Optimizing an index with spatiotemporal patterns to support GEOSS Clearinghouse

Abstract

A variety of Earth observation systems monitor the Earth and provide petabytes of geospatial data to decision-makers and scientists on a daily basis. However, few studies utilize spatiotemporal patterns to optimize the management of the Big Data. This article reports a new indexing mechanism with spatiotemporal patterns integrated to support Big Earth Observation (EO) metadata indexing for global user access. Specifically, the predefined multiple indices mechanism (PMIM) categorizes heterogeneous user queries based on spatiotemporal patterns, and multiple indices are predefined for various user categories. A new indexing structure, the Access Possibility R-tree (APR-tree), is proposed to build an R-tree-based index using spatiotemporal query patterns. The proposed indexing mechanism was compared with the classic R*-tree index in a number of scenarios. The experimental result shows that the proposed indexing mechanism generally outperforms a regular R*-tree and supports better operation of Global Earth Observation System of Systems (GEOSS) Clearinghouse.

Keywords:

• geospatial cyberinfrastructure
• CyberGIS
• spatiotemporal thinking and computing
• spatial/spatiotemporal index
• Big Data
• cloud computing

Funding

This research is supported by FGDC GeoCloud and GEOSS Clearinghouse grants, Microsoft Research, and NSF I/UCRC, EarthCube, and CyberPolar [grant number IIP-1338925], [grant number ICER-1343759], [grant number PLR-1349259].