Big Data Visualisation

Miguel Ángel Esbrí; Eva Klien; Karel Charvát; Christian Zinke-wehlmann; Javier Hitado; Caj Södergård

doi:10.1007/978-3-030-71069-9_13

Big Data Visualisation

Miguel Ángel Esbrí (Corresponding author), Eva Klien, Karel Charvát, Christian Zinke-wehlmann, Javier Hitado, Caj Södergård

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Research output: Chapter in Book/Report/Conference proceeding › Chapter or book article › Scientific › peer-review

Abstract

In this chapter, we introduce the topic of big data visualization with a focus on the challenges related to geospatial data. We present several efficient techniques to address these challenges. We then provide examples from the DataBio project of visualisation solutions. These examples show that there are many technologies and software components available for big data visualisation, but they also point to limitations and the need for further research and development.

Original language	English
Title of host publication	Big Data in Bioeconomy
Subtitle of host publication	Results from the European DataBio Project
Editors	Caj Södergård, Tomas Mildorf, Ephrem Habyarimana, Arne J. Berre, Jose A. Fernandes, Christian Zinke-Wehlmann
Publisher	Springer
Chapter	13
Pages	169-184
ISBN (Electronic)	978-3-030-71069-9
ISBN (Print)	978-3-030-71068-2
DOIs	https://doi.org/10.1007/978-3-030-71069-9_13
Publication status	Published - 14 Aug 2021
MoE publication type	A3 Part of a book or another research book

Keywords

Visualisation graphics
3D
time series
multidimensional
simplification and generalisation
raster and vector tiling
GPUs
web clients
linked open data

Access to Document

10.1007/978-3-030-71069-9_13Licence: CC BY

https://link.springer.com/10.1007/978-3-030-71069-9_13

DataBio: Data-Driven Bioeconomy
Södergård, C. (Manager)
1/01/17 → 31/12/19
Project: EU project

Cite this

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title = "Big Data Visualisation",

abstract = "In this chapter, we introduce the topic of big data visualization with a focus on the challenges related to geospatial data. We present several efficient techniques to address these challenges. We then provide examples from the DataBio project of visualisation solutions. These examples show that there are many technologies and software components available for big data visualisation, but they also point to limitations and the need for further research and development.",

keywords = "Visualisation graphics, 3D, time series, multidimensional, simplification and generalisation, raster and vector tiling, GPUs, web clients, linked open data",

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doi = "10.1007/978-3-030-71069-9_13",

language = "English",

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booktitle = "Big Data in Bioeconomy",

publisher = "Springer",

address = "Germany",

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Big Data Visualisation. / Esbrí, Miguel Ángel (Corresponding author); Klien, Eva; Charvát, Karel et al.
Big Data in Bioeconomy: Results from the European DataBio Project. ed. / Caj Södergård; Tomas Mildorf; Ephrem Habyarimana; Arne J. Berre; Jose A. Fernandes; Christian Zinke-Wehlmann. Springer, 2021. p. 169-184.

Research output: Chapter in Book/Report/Conference proceeding › Chapter or book article › Scientific › peer-review

TY - CHAP

T1 - Big Data Visualisation

AU - Esbrí, Miguel Ángel

AU - Klien, Eva

AU - Charvát, Karel

AU - Zinke-wehlmann, Christian

AU - Hitado, Javier

AU - Södergård, Caj

PY - 2021/8/14

Y1 - 2021/8/14

N2 - In this chapter, we introduce the topic of big data visualization with a focus on the challenges related to geospatial data. We present several efficient techniques to address these challenges. We then provide examples from the DataBio project of visualisation solutions. These examples show that there are many technologies and software components available for big data visualisation, but they also point to limitations and the need for further research and development.

AB - In this chapter, we introduce the topic of big data visualization with a focus on the challenges related to geospatial data. We present several efficient techniques to address these challenges. We then provide examples from the DataBio project of visualisation solutions. These examples show that there are many technologies and software components available for big data visualisation, but they also point to limitations and the need for further research and development.

KW - Visualisation graphics

KW - 3D

KW - time series

KW - multidimensional

KW - simplification and generalisation

KW - raster and vector tiling

KW - GPUs

KW - web clients

KW - linked open data

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DO - 10.1007/978-3-030-71069-9_13

M3 - Chapter or book article

SN - 978-3-030-71068-2

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EP - 184

BT - Big Data in Bioeconomy

A2 - Södergård, Caj

A2 - Mildorf, Tomas

A2 - Habyarimana, Ephrem

A2 - Berre, Arne J.

A2 - Fernandes, Jose A.

A2 - Zinke-Wehlmann, Christian

PB - Springer

ER -

Big Data Visualisation

Abstract

Keywords

Access to Document

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Projects

DataBio: Data-Driven Bioeconomy

Cite this