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Towards Visual Exploration of Large Temporal Datasets / Mohammed Ali; Mark W. Jones; Xianghua Xie; Mark Williams

In 2018 International Symposium on Big Data Visual Analytics (BDVA) 2018

Swansea University Author: Jones, Mark

Abstract

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 pre...

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Published in: In 2018 International Symposium on Big Data Visual Analytics (BDVA) 2018
ISSN: 2516-2314
Published: Konstanz, Germany 2018
Online Access: Check full text

URI: https://cronfa.swan.ac.uk/Record/cronfa43563
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Abstract: 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.
College: College of Science