Optimizing an index with spatiotemporal patterns to support GEOSS Clearinghouse

Jizhe Xia, Chaowei Yang, Zhipeng Gui, Kai Liu, Zhenlong Li

Research output: Contribution to journalArticlepeer-review

13 Scopus citations

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.

Original languageEnglish (US)
Pages (from-to)1459-1481
Number of pages23
JournalInternational Journal of Geographical Information Science
Volume28
Issue number7
DOIs
StatePublished - Jul 2014

All Science Journal Classification (ASJC) codes

  • Information Systems
  • Geography, Planning and Development
  • Library and Information Sciences

Cite this