Earth Science Data Analytics/2015-9-17 Telecon

ESDA Telecon notes – 9/17/15

Known Attendees:

ESIP Host (Annie Burgess), Steve Kempler, Tiffany Mathews, Sean Barberie, Beth Huffer, Denise, Robert Downs, Thomas Hearty

Agenda:

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

Presentations:

None, this time.

Use Case Information: https://docs.google.com/document/d/1U1mAt4ZjJqXeNmtRoE4VbI1nBgS1v7DzeHib_7mzOF8/edit

Notes:

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:

Done:

1. Finalize ESDA Definition and Goal categories

Initiate:

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

Next Telecon:

Thursday, November 12, 2015, 3:00 EST

Agenda:

Among other things, discuss statement for ESIP approval; Discuss process for matching use case requirements with capabilities of existing tools.

Actions:

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.