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

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Use Case Collection
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Use Case Collecting...
  
Please insert the pertinent information regarding your use case below.  Please try
 
  
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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:
 
  
Provided By:
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'''Use Case Name''': MERRA Analytics Services
Brief Description:
 
Key Analytics Needs:
 
  
Use Case Name:
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'''Provided By''': John Schnase
Provided By:
 
Brief Description:
 
Key Analytics Needs:
 
  
Use Case Name:
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'''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.
Provided By:
 
Brief Description:
 
Key Analytics Needs:
 
  
Use Case Name:
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'''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.
Provided By:
 
Brief Description:
 
Key Analytics Needs:
 
  
Use Case Name:
 
Provided By:
 
Brief Description:
 
Key Analytics Needs:
 
  
Use Case Name:
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Provided By:
 
Brief Description:
 
Key Analytics Needs:
 
  
Use Case Name:
 
Provided By:
 
Brief Description:
 
Key Analytics Needs:
 
  
Use Case Name:  
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'''Use Case Name''': Inter-calibrations among datasets
Provided By:
 
Brief Description:
 
Key Analytics Needs:
 
  
Use Case Name:
+
'''Provided By''': Tiffany Mathews
Provided By:
 
Brief Description:
 
Key Analytics Needs:
 
  
Use Case Name:
+
'''Brief Description''': To be able to quickly measure calibrations between different instruments and evaluate incremental improvements to data reduction algorithms or to models.
Provided By:
 
Brief Description:
 
Key Analytics Needs:
 
  
Use Case Name:
+
'''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. 
Provided By:
 
Brief Description:
 
Key Analytics Needs:
 
  
Use Case Name:
 
Provided By:
 
Brief Description:
 
Key Analytics Needs:
 
  
Use Case Name:  
+
'''Use Case Name''': Inter-comparisons between multiple model or data products
Provided By:
 
Brief Description:
 
Key Analytics Needs:
 
  
Use Case Name:
+
'''Provided By''': Tiffany Mathews
Provided By:
 
Brief Description:
 
Key Analytics Needs:
 
  
Use Case Name:
+
'''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.
Provided By:
 
Brief Description:
 
Key Analytics Needs:
 
  
Use Case Name:  
+
'''Key Analytics Needs''': Needs to work with appropriate data formats, 3-D data visualization,
Provided By:
+
 
Brief Description:
+
 
Key Analytics Needs:
+
'''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
 +
 
 +
 
 +
'''Use Case Name''': Ability to detect seismic instrumentation problems
 +
 
 +
'''Provided By''':  Robert Casey
 +
 
 +
'''Brief Description''': By processing the incoming time series signal, gathering statistics and signatures, and analyzing trends in these metrics, we hope to be able to observe and identify problems occurring with the instrumentation or with the installation site prior to calling someone out to that site.
 +
 
 +
'''Key Analytics Needs''':  Ability to gather seismic data for multiple instruments over many days, process against instrumentation metadata, store and retrieve statistical measurements of data, provide visualization, filtering, and comparison capabilities.
 +
 
 +
 
 +
'''Use Case Name''': Ability to diagnose seismic instrumentation problems
 +
 
 +
'''Provided By''': Robert Casey
 +
 
 +
'''Brief Description''': Given a pool of metrics history on seismic data, cross reference to a scoring matrix of characteristic measurements to correctly diagnosed condition or cause, providing automated diagnosis of instrumentation problems.
 +
 
 +
'''Key Analytics Needs''': Human experts to analyze and tabulate data problems, system to store and collect diagnoses cross referenced to key indicators, scoring mechanism to map out highest probability conditions, feedback mechanism for human to teach system which automated determinations are correct and which are not.
 +
 
 +
 
 +
'''Use Case Name''': Ability to forecast imminent seismic instrument degradation
 +
 
 +
'''Provided By''': Robert Casey
 +
 
 +
'''Brief Description''': Given a pool of metrics history on an instrument's data, apply appropriate trending algorithms to estimate where an instrument's data quality may be heading.  Provide projections for visual analysis.  Save projections for later comparison to real metrics.  Modify trending algorithm weighting to better fit forecasts to actual outcomes.
 +
 
 +
'''Key Analytics Needs''': good forecasting functions with malleable independent variables, storage of projections until comparisons are carried out, stored and gracefully modified weighting values to serve as a learning cycle for forecasting
 +
 
 +
 
 +
'''Use Case Name''': Composite models/automated model coupling
 +
 
 +
'''Provided By''': Beth Huffer
 +
 
 +
'''Brief Description''': Combining specialized climate models creates composite models that are capable of making predictions for much more complex science problems than the individual models themselves.  Components of composite models use outputs from other component models as inputs. 
 +
 
 +
'''Key Analytics Needs''': Robust, machine-readable metadata to determine which model outputs are suitable as inputs for companion models, and tools for converting units, regridding, etc.
 +
 
 +
 
 +
'''Use Case Name''': Finding correlations between variables
 +
 
 +
'''Provided By''':Beth Huffer
 +
 
 +
'''Brief Description''':To accurately predict the impact of climate change, scientists must determine the impact that changes in one variable have on other variables. For example, there are known correlations between sea surface temperature and vapor pressure, and sea surface temperature and land surface temperatures in coastal regions.
 +
 
 +
'''Key Analytics Needs''': Robust, machine-readable, metadata to enable researchers to easily find and identify, from multiple, heterogeneous datasets, variables that meet some set of criteria that makes them suitable for analysis. Subsetters for accessing individual variables. Automated regridding, unit conversion.
 +
 
 +
 
 +
'''Use Case Name''': Analysis and Statistical Tool/Model Selector
 +
 
 +
'''Provided By''': Emily Law
 +
 
 +
'''Brief Description''': Provide ability to help to data scientists to choose available and best fit tools/model specific to user's data set characteristics/variables.
 +
 
 +
'''Key Analytics Needs''': Data analytic, statistic, tools and models database, simply user interface, and data interpreter, intelligent algorithm to match data/tool sets.
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Latest revision as of 06:19, June 26, 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


Use Case Name: Ability to detect seismic instrumentation problems

Provided By: Robert Casey

Brief Description: By processing the incoming time series signal, gathering statistics and signatures, and analyzing trends in these metrics, we hope to be able to observe and identify problems occurring with the instrumentation or with the installation site prior to calling someone out to that site.

Key Analytics Needs: Ability to gather seismic data for multiple instruments over many days, process against instrumentation metadata, store and retrieve statistical measurements of data, provide visualization, filtering, and comparison capabilities.


Use Case Name: Ability to diagnose seismic instrumentation problems

Provided By: Robert Casey

Brief Description: Given a pool of metrics history on seismic data, cross reference to a scoring matrix of characteristic measurements to correctly diagnosed condition or cause, providing automated diagnosis of instrumentation problems.

Key Analytics Needs: Human experts to analyze and tabulate data problems, system to store and collect diagnoses cross referenced to key indicators, scoring mechanism to map out highest probability conditions, feedback mechanism for human to teach system which automated determinations are correct and which are not.


Use Case Name: Ability to forecast imminent seismic instrument degradation

Provided By: Robert Casey

Brief Description: Given a pool of metrics history on an instrument's data, apply appropriate trending algorithms to estimate where an instrument's data quality may be heading. Provide projections for visual analysis. Save projections for later comparison to real metrics. Modify trending algorithm weighting to better fit forecasts to actual outcomes.

Key Analytics Needs: good forecasting functions with malleable independent variables, storage of projections until comparisons are carried out, stored and gracefully modified weighting values to serve as a learning cycle for forecasting


Use Case Name: Composite models/automated model coupling

Provided By: Beth Huffer

Brief Description: Combining specialized climate models creates composite models that are capable of making predictions for much more complex science problems than the individual models themselves. Components of composite models use outputs from other component models as inputs.

Key Analytics Needs: Robust, machine-readable metadata to determine which model outputs are suitable as inputs for companion models, and tools for converting units, regridding, etc.


Use Case Name: Finding correlations between variables

Provided By:Beth Huffer

Brief Description:To accurately predict the impact of climate change, scientists must determine the impact that changes in one variable have on other variables. For example, there are known correlations between sea surface temperature and vapor pressure, and sea surface temperature and land surface temperatures in coastal regions.

Key Analytics Needs: Robust, machine-readable, metadata to enable researchers to easily find and identify, from multiple, heterogeneous datasets, variables that meet some set of criteria that makes them suitable for analysis. Subsetters for accessing individual variables. Automated regridding, unit conversion.


Use Case Name: Analysis and Statistical Tool/Model Selector

Provided By: Emily Law

Brief Description: Provide ability to help to data scientists to choose available and best fit tools/model specific to user's data set characteristics/variables.

Key Analytics Needs: Data analytic, statistic, tools and models database, simply user interface, and data interpreter, intelligent algorithm to match data/tool sets.


Use Case Name:

Provided By:

Brief Description:

Key Analytics Needs:


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