Towards a Model-Driven Datacube Analytics Language
Peter Baumann · 2021 IEEE International Conference on Big Data (Big Data) · 2021
Datacubes form an accepted cornerstone for analysis (and visualization) ready spatio-temporal data offerings. Geo datacubes have been standardized since long under the umbrella concept of coverages, and such data structures are well understood in concept and practice. This, however, is not paired by a similar understanding of coverage analytics.We present a formal model for datacube analytics which is based on Linear Algebra, incorporates space and time semantics, and allows a wide range of common datacube operations, up to, say, the Discrete Fourier Transform. For convenience, the formalism is based on a language allowing expressions of any complexity.The specification is currently in the avanced adoption process of ISO for becoming the future 19123-3 standard.