Earth Science Data Analytics/2015-9-17 Telecon
ESDA Telecon notes – 9/17/15
ESIP Host (Annie Burgess), Steve Kempler, Tiffany Mathews, Sean Barberie, Beth Huffer, Denise, Robert Downs, Thomas Hearty
1. Use Cases
A. Updated Use Case Template with cluster recommendations. Added: Dominant Data Analytics B. Use Case Status – one more added
2. Finalize ESDA Analytics Definitions and Goals
3. Associate Analytics Tools/Techniques Requirements with Analytics Goals
4. Are we ready to propose an ESIP ESDA Definition and Goals statement for ESIP approval?
5. Open Mic
None, this time.
Use Case Information: https://docs.google.com/document/d/1U1mAt4ZjJqXeNmtRoE4VbI1nBgS1v7DzeHib_7mzOF8/edit
Thank you all for attending and participating in our telecon.
Discussion began around finalizing the ESIP ESDA Definition and Goals for endorsement by the ESIP Federation. At this point the process for gaining ESIP approval was described:
- Bring a short white paper describing what is to be endorsed to the ESIP ExComm
- This is followed by a 30 day review cycle,
- Questions, suggestions, recommendations, to update proposed endorsement, are provided, or an explanation for not endorsing.
- The endorsement is put up for vote
Steve will initiate the writing of the white paper. And seeking writing and reviewing partners.
After a few more minor tweaks, this ESIP cluster's definition for Earth Science Data Analytics is:
The process of examining large amounts of spatial (3D), temporal, and/or spectral data of a variety of data types to uncover hidden patterns, unknown correlations and other useful information, involving one or more of the following:
- Data Preparation – Preparing heterogeneous data so that they can ‘play’ together
- Data Reduction – Smartly removing data that do not fit research criteria
- Data Analysis – Applying techniques/methods to derive results
and the goals of Earth Science Data Analytics, in which such analytics can be categorized, include:
ESDA Goals (read: Earth science data analytics needed ...)
1 To calibrate data
2 To validate data (note it does not have to be via data intercomparison)
3 To assess data quality
4 To perform course data preparation (e.g., subsetting, data mining, transformations, recover data)
5 To intercompare data (i.e., any data intercomparison; Could be used to better define validation/quality)
6 To tease out information from data
7 To glean knowledge from data and information
8 To forecast/predict phenomena (i.e., Special kind of conclusion)
9 To derive conclusions (i.e., that do not easily fall into another type)
10 To derive new analytics tools
These will be the basis for the ESIP Federation definition and goals for Earth Science Data Analytics
During the telecon, Steve reviewed a 'to do' list to describe our road ahead, that included:
1. Finalize ESDA Definition and Goal categories
2. Write a white paper for ESIP Executive Committee proposing that the ESDA Definitions and Goal categories be ESIP approved
3. Acquire many more additional use cases
4. Characterize use cases by Goal categories and other analytics driving considerations
5. Derive requirements from #4
6. Survey existing data analytics tools/techniques
7. Write our paper describing ... all the above
Questions to think about:
What is the best way to record use cases, and associated requirements, and matching tools? A forum?
Going to AGU?
The following Data Analytics / Big Data related sessions are listed to occur at the AGU in December:
- Advanced Information Systems to Support Climate Projection Data Analysis
Gerald L Potter, Tsengdar J Lee, Dean Norman Williams, and Chris A Mattmann
- Big Data Analytics for Scientific Data
Emily Law, Michael M Little, Daniel J Crichton, and Padma A Yanamandra-Fisher
- Big Data in Earth Science – From Hype to Reality
Kwo-Sen Kuo, Rahul Ramachandran, Ben James Kingston Evans. and Mike M Little
- Big Data in the Geosciences: New Analytics Methods and Parallel Algorithms
Jitendra Kumar and Forrest M Hoffman
- Computing Big Earth Data
Michael M Little, Darren L. Smith, Piyush Mehrotra, and Daniel Duffy
- Geophysical Science Data Analytics Use Case Scenarios
Steven J Kempler, Robert R Downs, Tiffany Joi Mathews, and John S Hughes
- Man vs. Machine - Machine Learning and Cognitive Computing in the Earth Sciences
Jens F Klump, Xiaogang Ma, Jess Robertson and Peter A Fox
- New approaches for designing Big Data databases
David W Gallaher and Glenn Grant
- Partnerships and Big Data Facilities in a Big Data World
Kenneth S Casey and Danie Kinkade
- Towards a Career in Data Science: Pathways and Perspectives
Karen I Stocks, Lesley A Wyborn, Ruth Duerr, and Lynn Yarmey
Thursday, November 12, 2015, 3:00 EST
Among other things, discuss statement for ESIP approval; Discuss process for matching use case requirements with capabilities of existing tools.
Steve: Initiate draft endorsement paper
Volunteers: Review endorsement paper, when ready
All: Think about process for matching use case requirements with capabilities of existing tools.