A Portable Approach to Integrating Diverse Geo-Science Data Using Stare-Aware Databases and Transitioning to Cloud

Big Data technologies such as Cloud and parallel distributed computing and storage are necessary to treat Earth Science data volume. Yet the great diversity of Earth Science data renders it nearly impossible to organize that data on scalable platforms without costly data movement or undesired interpolation that straitjackets scientific research. The SpatioTemporal Adaptive Resolution Encoding (STARE) is an alternative geolocation and indexing scheme for harmonizing data for integrative analysis on scalable systems. STARE uses a hierarchical, recursive partitioning of space and time in which the index or coordinates of each node are integers from the same index space, usually allowing quick comparison without floating-point calculation. STARE is well suited to provide a unifying geo-semantics for arranging data in databases. In this work, we outline the technical principles underlying STARE and its application to SQLite as an example. The STARELite STARE-aware lightweight geo-database can be used to catalogue diverse data for geographical querying and integration on local resources and Cloud.