Abstract
Scientific datasets are often stored on distributed archival storage systems, because geographically distributed sensor devices store the datasets in their local machines and also because the size of scientific datasets demands large amount of disk space. Multidimensional indexing techniques have been shown to greatly improve range query performance into large scientific datasets. In this paper, we discuss several ways of distributing a multidimensional index in order to speed up access to large distributed scientific datasets. This paper compares the designs, challenges, and problems for distributed multidimensional indexing schemes, and provides a comprehensive performance study of distributed indexing to provide guidelines to choose a distributed multidimensional index for a specific data analysis application.
| Original language | English |
|---|---|
| Pages (from-to) | 1552-1576 |
| Number of pages | 25 |
| Journal | Journal of Supercomputing |
| Volume | 59 |
| Issue number | 3 |
| DOIs | |
| State | Published - Mar 2012 |
| Externally published | Yes |
Keywords
- Data intensive computing
- Decentralized indexing
- Distributed indexing
- Multidimensional indexing