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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
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URI: https://cronfa.swan.ac.uk/Record/cronfa43563
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first_indexed 2018-08-24T19:49:08Z
last_indexed 2018-12-16T19:58:02Z
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spelling 2018-12-16T14:58:26Z v2 43563 2018-08-24 Towards Visual Exploration of Large Temporal Datasets Mark Jones Mark Jones true 0000-0001-8991-1190 false 2e1030b6e14fc9debd5d5ae7cc335562 dda0c29127c698255a4c2b822dd94125 uiPdnV+XNibOpUxFjI3lXQgr5y2nBRz3haj4DmVVDsQ= 2018-08-24 SCS 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. Conference contribution In 2018 International Symposium on Big Data Visual Analytics (BDVA) 2018 Konstanz, Germany 2516-2314 17 10 2018 2018-10-17 10.1109/BDVA.2018.8534025 College of Science Computer Science CSCI SCS Visual Computing None 2018-12-16T14:58:26Z 2018-08-24T14:28:44Z College of Science Computer Science Mohammed Ali 1 Mark W. Jones 2 Xianghua Xie 3 Mark Williams 4 0043563-04092018130958.pdf 2018_TemporalVE.pdf 2018-09-04T13:09:58Z Output 3593637 application/pdf AM true Updated Embargo 13/12/2018 2018-11-15T00:00:00 true eng
title Towards Visual Exploration of Large Temporal Datasets
spellingShingle Towards Visual Exploration of Large Temporal Datasets
Jones, Mark
title_short Towards Visual Exploration of Large Temporal Datasets
title_full Towards Visual Exploration of Large Temporal Datasets
title_fullStr Towards Visual Exploration of Large Temporal Datasets
title_full_unstemmed Towards Visual Exploration of Large Temporal Datasets
title_sort Towards Visual Exploration of Large Temporal Datasets
author_id_str_mv 2e1030b6e14fc9debd5d5ae7cc335562
author_id_fullname_str_mv 2e1030b6e14fc9debd5d5ae7cc335562_***_Jones, Mark
author Jones, Mark
author2 Mohammed Ali
Mark W. Jones
Xianghua Xie
Mark Williams
format Conference contribution
container_title In 2018 International Symposium on Big Data Visual Analytics (BDVA) 2018
publishDate 2018
institution Swansea University
issn 2516-2314
doi_str_mv 10.1109/BDVA.2018.8534025
college_str College of Science
hierarchytype
hierarchy_top_id collegeofscience
hierarchy_top_title College of Science
hierarchy_parent_id collegeofscience
hierarchy_parent_title College of Science
department_str Computer Science{{{_:::_}}}College of Science{{{_:::_}}}Computer Science
document_store_str 1
active_str 1
researchgroup_str Visual Computing
description 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.
published_date 2018-10-17T05:08:48Z
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score 10.860162