GeoMesa: a distributed architecture for spatio-temporal fusion

James N. Hughes; Andrew Annex; Christopher N. Eichelberger; Anthony Fox; Andrew Hulbert; Michael Ronquest

doi:10.1117/12.2177233

21 May 2015 GeoMesa: a distributed architecture for spatio-temporal fusion

James N. Hughes; Andrew Annex; Christopher N. Eichelberger; Anthony Fox; Andrew Hulbert; Michael Ronquest

Author Affiliations +

Proceedings Volume 9473, Geospatial Informatics, Fusion, and Motion Video Analytics V; 94730F (2015) https://doi.org/10.1117/12.2177233
Event: SPIE Defense + Security, 2015, Baltimore, Maryland, United States

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.

Citation Download Citation

James N. Hughes, James N. Hughes, Andrew Annex, Andrew Annex, Christopher N. Eichelberger, Christopher N. Eichelberger, Anthony Fox, Anthony Fox, Andrew Hulbert, Andrew Hulbert, Michael Ronquest, 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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