Difference between revisions of "Earth Science Data Analytics/2014-03-20 Telecon"

Revision as of 14:16, March 21, 2014

ESDA Telecom notes – 3/20/14

Known Attendees:

ESIP Host (Carol or Erin)
Bamshad Mobasher
Steve Kempler
Seung Hee Kim
John Schnase
Joan Aron
Helen Conover
Robert Downs
Ari Posner
Emily Law
fritz vanwijngaarden
chung-lin shie
Jennifer Davis
Rama
Bruce Caron
Brand Niemann
Anjanette Hawk
Rudy Husar
Thomas Huang
Deborah Smith
Smiley
John Farley
Sara Graves
Beth Huffer

Agenda:

1 Topics to better understand, so far:

- Dr. Brand Niemann, Director and Senior Data Scientist, Semantic Community: Sorting out Data Science and Data Analytics

2 Two Guest Speakers – What are people doing with Data Analytics

- Dr. John Schnase, NASA/GSFC: Hands on Experience: Big Data Challenges

- Prof. Bamshad Mobasher, Professor of Data Analytics, DePaul Univeristy: Data Analytics Masters Degree Overview

3 ESDA Activities Discussion: These are solid activities that have been suggested so far:

- Compile use cases (include producer/supplier and data user analytics utilization)

- Compile analytics tools (internal and external to ESIP)

- Do gap analysis

Referenced Material:

Presentations:

Notes:

Good News: We had 3 excellent speakers to discuss: Data Science/Data Analytics (Brand Niemann); An application of Data Analytics on the MERRA (data assimilation) dataset (John), and; Data Analytics Master Program Approach/Overview at DePaul University (Bamshad)

Bad News: The Telecom Convener did not plan enough time for what the presentations deservingly used, thus we did not get to Agenda Item 3. (More on item 3 later)

The cluster started with some thoughts for Cluster objectives and direction based on February’s telecom ideas (see notes from February’s telecom). Basically, It seems that this Cluster can serve multiple purposes to address the various levels of members understanding and interests regarding Data Analytics. This includes: - ‘Academic’ discussions that allow all of us to be better educated and on the same page in understanding the various aspects of Data Analytics - Bringing in guest speakers to describe overviews of external efforts and further teach us about the broader use of Data Analytics. (We can always invite speakers back to learn more) - Activities that ESIP members can actually address and tackle

As a start, this will lay groundwork for our understanding, as the field evolves, and the individual and collective interests of this cluster evolve, in turn, the cluster objectives can evolve. This will be put out as the basis of the ESDA cluster mission/objectives. Please take a look at tit at the top of our Wiki ‘ESDA Home Page’. Please provide comments on what you think of it, does it address your expectations, and/or what else we should include.

Take a look at Brand’s presentation. It provides a real breadth of information regarding Data Science, Data Analytics, what Data Scientists do, current activities in the field, more. Remember: ‘…try to make a story out of the data’.

John’s presentation was equally interesting, describing how he applies analytics (MapReduce) to the MERRA datasets.

Not to be outdone, Bamshad gave a great overview of DePaul University’s Data Analytics program, the types of course taught, a little philosophy behind the program, and the domain areas on which the program focus.

BTW, here is my new favorite predictive analytics figure describing the CRISP-DM process found in both, Brand and Bamshad’s presentations. Only I would substitute ‘Business Understanding’ with ‘Domain Expertise’, to make it more generic.

 Time ran out to discuss the third agenda item. This will be discussed at the next telecom (April 17), and provided here for your contemplation: ESDA Activity - Compile use cases (include producer/supplier and data user analytics utilization) - Need 2 to 4 owners - Compile analytics tools (internal and external to ESIP) – Need 2 to 4 owners (preferably different) - Do gap analysis – Need to 2 to 4 owners (different or some from above groups)

And Potential Future Activities (as of today) - Examine project long case studies to determine successfulness of using data analytics in the project (i.e., lessons learned) - Oh yeah: Create a Cluster Mission Statement and Objectives - Report out to the Federation All

For reference, I repeat some of the key ideas that came out of the February telecom. ▪ We can define the analytics toolset (focusing on Earth science) ▪ We can assemble end-to-end team(s) that together address various aspects of data analytics (and, more broadly, Data Science. This would also surface gaps in our expertise. ▪ We can better understand and provide potential ESIP expertise to NIST activities ▪ Data Supplier vs. Data User perspectives. We can surface/organize the analytics needs and use cases from both perspectives ( ▪ Another dimension of delineating Data Scientist and Data Analytics is along the Data Creator/Provider < --- > Data End User axis. -- The perspectives and the needs of Data Science and Data Analytics are very different where you are along that axis. -- Typically a real gap exists between the two perspectives, ▪ Idea: We can consider focusing on the collection of case studies where organizations have implemented big data solutions to problems, carried out analytics, quality assurance, and have allowed policy makers to make informed decisions based on the end products of data science. From this body of work, which can highlight both successes and failures, I think that the group can begin to form recommendations on how organizations should proceed in data science based on their particular goals. It can also serve as a bed of research for data scientists and IT staff to consider alternatives to their own approaches.

Next Telecon:

April 17, 3:00 EST (third Thursday of each month)
Agenda (as of now)

- Analytics related topic to better understand. DOES ANYBODY HAVE A TOPIC THEY WISH TO BETTER UNDERTSAND

- Listen and Learn - We will have 2 guest speakers to discuss their Analytics activities

- ESDA Activities

@@ Line 67: / Line 67: @@
 Bad News: The Telecom Convener did not plan enough time for what the presentations deservingly used, thus we did not get to Agenda Item 3. (More on item 3 later)
+The cluster started with some thoughts for Cluster objectives and direction based on February’s telecom ideas (see notes from February’s telecom).  Basically, It seems that this Cluster can serve multiple purposes to address the various levels of members understanding and interests regarding Data Analytics.  This includes:
+-  ‘Academic’ discussions that allow all of us to be better educated and on the same page in understanding the various aspects of Data Analytics
+-  Bringing in guest speakers to describe overviews of external efforts and further teach us about the broader use of Data Analytics.  (We can always invite speakers back to learn more)
+-  Activities that ESIP members can actually address and tackle
-More than 40 people attended this telecom.  Interest is high.  As in any start-up group addressing an area with extensive components that can be addressed in various ways, we too will coalesce in one or maybe more directions.
+As a start, this will lay groundwork for our understanding, as the field evolves, and the individual and collective interests of this cluster evolve, in turn, the cluster objectives can evolve.
+This will be put out as the basis of the ESDA cluster mission/objectives.  Please take a look at tit at the top of our Wiki ‘ESDA Home Page’.
+Please provide comments on what you think of it, does it address your expectations, and/or what else we should include.
-The purpose of this telecom was to initiate discussion on Earth Science Data Analytics and the Data Scientist to start the coalescing process that would result in ESIP contributions to, ultimately, facilitate the advancement of Earth science.
+Take a look at Brand’s presentation.  It provides a real breadth of information regarding Data Science, Data Analytics, what Data Scientists do, current activities in the field, more.  Remember: ‘…try to make a story out of the data’.
-The following show the process commencing and several potential actionable ideas that have so far come forth.  Please feel free to add additional comments to the meeting notes or send me an e-mail.
+John’s presentation was equally interesting, describing how he applies analytics (MapReduce) to the MERRA datasets.
-External Activities:
+Not to be outdone, Bamshad gave a great overview of DePaul University’s Data Analytics program, the types of course taught, a little philosophy behind the program, and the domain areas on which the program focus.
-* We should look at inventory activities pursued outside ESIP (Emily L)
+BTW, here is my new favorite predictive analytics figure describing the CRISP-DM process found in both, Brand and Bamshad’s presentations.  Only I would substitute ‘Business Understanding’ with ‘Domain Expertise’, to make it more generic.
-* John Schnase (GSFC) has relevant activities related to ‘Climate Analytics-as-a-Service’  (Chris L)
-* We should also look into inviting individuals from other groups (e.g., CODATA, NSF, IEEE)  (Bob C, who will help look for/provide points of contact)
-Information Sharing:
-* There is a growing amount of literature addressing data analytics.  E.g., “Doing Data Science” by Cathy O’Neil (Bob C)
-* Very nice presentation:  ‘Demystifying Data Science’ by Natasha Balac (http://bigdatawg.nist.gov/_uploadfiles/M0169_v1_9072641833.pdf).  I am curious how/if you ESIP Data Scientists resonate with this presentation
-* NIST provides an excellent list of ‘Big Data Analytics’ reading material:  http://bigdatawg.nist.gov/_uploadfiles/M0264_v1_5728417524.pdf
-Ideas (potential direction) and Other Notes:
+ Time ran out to discuss the third agenda item.  This will be discussed at the next telecom (April 17), and provided here for your contemplation:
-* Idea:  What does analytics mean in Earth science.  Currently, tools are crude.  We can we help users find what they are looking for (Chris L)
+ESDA Activity
-* Idea:  We can define the analytics toolset (focusing on Earth science) (Sara G)?
+-	Compile use cases (include producer/supplier and data user analytics utilization) - Need 2 to 4 owners
-* Idea:   We can assemble end-to-end team(s) that together address various aspects of data analytics (and, more broadly, Data Science.  This would also surface gaps in our expertise. (Bob C)
+-	Compile analytics tools (internal and external to ESIP) – Need 2 to 4 owners (preferably different)
-* Note:  Data Science is much bigger than analytics (Sara, others).  Thus, let’s not treat them the same.  (We can address both topics, but not as one topic)
+-	Do gap analysis – Need to 2 to 4 owners (different or some from above groups)
-RDA Highlights (thanks to Rahul)
+And Potential Future Activities (as of today)
-* Idea: We can provide ESIP Earth science expertise to support RDA activities (e.g.,use cases)  (Sara G, Nancy H)
+- Examine project long case studies to determine successfulness of using data analytics in the project (i.e., lessons learned)
-* Idea: We can identify cross domain commonalities (Emily L)
+- Oh yeah:  Create a Cluster Mission Statement and Objectives
+- Report out to the Federation All
-NIST highlights (thanks to Wo) – See presentation
-* Idea: We can better understand and provide potential ESIP expertise to NIST activities
-Post Telecom Comments:
-* Idea:  Data Supplier vs. Data User perspectives.  We can surface/organize the analytics needs and use cases from both perspectives (as noted below, related Bob’s idea above)
-Comment 1 (from Rudy H):
-* Another dimension of delineating Data Scientist and Data Analytics is along the Data Creator/Provider < --- > Data End User axis.   -- The perspectives and the needs of Data Science and Data Analytics are very different where you are along that axis.   -- Typically a real gap exists between the two perspectives,
-Comment 2 (from Joan A):
-* My main comment is that the telecom tended to focus more on the suppliers of tools.   This should be complemented by attention to the demand side.  I am thinking of environmental monitoring and protection decision-makers who need interaction with the suppliers of the technologies.  ESIP has a niche in contributing to this understanding.    Bob Chen's comments about examining the whole process and comments about use cases fit in here.  I have a particular interest in the perspective as a user in how data analytics and sharing can support better decisions linking environmental protection and public health.
-* Idea:  We can consider focusing on the collection of case studies where organizations have implemented big data solutions to problems, carried out analytics, quality assurance, and have allowed policy makers to make informed decisions based on the end products of data science.  From this body of work, which can highlight both successes and failures, I think that the group can begin to form recommendations on how organizations should proceed in data science based on their particular goals.  It can also serve as a bed of research for data scientists and IT staff to consider alternatives to their own approaches. (Rob C)
+For reference, I repeat some of the key ideas that came out of the February telecom.
+▪	We can define the analytics toolset (focusing on Earth science)
+▪	We can assemble end-to-end team(s) that together address various aspects of data analytics (and, more broadly, Data Science. This would also surface gaps in our expertise.
+▪	We can better understand and provide potential ESIP expertise to NIST activities
+▪	Data Supplier vs. Data User perspectives. We can surface/organize the analytics needs and use cases from both perspectives (
+▪	Another dimension of delineating Data Scientist and Data Analytics is along the Data Creator/Provider < --- > Data End User axis. -- The perspectives and the needs of Data Science and Data Analytics are very different where you are along that axis. -- Typically a real gap exists between the two perspectives,
+▪	Idea: We can consider focusing on the collection of case studies where organizations have implemented big data solutions to problems, carried out analytics, quality assurance, and have allowed policy makers to make informed decisions based on the end products of data science. From this body of work, which can highlight both successes and failures, I think that the group can begin to form recommendations on how organizations should proceed in data science based on their particular goals. It can also serve as a bed of research for data scientists and IT staff to consider alternatives to their own approaches.
 ===Next Telecon:===