A visual analytics approach for exploration of high-dimensional time series based on neighbor-joining tree

Roberto Rodriguez Urquiaga; Ana Maria Cuadros Valdivia; Reynaldo Alfonte Zapana

doi:10.1109/ISSPIT.2017.8388663

A visual analytics approach for exploration of high-dimensional time series based on neighbor-joining tree

Roberto Rodriguez Urquiaga, Ana Maria Cuadros Valdivia, Reynaldo Alfonte Zapana

Resultado de la investigación: Capítulo del libro/informe/acta de congreso › Contribución a la conferencia › revisión exhaustiva

Resumen

High-dimensional time series analysis through visual techniques poses many challenges due to the visualization solutions proposed until now for exploratory tasks are not well-oriented to high volume of data. When the data sets grow large, the visual alternatives do not allow for a good association between similar time series. With the aim to increase more alternatives, we introduce a visual analytic approach based on Neighbor-Joining similarity tree. The proposed approach internally consists of five time series dimension reduction techniques widely used, two well-known similarity measures and interaction mechanisms to do exploratory analysis of high-dimensional time series data interactively.

Idioma original	Inglés
Título de la publicación alojada	2017 IEEE International Symposium on Signal Processing and Information Technology, ISSPIT 2017
Editorial	Institute of Electrical and Electronics Engineers Inc.
Páginas	325-330
Número de páginas	6
ISBN (versión digital)	9781538646625
DOI	https://doi.org/10.1109/ISSPIT.2017.8388663
Estado	Publicada - 18 jun. 2018
Publicado de forma externa	Sí
Evento	17th IEEE International Symposium on Signal Processing and Information Technology, ISSPIT 2017 - Bilbao, Espana Duración: 18 dic. 2017 → 20 dic. 2017

Serie de la publicación

Nombre	2017 IEEE International Symposium on Signal Processing and Information Technology, ISSPIT 2017

Conferencia

Conferencia	17th IEEE International Symposium on Signal Processing and Information Technology, ISSPIT 2017
País/Territorio	Espana
Ciudad	Bilbao
Período	18/12/17 → 20/12/17

Nota bibliográfica

Publisher Copyright:
© 2017 IEEE.

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10.1109/ISSPIT.2017.8388663

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Urquiaga, R. R., Valdivia, A. M. C., & Zapana, R. A. (2018). A visual analytics approach for exploration of high-dimensional time series based on neighbor-joining tree. En 2017 IEEE International Symposium on Signal Processing and Information Technology, ISSPIT 2017 (pp. 325-330). (2017 IEEE International Symposium on Signal Processing and Information Technology, ISSPIT 2017). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ISSPIT.2017.8388663

Urquiaga, Roberto Rodriguez ; Valdivia, Ana Maria Cuadros ; Zapana, Reynaldo Alfonte. / A visual analytics approach for exploration of high-dimensional time series based on neighbor-joining tree. 2017 IEEE International Symposium on Signal Processing and Information Technology, ISSPIT 2017. Institute of Electrical and Electronics Engineers Inc., 2018. pp. 325-330 (2017 IEEE International Symposium on Signal Processing and Information Technology, ISSPIT 2017).

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title = "A visual analytics approach for exploration of high-dimensional time series based on neighbor-joining tree",

abstract = "High-dimensional time series analysis through visual techniques poses many challenges due to the visualization solutions proposed until now for exploratory tasks are not well-oriented to high volume of data. When the data sets grow large, the visual alternatives do not allow for a good association between similar time series. With the aim to increase more alternatives, we introduce a visual analytic approach based on Neighbor-Joining similarity tree. The proposed approach internally consists of five time series dimension reduction techniques widely used, two well-known similarity measures and interaction mechanisms to do exploratory analysis of high-dimensional time series data interactively.",

author = "Urquiaga, {Roberto Rodriguez} and Valdivia, {Ana Maria Cuadros} and Zapana, {Reynaldo Alfonte}",

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Urquiaga, RR, Valdivia, AMC & Zapana, RA 2018, A visual analytics approach for exploration of high-dimensional time series based on neighbor-joining tree. En 2017 IEEE International Symposium on Signal Processing and Information Technology, ISSPIT 2017. 2017 IEEE International Symposium on Signal Processing and Information Technology, ISSPIT 2017, Institute of Electrical and Electronics Engineers Inc., pp. 325-330, 17th IEEE International Symposium on Signal Processing and Information Technology, ISSPIT 2017, Bilbao, Espana, 18/12/17. https://doi.org/10.1109/ISSPIT.2017.8388663

A visual analytics approach for exploration of high-dimensional time series based on neighbor-joining tree. / Urquiaga, Roberto Rodriguez; Valdivia, Ana Maria Cuadros; Zapana, Reynaldo Alfonte.

2017 IEEE International Symposium on Signal Processing and Information Technology, ISSPIT 2017. Institute of Electrical and Electronics Engineers Inc., 2018. p. 325-330 (2017 IEEE International Symposium on Signal Processing and Information Technology, ISSPIT 2017).

Resultado de la investigación: Capítulo del libro/informe/acta de congreso › Contribución a la conferencia › revisión exhaustiva

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PY - 2018/6/18

Y1 - 2018/6/18

N2 - High-dimensional time series analysis through visual techniques poses many challenges due to the visualization solutions proposed until now for exploratory tasks are not well-oriented to high volume of data. When the data sets grow large, the visual alternatives do not allow for a good association between similar time series. With the aim to increase more alternatives, we introduce a visual analytic approach based on Neighbor-Joining similarity tree. The proposed approach internally consists of five time series dimension reduction techniques widely used, two well-known similarity measures and interaction mechanisms to do exploratory analysis of high-dimensional time series data interactively.

AB - High-dimensional time series analysis through visual techniques poses many challenges due to the visualization solutions proposed until now for exploratory tasks are not well-oriented to high volume of data. When the data sets grow large, the visual alternatives do not allow for a good association between similar time series. With the aim to increase more alternatives, we introduce a visual analytic approach based on Neighbor-Joining similarity tree. The proposed approach internally consists of five time series dimension reduction techniques widely used, two well-known similarity measures and interaction mechanisms to do exploratory analysis of high-dimensional time series data interactively.

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M3 - Contribución a la conferencia

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Urquiaga RR, Valdivia AMC, Zapana RA. A visual analytics approach for exploration of high-dimensional time series based on neighbor-joining tree. En 2017 IEEE International Symposium on Signal Processing and Information Technology, ISSPIT 2017. Institute of Electrical and Electronics Engineers Inc. 2018. p. 325-330. (2017 IEEE International Symposium on Signal Processing and Information Technology, ISSPIT 2017). doi: 10.1109/ISSPIT.2017.8388663

A visual analytics approach for exploration of high-dimensional time series based on neighbor-joining tree

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