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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Ameri, Parinaz Grabowski, Udo Meyer, Jorg Streit, Achim |
| Copyright Year | 2014 |
| Abstract | Analyses in climate research typically operate on large datasets stored in file hierarchies. However, e.g. Indexing, meta-data search and replication is challenging with this approach. A new approach is to store and index datasets in large, distributed databases. In order to demonstrate the performance improvement, we utilized the so-called general matching problem between measurements of two satellites that differ in orbits and measurement cycles. For comparison purpose the measurements are matched within a specified maximum spatial and time offset constraint. We describe the steps from a single-threaded approach using a SQL database to a multi-threaded approach using the NoSQL database MongoDB. The performance as well as limitations on CPU and I/O are evaluated and discussed for each approach. The performance has been improved up to a factor of 46 using 11 threads on a 12-core system. |
| Starting Page | 652 |
| Ending Page | 659 |
| File Size | 281553 |
| Page Count | 8 |
| File Format | |
| ISBN | 9781479965137 |
| DOI | 10.1109/TrustCom.2014.84 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2014-09-24 |
| Publisher Place | China |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | Satellites Databases Atmospheric measurements NoSQL Instruction sets MySQL Extraterrestrial measurements MongoDB Geospatial Servers Performance Meteorology |
| Content Type | Text |
| Resource Type | Article |
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