ContiMap: Continuous Heatmap for Large Time Series Data

Vung Pham, Ngan Nguyen, Tommy Dang

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

Limited human cognitive load, limited computing resources, and finite display resolutions are the major obstacles for developing interactive visualization systems in large-scale data analysis. Recent technological innovation has significantly improved computing power, such as faster CPUs and GPUs, as well as display resources, including ultra-high-resolution displays and video walls. However, large and complex data is still ahead in the run as we are generating huge amounts of data daily. Our strategy to bridge these gaps is to present the right amount of information through the use of compelling graphics. This paper proposes an approximation algorithm and a web prototype for representing a high-level abstraction of time series based on heatmap designs. Our approach aims to handle a significant amount of time series data arising from various application domains, such as cybersecurity, sensor network, and gene expression analysis.

Original languageEnglish
Title of host publicationProceedings - 2020 IEEE Visualization in Data Science, VDS 2020
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages42-51
Number of pages10
ISBN (Electronic)9781728192840
DOIs
StatePublished - Oct 2020
Event2020 IEEE Visualization in Data Science, VDS 2020 - Virtual, Salt Lake City, United States
Duration: Oct 26 2020 → …

Publication series

NameProceedings - 2020 IEEE Visualization in Data Science, VDS 2020

Conference

Conference2020 IEEE Visualization in Data Science, VDS 2020
CountryUnited States
CityVirtual, Salt Lake City
Period10/26/20 → …

Keywords

  • Continuous heatmap
  • approximation algorithm
  • multivariate data analysis
  • time-series visualization

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