Mid Sweden University

miun.sePublications
System disruptions
We are currently experiencing disruptions on the search portals due to high traffic. We are working to resolve the issue, you may temporarily encounter an error message.
Change search
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf
PixVid: Capturing Temporal Correlated Changes in Time Series
Donghua Univ, Dept Comp Sci & Technol, Shanghai, Peoples R China.
Mid Sweden University, Faculty of Science, Technology and Media, Department of Information Systems and Technology. (Communication Systems and Network (CSN))
Donghua Univ, Dept Comp Sci & Technol, Shanghai, Peoples R China.
Mid Sweden University, Faculty of Science, Technology and Media, Department of Information Systems and Technology.
2017 (English)In: Proceedings - 5th International Conference on Advanced Cloud and Big Data, CBD 2017, 2017, p. 337-342, article id 8026960Conference paper, Published paper (Refereed)
Abstract [en]

Time series is one of the main research domains in variety of disciplines. Visualization is an important mechanism to present the raw data as well as the processed time series for further analysis. Many successful visualization techniques have been reported recently. However, most of these techniques display data statically, intending to show as much information as possible by one image or plot. We propose PixVid, a visualization technique which orders the dimensions by constructing a hierarchal dimension cluster tree, and then uses a pixel-oriented technique to form images and displays the data in video format.

Place, publisher, year, edition, pages
2017. p. 337-342, article id 8026960
Series
International Conference on Advanced Cloud and Big Data, ISSN 2573-301X
Keywords [en]
Data Visualisation, Big Data
National Category
Computer Sciences Computer Engineering
Identifiers
URN: urn:nbn:se:miun:diva-31487DOI: 10.1109/CBD.2017.65ISI: 000426950400057Scopus ID: 2-s2.0-85031717526Local ID: STCISBN: 978-1-5386-1072-5 (print)OAI: oai:DiVA.org:miun-31487DiVA, id: diva2:1136373
Conference
The Fifth International Conference on Advanced Cloud and Big Data, CBD, August 13-16, 2017, Shanghai, China
Available from: 2017-08-28 Created: 2017-08-28 Last updated: 2018-04-03Bibliographically approved

Open Access in DiVA

No full text in DiVA

Other links

Publisher's full textScopus

Authority records

Lavassani, MehrzadZhang, Tingting

Search in DiVA

By author/editor
Lavassani, MehrzadZhang, Tingting
By organisation
Department of Information Systems and Technology
Computer SciencesComputer Engineering

Search outside of DiVA

GoogleGoogle Scholar

doi
isbn
urn-nbn

Altmetric score

doi
isbn
urn-nbn
Total: 396 hits
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf