Towards Visual Exploration of Large Temporal Datasets

Mohammed Ali, Mark W. Jones, Xianghua Xie, Edgar Williams

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

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We address the problem of visualizing and interacting with large multi-dimensional time-series data. We propose a visual analytics system and approach which aims to visualize, analyze, present and enable exploration of large temporal datasets. Our approach consists of three main stages which are preprocessing, dimensionality reduction, and visual exploration. It assists with finding the interesting features in the data which are often obscured in the line chart because of the visual compression that is required to render the large dataset to screen. Our approach helps to obtain an overview of the entire dataset and track changes over time. It enables the user to detect clusters and outliers and observe the transitions between data. The juxtaposed views are used to visualize and interact both with raw time series data and projected data. Different time series datasets are deployed on our system, and we demonstrate the utility and evaluate the results using a case study with two different datasets which show the effectiveness of our system.
Original languageEnglish
Title of host publication2018 International Symposium on Big Data Visual Analytics (BDVA) 2018
Place of PublicationKonstanz, Germany
Publication statusPublished - 17 Oct 2018
Event4th International Symposium on Big Data Visual and Immersive Analytics - University of Konstanz , Konstanz , Germany
Duration: 17 Oct 201819 Oct 2018
Conference number: 4


Conference4th International Symposium on Big Data Visual and Immersive Analytics
Abbreviated titleBDVA'18


  • 2D Projection
  • Clusters
  • Exploration
  • PCA
  • Time series data
  • Time series graphs
  • visual analytics


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