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Conference Paper/Proceeding/Abstract 336 views 56 downloads

Towards Visual Exploration of Large Temporal Datasets / Mark, Jones; Xianghua, Xie

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

Swansesa University Authors: Mark, Jones, Xianghua, Xie

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 2018-12-16T14:58:26.5698811 v2 43563 2018-08-24 Towards Visual Exploration of Large Temporal Datasets 2e1030b6e14fc9debd5d5ae7cc335562 0000-0001-8991-1190 Mark Jones Mark Jones true false b334d40963c7a2f435f06d2c26c74e11 0000-0002-2701-8660 Xianghua Xie Xianghua Xie true false 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 Paper/Proceeding/Abstract 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 NANME Computer Science COLLEGE CODE SCS Swansea University 2018-12-16T14:58:26.5698811 2018-08-24T14:28:44.2662536 College of Science Computer Science Mohammed Ali 1 Mark Jones 0000-0001-8991-1190 2 Xianghua Xie 0000-0002-2701-8660 3 Mark Williams 4 0043563-04092018130958.pdf 2018_TemporalVE.pdf 2018-09-04T13:09:58.6500000 Output 3593637 application/pdf Accepted Manuscript true 2018-11-15T00:00:00.0000000 true eng
title Towards Visual Exploration of Large Temporal Datasets
spellingShingle Towards Visual Exploration of Large Temporal Datasets
Mark, Jones
Xianghua, Xie
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
b334d40963c7a2f435f06d2c26c74e11
author_id_fullname_str_mv 2e1030b6e14fc9debd5d5ae7cc335562_***_Mark, Jones
b334d40963c7a2f435f06d2c26c74e11_***_Xianghua, Xie
author Mark, Jones
Xianghua, Xie
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institution Swansea University
issn 2516-2314
doi_str_mv 10.1109/BDVA.2018.8534025
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hierarchy_parent_title College of Science
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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 2018-10-17T20:03:57Z
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score 10.734507