Colorado State University
The quantity and precision of geospatial and time series observational data being collected has increased in tandem with the steady expansion of processing and storage capabilities in modern computing hardware. The storage requirements for this information are vastly greater than the capabilities of a single computer, and are primarily met in a distributed manner. However, distributed solutions often impose strict constraints on retrieval semantics. In this paper, the authors investigate the factors that influence storage and retrieval operations on large datasets in a cloud setting, and propose a lightweight data partitioning and indexing scheme to facilitate these operations.