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Free keywords:
Urban Planning; Climatology; Spatiotemporal data; Geospatial; Data Management; Columns-stores; NetCDF; R; GIS
Abstract:
Earth observation sciences produce large sets of data which are inherently rich in spatial and geo-spatial information. Together with live data collected from monitoring systems and large collections of semantically rich objects they provide new opportunities for advanced eScience research on climatology, urban planing and smart cities. Such combination of heterogeneous data sets forms a new source of knowledge. Efficient knowledge extraction from them is an eScience challenge. It requires efficient bulk data injection from both static and streaming data sources, dynamic adaptation of the physical and logical schema, efficient methods to correlate spatial and temporal data, and flexibility to (re-)formulate the research question at any time. In this work, we present a data management layer over a column-oriented relational data management system that provides efficient analysis of spatiotemporal data. It provides fast data ingestion through different data loaders, tabular and array based storage, and a dynamic step-wise exploration.
