Towards Interactive Visualization of Time Series Data to
Support Knowledge Discovery

D 2015

Towards Interactive Visualization of Time Series Data to Support Knowledge Discovery

GÉRYK, Jan

Základní údaje

Originální název

Towards Interactive Visualization of Time Series Data to Support Knowledge Discovery

Autoři

GÉRYK, Jan (203 Česká republika, garant, domácí)

Vydání

Portugal, Progress in Artificial Intelligence - 17th Portuguese Conference on Artificial Inteligence - EPIA 2015, od s. 578-583, 6 s. 2015

Nakladatel

Springer International Publishing

Další údaje

Jazyk

angličtina

Typ výsledku

Stať ve sborníku

Obor

10201 Computer sciences, information science, bioinformatics

Stát vydavatele

Portugalsko

Utajení

není předmětem státního či obchodního tajemství

Forma vydání

tištěná verze "print"

Impakt faktor

Impact factor: 0.402 v roce 2005

Kód RIV

RIV/00216224:14330/15:00083876

Organizační jednotka

Fakulta informatiky

ISBN

978-3-319-23484-7

ISSN

DOI

http://dx.doi.org/10.1007/978-3-319-23485-4_57

UT WoS

000363570000057

Klíčová slova anglicky

animation;motion charts;visual analytics;academic analytics;experiment

Příznaky

Mezinárodní význam, Recenzováno

Změněno: 28. 4. 2016 14:48, RNDr. Pavel Šmerk, Ph.D.

Anotace

V originále

Higher education institutions have a significant interest in increasing the educational quality and effectiveness. A major challenge in modern education is the large amount of time-dependent data, which requires efficient tools and methods to provide efficient decision making. Methods like motion charts (MC) show changes over time by presenting animations in two-dimensional space and by changing element appearances. In this paper, we present a visual analytics tool which makes use of enhanced animated data visualization methods. The tool is primarily designed for exploratory analysis of academic analytics (AA) and offers several interactive visualization methods that enhance the MC design. AA is the business intelligence term used in academic settings and particularly facilitates creation of actionable intelligence to enhance learning and improve student retention. We evaluate the usefulness and the general applicability of the tool with a controlled experiment to assess the efficacy of described methods. To interpret the experiment results, we utilized one-way repeated measures ANOVA.

Citovat

GÉRYK, Jan. Towards Interactive Visualization of Time Series Data to Support Knowledge Discovery. In Francisco Pereira, Penousal Machado, Ernesto Costa, Amílcar Cardoso. Progress in Artificial Intelligence - 17th Portuguese Conference on Artificial Inteligence - EPIA 2015. Portugal: Springer International Publishing, 2015, s. 578-583. ISBN 978-3-319-23484-7. Dostupné z: https://dx.doi.org/10.1007/978-3-319-23485-4_57.

@inproceedings{1311274,
   author = {Géryk, Jan},
   address = {Portugal},
   booktitle = {Progress in Artificial Intelligence - 17th Portuguese Conference on Artificial Inteligence - EPIA 2015},
   doi = {http://dx.doi.org/10.1007/978-3-319-23485-4_57},
   editor = {Francisco Pereira, Penousal Machado, Ernesto Costa, Amílcar Cardoso},
   keywords = {animation;motion charts;visual analytics;academic analytics;experiment},
   howpublished = {tištěná verze "print"},
   language = {eng},
   location = {Portugal},
   isbn = {978-3-319-23484-7},
   pages = {578-583},
   publisher = {Springer International Publishing},
   title = {Towards Interactive Visualization of Time Series Data to Support Knowledge Discovery},
   year = {2015}
}

TY  - JOUR
ID  - 1311274
AU  - Géryk, Jan
PY  - 2015
TI  - Towards Interactive Visualization of Time Series Data to Support Knowledge Discovery
PB  - Springer International Publishing
CY  - Portugal
SN  - 9783319234847
KW  - animation;motion charts;visual analytics;academic analytics;experiment
N2  - Higher education institutions have a significant interest in increasing the educational quality and effectiveness. A major challenge in modern education is the large amount of time-dependent data, which requires efficient tools and methods to provide efficient decision making. Methods like motion charts (MC) show changes over time by presenting animations in two-dimensional space and by changing element appearances. In this paper, we present a visual analytics tool which makes use of enhanced animated data visualization methods. The tool is primarily designed for exploratory analysis of academic analytics (AA) and offers several interactive visualization methods that enhance the MC design. AA is the business intelligence term used in academic settings and particularly facilitates creation of actionable intelligence to enhance learning and improve student retention. We evaluate the usefulness and the general applicability of the tool with a controlled experiment to assess the efficacy of described methods. To interpret the experiment results, we utilized one-way repeated measures ANOVA.
ER  -

GÉRYK, Jan. Towards Interactive Visualization of Time Series Data to Support Knowledge Discovery. In Francisco Pereira, Penousal Machado, Ernesto Costa, Amílcar Cardoso. \textit{Progress in Artificial Intelligence - 17th Portuguese Conference on Artificial Inteligence - EPIA 2015}. Portugal: Springer International Publishing, 2015, s.~578-583. ISBN~978-3-319-23484-7. Dostupné z: https://dx.doi.org/10.1007/978-3-319-23485-4\_{}57.

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