Big research efforts have been devoted to efficiently manage spatio-temporal data. However, most works focused on vectorial data, and much less, on raster data. This work presents a new representation for raster data that evolve along time named Temporal k^2 raster. It faces the two main issues that arise when dealing with spatio-temporal data: the space consumption and the query response times. It extends a compact data structure for raster data in order to manage time and thus, it is possible to query it directly in compressed form, instead of the classical approach that requires a complete decompression before any manipulation. In addition, in the same compressed space, the new data structure includes two indexes: a spatial index and an index on the values of the cells, thus becoming a self-index for raster data.
@article{arxiv.1810.10965,
title = {Towards a compact representation of temporal rasters},
author = {Ana Cerdeira-Pena and Guillermo de Bernardo and Antonio Fariña and Jose R. Parama and Fernando Silva-Coira},
journal= {arXiv preprint arXiv:1810.10965},
year = {2018}
}
Comments
This research has received funding from the European Union's Horizon 2020 research and innovation programme under the Marie Sklodowska-Curie Actions H2020-MSCA-RISE-2015 BIRDS GA No. 690941. Published in SPIRE 2018