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Integrated Analysis and Hypothesis Testing for Complex Spatio-Temporal Data

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Transactions on Computational Science XXXVII

Abstract

Analysis of unstructured, complex data is a challenging task that requires a combination of various data analysis techniques, including, among others, deep learning, statistical analysis, and interactive methods. A simple use of individual data analysis techniques addresses only a part of the overall data exploration and analysis challenge. The visual exploration process also requires exploration of what-if scenarios, a continuous and iterative process of generating and testing hypotheses. We describe a comprehensive approach to exploration of complex data that combines automatic and interactive data analysis and hypotheses testing techniques. The proposed approach is illustrated on a publicly available spatio-temporal data set, a collection of bird songs recorded over an extended period of time. Convolutional Neural Network is used to identify and classify bird species from the bird songs data. In addition, two new interactive views, integrated within a coordinated multiple views setup, are introduced: the what-if view and the spectrogram view. The proposed approach is used to develop a unified tool for exploration of bird songs data, called Bird Song Explorer.

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Acknowledgements

VRVis is funded by BMK, BMDW, Styria, SFG and Vienna Business Agency in the scope of COMET - Competence Centers for Excellent Technologies (854174) which is managed by FFG. This work also was supported, in part, by a grant from the Virginia Tech Institute for Creativity, Arts, and Technology.

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Correspondence to Krešimir Matković .

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Matković, K., Gračanin, D., Beham, M., Splechtna, R., Meyer, M., Ginina, E. (2020). Integrated Analysis and Hypothesis Testing for Complex Spatio-Temporal Data. In: Gavrilova, M., Tan, C., Chang, J., Thalmann, N. (eds) Transactions on Computational Science XXXVII. Lecture Notes in Computer Science(), vol 12230. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-61983-4_3

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  • DOI: https://doi.org/10.1007/978-3-662-61983-4_3

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