Abstract
This paper focuses on the demonstration of an analytics dashboard application for analyzing interesting spatio-temporal associations between anomalies across multiple spatio-temporal datasets, potentially from disparate domains, to find interesting hidden relationships. The proposed system is intended to analyze spatio-temporal data across multiple phenomena from disparate domains (for example traffic and weather) to identify interesting phenomena relationships by linking anomalies from each of these domain datasets. This web-based dashboard application developed in R Shiny [1] provides interactive visualizations to quantify the multi-domain associations. The application uses a novel framework of algorithms and quantification metrics to associate these anomalies across multiple domains using spatial and temporal proximity and influence metrics.
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Acknowledgement
This work is supported in part by the US Army Corps of Engineers, agreement number: W9132V -15-C-0004.
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Walkikar, P., Janeja, V.P. (2017). PhenomenaAssociater: Linking Multi-domain Spatio-Temporal Datasets. In: Candan, S., Chen, L., Pedersen, T., Chang, L., Hua, W. (eds) Database Systems for Advanced Applications. DASFAA 2017. Lecture Notes in Computer Science(), vol 10178. Springer, Cham. https://doi.org/10.1007/978-3-319-55699-4_44
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DOI: https://doi.org/10.1007/978-3-319-55699-4_44
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