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

Basic information

Original name

Towards Interactive Visualization of Time Series Data to Support Knowledge Discovery

Authors

GÉRYK, Jan (203 Czech Republic, guarantor, belonging to the institution)

Edition

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

Publisher

Springer International Publishing

Other information

Language

English

Type of outcome

Stať ve sborníku

Field of Study

10201 Computer sciences, information science, bioinformatics

Country of publisher

Portugal

Confidentiality degree

není předmětem státního či obchodního tajemství

Publication form

printed version "print"

Impact factor

Impact factor: 0.402 in 2005

RIV identification code

RIV/00216224:14330/15:00083876

Organization unit

Faculty of Informatics

ISBN

978-3-319-23484-7

ISSN

DOI

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

UT WoS

000363570000057

Keywords in English

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

Abstract

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, p. 578-583. ISBN 978-3-319-23484-7. Available from: 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, p.~578-583. ISBN~978-3-319-23484-7. Available from: https://dx.doi.org/10.1007/978-3-319-23485-4\_{}57.

Detailed Information on Publication Record