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Towards Visualization Recommendation: A Semi-Automated Ecological Data Specific Learning Approach, GfÖ 2015,
Title: | Towards Visualization Recommendation: A Semi-Automated Ecological Data Specific Learning Approach, GfÖ 2015, |
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Presenter(s): | Pawandeep Kaur |
Event: | GfOe Annual Meeting 2015 |
Date: | 2015-08-31 16:15 |
Description: | Ecological studies produce highly complex, heterogeneous and distributed data from its wider research activities. For efficiently communicating the research work and presentation of the related data, visualization plays an important role, due to its ability to condense large amounts of data into effective and understandable graphics. The decision of optimal choice of visualization, not only produces more interpretable graphics, but support the community to understand, analyse the data and reuse it for their respective studies. However, studies have shown that the potential of visualization has not been fully utilized in scientific journals, due to in-appropriate visualization selection with respect to the nature of data and message to convey. This does not only impede analysis but also results in misleading conclusions. To provide a solution for the problem of visualization selection, we propose a semi‐automated context‐aware visualization recommendation model. In the model, information will be extracted from data and metadata, and annotated with suitable ecological operations (analytical tasks like spatial distribution, relative species abundance). This information will be mapped to the visualization semantics; like in each extracted operation which variables are involved and how they are visually represented. This helps in deriving the relevant visualizations for that data. We also propose an interactive learning workflow for visualization recommendation that will enrich the model from the knowledge gathered from each interaction with the user. In our work, we will develop our base knowledge (which visualizations have been used to represent what ecological operations) from the visualization presented in the ecological publications. This knowledge is integral in making decisions based on the current trends in visualizations for representing ecological concepts and data. |