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The big data is characterized by challenges on variety, volumes, velocity etc. Recent advocate of data cube in the Earth observation (EO) domain has shown great promise to provide analysis ready data for remote sensing applications. It is possible to develop a geospatial big data infrastructure layered on the data cube by incorporating a uniform analysis-ready multidimensional data structure and exploiting its usage in connecting EO data analytics and OLAP (Online Analytical Processing), thus enabling a multi-source geospatial data cube accommodating both EO and location-based social-economic data. The creation of such a geospatial data cube, named GeoCube, needs special attentions from geospatial perspective including the formalization of spatio-temporal dimensions, tiling along these dimensions for high performance geoprocessing, and processing of geospatial/EO queries against these dimensions. This will help develop a new framework for big geospatial data analytics while at the time keeping connections to the data cube in the business intelligence domain. The paper will highlight these issues and identify a research agenda for developing such a geospatial data cube.
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Papers that cite the same references — the relational fallback while embeddings are still backfilling.
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