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Climate research often seeks to detect changes over time. With the growth of remote sensing data sets, however, Earth scientists are being overwhelmed with data. Much of the data is organized in directory structures that make it hard to find and difficult to analyze.
The Data Rods project is an effort to fundamentally reorganize the archival structure of remote sensing data sets at the National Snow and Ice Data Center (NSIDC). Data sets will be stored in a pure-object database structure that permits rapid spatial and temporal subsetting, filtering, and algorithmic post-processing. The database format and user interface will also facilitate intercomparisons between data sets, enhancing inter-data-set correlations pattern detection capabilities.
David W. Gallaher, Glenn E. Grant, Julienne Stroeve, Dr. Qin Lv, and Iinstry Liang of the CU Computer Science Department
Satellite data rates are expected to grow exponentially for the foreseeable future. NSIDC stores large volumes of cryospheric remote sensing data, and retrieval and processing of these data already presents significant challenges. The Data Rods project proposes to create a new data structure for rapid retrieval, filtering, and analysis of massive multi-modality data sets.
Pre-processing data sets: gridding
Constructing the databases
The Data Rods concept
Why use a pure-object database?
A "pure-object" database stores information using data structures that mirror how an object-oriented program handles its internal objects. Object-oriented programs may insert data into a pure-object database simply by requesting that their instantiated objects be made persistent.
Another program can then connect to the database and re-instantiate the same objects, gaining access to the stored object data structures and methods. This differs substantially from a relational database: Instead of using logical references to search for data items (which becomes inefficient as the database grows) a pure-object database stores data in a linear fashion, permitting rapid searches across massive data sets.
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