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Visual Analysis of Large, Time-Dependent, Multi-Dimensional Smart Sensor Tracking Data / James Walker

Swansea University Author: James Walker

DOI (Published version): 10.23889/SUthesis.36342

Abstract

Technological advancements over the past decade have increased our ability to collect data to previously unimaginable volumes [Kei02]. Understanding temporal patterns is key to gaining knowledge and insight. However, our capacity to store data now far exceeds the rate at which we are able to underst...

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Published: 2017
Institution: Swansea University
Degree level: Doctoral
Degree name: Ph.D
URI: https://cronfa.swan.ac.uk/Record/cronfa36342
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first_indexed 2017-10-27T19:15:54Z
last_indexed 2019-10-21T14:20:48Z
id cronfa36342
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spelling 2018-06-25T12:47:46.2428383 v2 36342 2017-10-27 Visual Analysis of Large, Time-Dependent, Multi-Dimensional Smart Sensor Tracking Data cb1de536adbd7ea9448997d205c6bfc6 NULL James Walker James Walker true true 2017-10-27 Technological advancements over the past decade have increased our ability to collect data to previously unimaginable volumes [Kei02]. Understanding temporal patterns is key to gaining knowledge and insight. However, our capacity to store data now far exceeds the rate at which we are able to understand it [KKEM10]. This phenomenon has led to a growing need for advanced solutions to make sense and use of an ever-increasing data space. Abstract temporal data provides additional challenges in its, representation, size, and scalability, high dimensionality, and unique structure.One instance of such temporal data is acquired from smart sensor tags attached to freely roaming animals recording multiple parameters at infra-second rates which are becoming commonplace, and are transforming biologists understanding of the way wild animals behave.The excitement at the potential inherent in sophisticated tracking devices has, however, been limited by a lack of available software to advance research in the field. This thesis introduces methodologies to deal with the problem of the analysis of the large, multi-dimensional, time-dependent data acquired. Interpretation of such data is complex and currently limits the ability of biologists to realise the value of their recorded information.We present several contributions to the field of time-series visualisation, that is, the visualisation of ordered collections of real value data attributes at successive points in time sampled at uniform time intervals. Traditionally, time-series graphs have been used for temporal data. However, screen resolution is small in comparison to the large information space commonplace today. In such cases, we can only render a proportion of the data.It is widely accepted that the effective interpretation of large temporal data sets requires advanced methods and interaction techniques. In this thesis, we address these issues to enhance the exploration, analysis, and presentation of time-series data for movement ecologists in their smart sensor data analysis. E-Thesis data, temporal patterns 31 12 2017 2017-12-31 10.23889/SUthesis.36342 A selection of third party content is redacted or is partially redacted from this thesis. Figs. 2.10 & 2.11 page 31. Figs. 2.12 & 2.13 page 32. COLLEGE NANME Computer Science COLLEGE CODE Swansea University Doctoral Ph.D EPSRC 2018-06-25T12:47:46.2428383 2017-10-27T17:29:32.9287263 Faculty of Science and Engineering School of Mathematics and Computer Science - Computer Science James Walker NULL 1 0036342-27102017173037.pdf Walker_James_final_thesis_Redacted.pdf 2017-10-27T17:30:37.7000000 Output 19705196 application/pdf Redacted version - open access true 2017-10-27T00:00:00.0000000 true
title Visual Analysis of Large, Time-Dependent, Multi-Dimensional Smart Sensor Tracking Data
spellingShingle Visual Analysis of Large, Time-Dependent, Multi-Dimensional Smart Sensor Tracking Data
James Walker
title_short Visual Analysis of Large, Time-Dependent, Multi-Dimensional Smart Sensor Tracking Data
title_full Visual Analysis of Large, Time-Dependent, Multi-Dimensional Smart Sensor Tracking Data
title_fullStr Visual Analysis of Large, Time-Dependent, Multi-Dimensional Smart Sensor Tracking Data
title_full_unstemmed Visual Analysis of Large, Time-Dependent, Multi-Dimensional Smart Sensor Tracking Data
title_sort Visual Analysis of Large, Time-Dependent, Multi-Dimensional Smart Sensor Tracking Data
author_id_str_mv cb1de536adbd7ea9448997d205c6bfc6
author_id_fullname_str_mv cb1de536adbd7ea9448997d205c6bfc6_***_James Walker
author James Walker
author2 James Walker
format E-Thesis
publishDate 2017
institution Swansea University
doi_str_mv 10.23889/SUthesis.36342
college_str Faculty of Science and Engineering
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hierarchy_top_id facultyofscienceandengineering
hierarchy_top_title Faculty of Science and Engineering
hierarchy_parent_id facultyofscienceandengineering
hierarchy_parent_title Faculty of Science and Engineering
department_str School of Mathematics and Computer Science - Computer Science{{{_:::_}}}Faculty of Science and Engineering{{{_:::_}}}School of Mathematics and Computer Science - Computer Science
document_store_str 1
active_str 0
description Technological advancements over the past decade have increased our ability to collect data to previously unimaginable volumes [Kei02]. Understanding temporal patterns is key to gaining knowledge and insight. However, our capacity to store data now far exceeds the rate at which we are able to understand it [KKEM10]. This phenomenon has led to a growing need for advanced solutions to make sense and use of an ever-increasing data space. Abstract temporal data provides additional challenges in its, representation, size, and scalability, high dimensionality, and unique structure.One instance of such temporal data is acquired from smart sensor tags attached to freely roaming animals recording multiple parameters at infra-second rates which are becoming commonplace, and are transforming biologists understanding of the way wild animals behave.The excitement at the potential inherent in sophisticated tracking devices has, however, been limited by a lack of available software to advance research in the field. This thesis introduces methodologies to deal with the problem of the analysis of the large, multi-dimensional, time-dependent data acquired. Interpretation of such data is complex and currently limits the ability of biologists to realise the value of their recorded information.We present several contributions to the field of time-series visualisation, that is, the visualisation of ordered collections of real value data attributes at successive points in time sampled at uniform time intervals. Traditionally, time-series graphs have been used for temporal data. However, screen resolution is small in comparison to the large information space commonplace today. In such cases, we can only render a proportion of the data.It is widely accepted that the effective interpretation of large temporal data sets requires advanced methods and interaction techniques. In this thesis, we address these issues to enhance the exploration, analysis, and presentation of time-series data for movement ecologists in their smart sensor data analysis.
published_date 2017-12-31T03:45:25Z
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score 11.016258

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