Difference between revisions of "Earth Science Data Analytics/2015-2-5 Telecon"

Latest revision as of 16:28, February 27, 2015

ESDA Telecom notes – 2/5/15

Known Attendees:

ESIP Host (Nancy Hoebelheinrich), Steve Kempler, Chung-lin Shie, Thomas Hearty, Tiffany Mathews, Ethan McMahon, Robert Downs, Brand Niemann, Ethan Davis, Jennifer Wei, Liping Di, Radina Soebiyanto, Robert Casey, Sara Graves, Joan Aron

Agenda:

1. Discuss Use Case Information Needed.

Continuing our face to face at ESIP, we decided to build a library of Earth science data analytics use cases, but first needed to ensure the information requested was clear and appropriate. I created a Google Spreadsheet open for review and comments. Besides the folks who, at the face-to-face, signed up to spend a little time to review and provide inputs on the spreadsheet, if others wish to be involved in editing, please send me an e-mail.

2. Discuss ESIP Summer Meeting theme

Earth Science Data Analytics. Let’s continue the discussion started in January to help the meeting organizers with gathering speakers, themes orientation for the rest of the Federation, etc. I will know more before Thursday.

3. Open Mic

Presentations:

None, this time. Worked off Google Doc: Use Case Information: https://docs.google.com/spreadsheets/d/108glVB8Rni8M47e5G1_oZ6g1q5AOxzdTbC_P2WrEt6o/edit#gid=0

Notes:

Thank you all for attending.

Today, agenda topic number 1 captured the bulk of time during our telecon. We had a very good discussion describing and discussing the information we should pursue when collecting Earth Science Data Analytics use cases. We examined/compared the categories that we had come up with and the categories utilize by the NIST Big Data Use Case gathering effort (see: http://bigdatawg.nist.gov/usecases.php). By consolidating the categories, we acknowledged that we may be asking for more information that is available in our use cases, but that is OK as we learn. It was also noted that the NIST list addressed categories valuable for Big Data utilization use cases, thus some NIST categories amy not apply to ESDSA use cases. Again]n, we'll give it try. Steve consolidated categories and ESDA group comments in the above mentioned Google Doc. See action.

We did not get a chance to talk much about Earth Science Data Analytics as a potential theme for the summer's ESIP meeting. See action.

Post telecon note: At Monday's Visioneer meeting, which I suggest attending if you are interested in being part of forward looking ESIP activities and direction, Summer meeting logistics was discussed, theme did not get addressed. So, I have nothing to report on that, at this time

Next Telecon:

Thursday, February 26, 2015, 3:00 EST

Agenda:

1. Review ESDA Use Case provided in Google Doc.

2. Discuss ESIP Summer Meeting theme

3. Open Mic

Actions:

Steve - Steve consolidated categories and ESDA group comments in our Google Doc: https://docs.google.com/spreadsheets/d/108glVB8Rni8M47e5G1_oZ6g1q5AOxzdTbC_P2WrEt6o/edit#gid=0

Done

Ethan M, Robert C, Tiffany M, Steve K - Each person enter up to 2 ESDA use cases into the Google Doc (prototypes entries) to determine if the information we are gathering per project will lead us to be able to categorize the data analytics being utilized. Due Friday, February 13.

Active Participants - Review use cases submitted and provide feedback in Google Doc, in comments pull down. Due for next telecon, February 20.

All Other Participants - E-mail Steve (Steven.J.Kempler@nasa.gov) so you can be an Active Participant Soon. But don't need to be an Active Participant to review use cases...so please do

Steve - Look at how themes were reflected in past ESIP Meeting agendas. Report back to group.

All - For next telecon, prepare to discuss ESDA theme for next ESIP Meeting: Focus of theme, who can we invite as speakers, which other clusters have some relation to the usage of data analytics

@@ Line 3: / Line 3: @@
 ===Known Attendees:===
-ESIP Host (Nancy), Steve Kempler, Chung-lin Shie, Thomas Hearty, Tiffany Mathews, Ethan McMahon, Robert Downs, Brand Niemann, Ethan Davis, Jennifer Wei, Liping Di, Radina Soebiyanto, Bob Casey, Sara Graves,
+ESIP Host (Nancy Hoebelheinrich), Steve Kempler, Chung-lin Shie, Thomas Hearty, Tiffany Mathews, Ethan McMahon, Robert Downs, Brand Niemann, Ethan Davis, Jennifer Wei, Liping Di, Radina Soebiyanto, Robert Casey, Sara Graves, Joan Aron
 ===Agenda:===
-CONGRATULATIONS TO ERIN!
+Agenda:
+.  Discuss Use Case Information Needed.
-– Recap of our last telecon on Diagnostic Analytics
+Continuing our face to face at ESIP, we decided to build a library of Earth science data analytics use cases, but first needed to ensure the information requested was clear and appropriate.  I created a Google Spreadsheet open for review and comments.  Besides the folks who, at the face-to-face, signed up to spend a little time to review and provide inputs on the spreadsheet, if others wish to be involved in editing, please send me an e-mail.
+.  Discuss ESIP Summer Meeting theme
-- Discussion:  Discoveritive and Predictive Analytics
+Earth Science Data Analytics.  Let’s continue the discussion started in January to help the meeting organizers with gathering speakers, themes orientation for the rest of the Federation, etc.  I will know more before Thursday.
+.  Open Mic
-– Planning ahead discussion:   Winter ESIP Meeting ESDA Planning:  Sessions; Suggestions for guest speakers;  Are we starting to learn enough to write a paper on the Types of Data Analytics Utilized in (the various phases of) Earth Science
-- Open Mic – Thoughts, Ideas
+Presentations:
-Presentations:
+None, this time.  Worked off Google Doc:  Use Case Information:  https://docs.google.com/spreadsheets/d/108glVB8Rni8M47e5G1_oZ6g1q5AOxzdTbC_P2WrEt6o/edit#gid=0
-* [[Media: 2014-11-20 ESDA.pdf|  Steve Kempler: ESDA Cluster Discussion slides, November 20, 2014]]
@@ Line 31: / Line 30: @@
 Thank you all for attending.
-Next, in our movement to review the various types of Data Analytics, with the objective to clarify and specifically define, one by one, each type of data analytics, we discussed Discoveritive, Predictive, and Prescriptive Data Analytics.
+Today, agenda topic number 1 captured the bulk of time during our telecon.  We had a very good discussion describing and discussing the information we should pursue when collecting Earth Science Data Analytics use cases.  We examined/compared the categories that we had come up with and the categories utilize by the NIST Big Data Use Case gathering effort (see: http://bigdatawg.nist.gov/usecases.php).  By consolidating the categories, we acknowledged that we may be asking for more information that is available in our use cases, but that is OK as we learn.  It was also noted that the NIST list addressed categories valuable for Big Data utilization use cases, thus some NIST categories amy not apply to ESDSA use cases.  Again]n, we'll give it  try.   Steve consolidated categories and ESDA group comments in the above mentioned Google Doc.  See action.
-As a reminder:
+We did not get a chance to talk much about Earth Science Data Analytics as a potential theme for the summer's ESIP meeting.  See action.
-'''Types of Data Analytics'''
+Post telecon note:  At Monday's Visioneer meeting, which I suggest attending if you are interested in being part of forward looking ESIP activities and direction, Summer meeting logistics was discussed, theme did not get addressed.  So, I have nothing to report on that, at this time
-Descriptive Analytics:  You can quickly understand "what happened" during a given period in the past and verify if a campaign was successful or not based on simple parameters.
-Diagnostic Analytics:  If you want to go deeper into the data you have collected from users in order to understand "Why some things happened," you can use … intelligence tools to get some insights.
+===Next Telecon:===
+Thursday, February 26, 2015, 3:00 EST
-Discoveritive Analytics:  The use of data and analysis tools/models to discover information
-Predictive Analytics:  If you can collect contextual data and correlate it with other user behavior datasets, as well as expand user data … you enter a whole new area where you can get real insights.
-Prescriptive Analytics:  Once you get to the point where you can consistently analyze your data to predict what's going to happen, you are very close to being able to understand what you should do in order to maximize good outcomes and also prevent potentially bad outcomes. This is on the edge of innovation today, but it's attainable!
-The following '''Discoveritive Data Analytics''' definitions were offered:
-- Tell me something that I don't know" is the definition of data mining - discovering unexpected patterns and relationships in data. (http://online-behavior.com/emetrics/data-discovery-1073)
-- Four types of discovery analytics: visual discovery, data discovery, information discovery and event discovery (http://www.information-management.com/blogs/3-major-trends-in-new-discovery-analytics-10024769-1.html)
-The following '''Predictive Data Analytics''' definitions were offered:
-- Encompasses a variety of statistical techniques from modeling, machine learning, and data mining that analyze current and historical facts to make predictions about future, or otherwise unknown, events
-- Combines techniques from statistics, data mining and machine learning to find meaning from large amounts of data…and predict where you’re going.
-Predictive analytics is the practice of extracting information from existing data sets in order to determine patterns and predict future outcomes and trends.
-- Predictive analytics does not tell you what will happen in the future. It forecasts what might happen in the future with an acceptable level of reliability, and includes what-if scenarios and risk assessment  (http://www.webopedia.com/TERM/P/predictive_analytics.html)
-- Predictive analytics is the branch of data mining concerned with the prediction of future probabilities and trends. (http://searchcrm.techtarget.com/definition/predictive-analytics)
-- While regression analysis is commonly used, there exists another class of methods that deserve proper mentions. E.g. Bayes Network, Artificial Neural Net, Decision Tree, Support Vector Machine, etc. More importantly, the non-linear analysis aspect and the probability based approach that underpin many of the aforementioned methods.
-Bonus:  The following '''Prescriptive Data Analytics''' definitions were offered:
-- Prescriptive analytics goes beyond descriptive and predictive models by recommending one or more courses of action and showing the likely outcome of each decision
-- Prescriptive analytics goes beyond predicting future outcomes by also suggesting actions to benefit from the predictions and showing the decision maker the implications of each decision option.
-Providing examples, use cases, and additional understanding is highly encouraged.
-'''Please contact Steve'''
-We next talked about our two sessions at the Federation Meeting, in January
-- '''Earth Science Data Analytics 101''':
-Purpose:  To ‘educate’ ESIP community on what Earth Science Data Analytics means, and provide exemplary use cases.
-Cluster Goal:  Bring in speakers to provide their Data Analytics Use Cases to stir innovation juices that can generate ideas/techniques/collaborations/etc. that can facilitate/aid usage of data analytics
-Draft Agenda:
-- Introduction to Earth science data analytics – (15 min)
-- 3 or 4 use case speakers (10-15 min each)   I have 2 already…any suggestions
-- Current Data Analytics technologies useful in Earth science (15 min)
-- Panel – Q&A (all speakers)
+Agenda:
-Excellent suggestions were made to ensure speakers targeted our interest in learning how they perform data analytics in their research.
+.  Review ESDA Use Case provided in Google Doc.
+.  Discuss ESIP Summer Meeting theme
-- '''Earth Science Data Analytics 201''':
+.  Open Mic
-Purpose:  To scope a study that would meaningfully benefit the ESIP and broad community; Develop an outline for the study
-Cluster Goal:  Discuss: Publish our findings; Generate a library of Data Analytic methodologies
+===Actions:===
-Discussion and work breakout.  This is where we will further discuss and develop a more detailed outline for a paper that describes Earth science data analytics methodologies.  It seems the bulk of our research at this time would be gathering and characterizing use cases.  This lead to the possibility of creating an Earth science data analytics library of such methodologies.
+Steve - Steve consolidated categories and ESDA group comments in our Google Doc:  https://docs.google.com/spreadsheets/d/108glVB8Rni8M47e5G1_oZ6g1q5AOxzdTbC_P2WrEt6o/edit#gid=0
-More to come.  '''Be a part of some ground breaking work'''
+Done
-The following was provided to initiate discussion of such a paper:
+Ethan M, Robert C, Tiffany M, Steve K - Each person enter up to 2 ESDA use cases into the Google Doc (prototypes entries) to determine if the information we are gathering per project will lead us to be able to categorize the data analytics being utilized.  Due Friday, February 13.
-.  Take what we learn, refine, and define about the different types of Data Analytics
-- Descriptive Analytics
+Active Participants - Review use cases submitted and provide feedback in Google Doc, in comments pull down.  Due for next telecon, February 20.
-- Diagnostic Analytics
-- Discoveritive Analytics
-- Predictive Analytics
-- Prescriptive Analytics
-. Associate exemplary Earth science use cases to each type
-. Associate Data Analytics techniques/tools to each type
+All Other Participants - E-mail Steve (Steven.J.Kempler@nasa.gov) so you can be an Active Participant  Soon.  But don't need to be an Active Participant to review use cases...so please do
-. Associate user categories to each type
-. Describe skills and expertise needed for each type
+Steve - Look at how themes were reflected in past ESIP Meeting agendas.  Report back to group.
-- Currently, we talk about our expertise and experience, but they seldom seem to connect to each other
-- This will help us, the industry, and hopefully, educators, focus their understanding and interests regarding Earth Science Data Analytics.
-REMINDER:  AGU sessions that pertain to Data Analytics:
-- Teaching Science Data Analytics Skills Needed to Facilitate Heterogeneous Data/Information Research:  The Future Is Here - Session ID#: 1879
-- Identifying and Better Understanding Data Science Activities, Experiences, Challenges, and Gaps Areas - Session ID#: 1809
-- Advancing Analytics using Big Data Climate Information System - Session ID#: 3022
-- Big Data in the Geosciences: New Analytics Methods and Parallel Algorithm - Session ID#: 3292
-- Leveraging Enabling Technologies and Architectures to enable Data Intensive Science - Session ID#: 3041
-- Open source solutions for analyzing big earth observation data - Session ID#: 3080
-- Technology Trends for Big Science Data Management - Session ID#: 2525
-===Next Telecon:===
-* No telecon.  Face to face January 7 at the Federation Meeting in Washington
-* Agenda:  See planned sessions above
+All - For next telecon, prepare to discuss ESDA theme for next ESIP Meeting:  Focus of theme, who can we invite as speakers, which other clusters have some relation to the usage of data analytics