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^*
, Eva Klien
, Karel Charvát
, Christian Zinke-wehlmann
, Javier Hitado
, Caj Södergård

^*Corresponding author for this work

BA64 Safe and connected society

ATOS IT Solutions and Services Iberia (ATOS)
Fraunhofer Institute for Computer Graphics Research (IGD)
Lesprojekt služby s.r.o.
Leipzig University

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
Number of pages	16
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

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",

author = "Esbr{\'i}, \{Miguel {\'A}ngel\} and Eva Klien and Karel Charv{\'a}t and Christian Zinke-wehlmann and Javier Hitado and Caj S{\"o}derg{\aa}rd",

year = "2021",

month = aug,

day = "14",

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

language = "English",

isbn = "978-3-030-71068-2",

pages = "169--184",

editor = "Caj S{\"o}derg{\aa}rd and Tomas Mildorf and Ephrem Habyarimana and Berre, \{Arne J.\} and Fernandes, \{Jose A.\} and Christian Zinke-Wehlmann",

booktitle = "Big Data in Bioeconomy",

publisher = "Springer",

address = "Germany",

}

Big Data Visualisation. / Esbrí, Miguel Ángel; 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

UR - https://www.scopus.com/pages/publications/105003347225

U2 - 10.1007/978-3-030-71069-9_13

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

M3 - Chapter or book article

SN - 978-3-030-71068-2

SP - 169

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

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DataBio: Data-Driven Bioeconomy

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Big Data Visualisation

Abstract

Keywords

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Projects

DataBio: Data-Driven Bioeconomy

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