A_Visual_Recommender_Framework_for_Exploratory_Data_Analytics.pdf (1.91 MB)
A Visual Recommender Framework for Exploratory Data Analytics
presentationposted on 2019-05-15, 00:18 authored by Alan Tan, Yue Lin, Ralf Gommers
Information on our surrounding environment, such as the forests, soil and climate, have been captured extensively throughout New Zealand and across the years for various research studies. While numerous studies have looked at analysing subsets of the data, few have looked at the data from a holistic perspective. This is partly due to difficulties in handling the data size, complexity and quality and the lack of computational tools required for understanding and analysing the data in its entirety. In this talk, we briefly introduce our work on designing a framework for exploring large complex spatio-temporal datasets. We show how the designed framework can help facilitate efficient visual exploration of large complex spatio-temporal datasets.
ABOUT THE AUTHORS
Dr Alan Tan is currently a Data Scientist at Scion. His work focuses on the analysis and modelling of forestry data. His research interest is mainly in exploratory data analytics, distributed computing and data visualisation.
Dr Yue Lin is a Quantitative Silviculturist and Forest Ecologist at Scion. His research interests include using quantitative approaches and simulation models to investigate forest dynamics. His research work involving data mining and machine learning in ecology and forestry and spatially-explicit individual-based forest modelling.
Dr Ralf Gommers is currently a Senior Data Scientist at Scion. His research interests include scientific computing toolsets, interactive data exploration, distributed computing and reproducible research. He enjoys applying data science techniques to environmental issues. He is a board member of NumFOCUS, which governs open source data science projects, and is co-leading both NumPy and SciPy (major open source projects with 500 developers and 5 million users).