21 May 2015 GeoMesa: a distributed architecture for spatio-temporal fusion
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Abstract
Recent advances in distributed databases and computing have transformed the landscape of spatio-temporal machine learning. This paper presents GeoMesa, a distributed spatio-temporal database built on top of Hadoop and column-family databases such as Accumulo and HBase, that includes a suite of tools for indexing, managing and analyzing both vector and raster data. The indexing techniques use space filling curves to map multi-dimensional data to the single lexicographic list managed by the underlying distributed database. In contrast to traditional non-distributed RDBMS, GeoMesa is capable of scaling horizontally by adding more resources at runtime; the index rebalances across the additional resources. In the raster domain, GeoMesa leverages Accumulo's server-side iterators and aggregators to perform raster interpolation and associative map algebra operations in parallel at query time. The paper concludes with two geo-time data fusion examples: using GeoMesa to aggregate Twitter data by keywords; and georegistration to drape full-motion video (FMV) over terrain.
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James N. Hughes, Andrew Annex, Christopher N. Eichelberger, Anthony Fox, Andrew Hulbert, Michael Ronquest, "GeoMesa: a distributed architecture for spatio-temporal fusion", Proc. SPIE 9473, Geospatial Informatics, Fusion, and Motion Video Analytics V, 94730F (21 May 2015); doi: 10.1117/12.2177233; https://doi.org/10.1117/12.2177233
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