E-Thesis 16 views 11 downloads
Visual Analysis of Large, Time-Dependent, Multi-Dimensional Smart Sensor Tracking Data / James Walker
Swansea University Author: Walker, James
PDF | ETRVOADownload (18.93MB)
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...
|Supervisor:||Mark W. Jones|
|Sponsors / Funders:||EPSRC|
No Tags, Be the first to tag this record!
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.
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.
data, temporal patterns
College of Science