Difference between revisions of "Earth Science Data Analytics/Use Case Collection"

From Federation of Earth Science Information Partners
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'''Use Case Name''':  
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'''Use Case Name''': Inter-calibrations among datasets
  
'''Provided By''':
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'''Provided By''': Tiffany Mathews
  
'''Brief Description''':
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'''Brief Description''': To be able to quickly measure calibrations between different instruments and evaluate incremental improvements to data reduction algorithms or to models.
  
'''Key Analytics Needs''':
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'''Key Analytics Needs''': Ability to quickly identify which time model each instrument that created the data was using and convert the time to be identical before making comparisons. 
  
  
'''Use Case Name''':  
+
'''Use Case Name''': Inter-comparisons between multiple model or data products
  
'''Provided By''':
+
'''Provided By''': Tiffany Mathews
  
'''Brief Description''':
+
'''Brief Description''': To be able to obtain high-resolution inter-comparisons between two or more model or data products in order to get more accurate measurements than can be received from observing the global monthly means such as enabling one to make a point by point comparisons of specific locations.
  
'''Key Analytics Needs''':
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'''Key Analytics Needs''': Needs to work with appropriate data formats, 3-D data visualization,
  
  
'''Use Case Name''':  
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'''Use Case Name''': Ability to see the original source data components
  
'''Provided By''':
+
'''Provided By''': Tiffany Mathews
  
'''Brief Description''':
+
'''Brief Description''':To enable users to select a value from a Level 2 (or higher) data product to go back to see the original source data components (in the Level 1 product) to be able to make their own assessments and better identify and understand phenomena.
  
'''Key Analytics Needs''':
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'''Key Analytics Needs''': Understanding and compatibility to work with the data format
  
  

Revision as of 12:04, April 11, 2014

Use Case Collecting...


Please insert the pertinent information regarding your use case below. We can beef up the details later. Alternatively, e-map information to Steve Kempler ([email protected])


Use Case Name: MERRA Analytics Services

Provided By: John Schnase

Brief Description: Enables Climate Analytics-as-a-Service by combining iRODS data management, Cloudera MapReduce, and the Climate Data Services API to serve MERRA reanalysis products.

Key Analytics Needs: Store the MERRA reanalysis data collection in an HDFS to enable parallel, high-performance, storage-side data reductions; Manage storage-side <driver, mapper, reducer> code sets and realized objects for users; Provide a library of commonly used spatiotemporal operations (canonical ops) that can be composed to enable higher-order analyses; and Deliver end-user and application capabilities through the NASA Climate Data Services API.




Use Case Name: Inter-calibrations among datasets

Provided By: Tiffany Mathews

Brief Description: To be able to quickly measure calibrations between different instruments and evaluate incremental improvements to data reduction algorithms or to models.

Key Analytics Needs: Ability to quickly identify which time model each instrument that created the data was using and convert the time to be identical before making comparisons.


Use Case Name: Inter-comparisons between multiple model or data products

Provided By: Tiffany Mathews

Brief Description: To be able to obtain high-resolution inter-comparisons between two or more model or data products in order to get more accurate measurements than can be received from observing the global monthly means such as enabling one to make a point by point comparisons of specific locations.

Key Analytics Needs: Needs to work with appropriate data formats, 3-D data visualization,


Use Case Name: Ability to see the original source data components

Provided By: Tiffany Mathews

Brief Description:To enable users to select a value from a Level 2 (or higher) data product to go back to see the original source data components (in the Level 1 product) to be able to make their own assessments and better identify and understand phenomena.

Key Analytics Needs: Understanding and compatibility to work with the data format


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