Machado Traina
• Modalidade de apoio: Auxílio à Pesquisa - Temático