| Grant number: | 13/50379-6 |
| Support Opportunities: | Regular Research Grants |
| Start date: | November 01, 2013 |
| End date: | October 31, 2015 |
| Field of knowledge: | Physical Sciences and Mathematics - Computer Science |
| Agreement: | Consortium of Alberta, Laval, Dalhousie and Ottawa (CALDO) |
| Principal Investigator: | Gustavo Enrique de Almeida Prado Alves Batista |
| Grantee: | Gustavo Enrique de Almeida Prado Alves Batista |
| Principal researcher abroad: | Stan Matwin |
| Institution abroad: | Dalhousie University, Halifax, Canada |
| Host Institution: | Instituto de Ciências Matemáticas e de Computação (ICMC). Universidade de São Paulo (USP). São Carlos , SP, Brazil |
| Associated research grant: | 12/07295-3 - Complexity-invariance for classification, clustering and motif discovery in time series, AP.R |
Abstract
We propose a focused, interdisciplinary research project on data mining and data visualization with a specific focus on marine data. This data is particularly challenging for data mining as it presents only a very sparse set of data points with respect to the volume of the marine space that is being modelled and investigated. It also presents a set of challenges in visualization of data and of modelling results, as the data are inherently three-dimensional and from an unfamiliar context relative to data from on land. We will work in an interdisciplinary team with researchers in data mining, data visualization, and marine biology to develop visualization methods that will be appropriate for marine biology applications of data mining. The data may be derived from multiple disparate sources, including fisheries or scientific surveys, autonomous sensors, satellite data or field studies. For model outputs, we will particularly work on the visualization of results from a new generation of ecosystem model, analogous to the general circulation models used to predict global climate. This model includes ali organism types on both land and sea. We face the challenge that it can produce gigabytes to terabytes of outputs, including tracking ali organism interactions, individual states, and the spatial distribution of individuais. Thus we need to summarize, extract, and visualize outputs at multiple scales including that of individuais, ecological communities, and the globe. These data need to be visualized in a manner that will then be useful and interpretable for the international policy community. (AU)
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