Earth Science Data Analytics/2014-10-23 Telecon

From Federation of Earth Science Information Partners

ESDA Telecom notes – 10/23/14

Known Attendees:

ESIP Host (Erin), Steve Kempler, Jennifer Wei, Beth Huffer, Chung-lin Shie, S Doman Bennett, Seung Hee Kim, Suhung Shen, Tiffany, Eric Kihn, Djorgovski, Beth Huffer, Ethan McMahon, Robert Downs

Agenda:

1 – Steve Kempler - Recap of our last telecon on Descriptive Analytics


2 – Guest Speakers: George Djorgovski, Cal Tech, who is interested in the roles of computation in knowledge discovery.


3 – Discussion: Diagnostic Analytics


4- Steve Kempler - Planning Ahead Discussion

Presentations:


Notes:

Thank you all for attending a very interesting and 'getting more focused' telecon. Much appreciation goes to our speaker, Dr. George Djorgovski, who gave his all interesting insights on the complexity of data analysis, as we are beginning to know it, today.

After a review of the highlights from our Frisco Cluster Meeting, and the discussion about Descriptive Data Analytics from our August telecon (see link to presentation, above), Dr. Djorgovski provided discussion (unfortunately no slides) on his experiences in working with large amounts of multi-variant data. Dr. Djorgovski's relevant interests include: Development of e-Science/Cyber-infrastructure, the roles of computation in knowledge discovery, Astroinformatics, Virtual Observatory, advanced data-mining and exploration techniques. Some of the main points I was able to extract (others please edit in your highlights) include:

- Not only are we dealing with large amounts of data, but we are also dealing with increasing data growth

- What is interesting is the need to deal with multi-dimensional data, fusing data, and matching data to model

- However, there are no tools that can analyze these datasets: Classify, data, look for outliers, correlate multi-dimensional data

- Data rich science: Data contains knowledge, but it is currently not easily obtainable

- Machine learning would be helpful for knowledge discovery

- Need computer scientists, mathematicians, applied computer science

- But once solution is not globally applicable. Tat is why we need to look for commonalities between problems, and domain knowledge of the application

- Caltech, in partnership with JPL, has developed a student curriculum for Big Data Analytics (https://www.coursera.org/course/bigdataschool).

- Discussion: Dealing with data correlation - How to obtain causality? Need more context, and need more variables. Both subject to the need for Data Analytics tools that address causation. Discoverative Data Analysis?


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 Diagnostics Data Analytics.


As a reminder:

Types of Data Analytics

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.

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!


We also noted that the diagram in the presentation, showing the different types of Data Analytics, need to be revised to de-emphasize the data quality and timeliness relationship between the various types. It also needs to be made more applicable to Earth science Data Analytics, thus putting our 'mark' on the subject. Tiffany and I are going to give it a go.


The following Diagnostic Data Analytics definitions were offered:

- Determine why something happened, using content analytics and natural language processing to cull insights found in documents, email, websites, social media and so on. Understand the root cause of geophysical changes through more detailed analysis and visualizations. (modified from: http://www.ibm.com/analytics/us/en/analytics-tools.html)

- Diagnostic analytics looks deeper into what has happened and seeks to understand why a problem or event of interest occurs. How do various measurable events and actions in the focal domain relate to each other? (http://www.lifescaleanalytics.com/files/lifescale/files/brief_descriptivetoprescriptive.pdf)

- Diagnostic data analytics is used to answer the question “Why is it happening?”. It strives to identify root causes, key factors, and unseen patterns (http://webcache.googleusercontent.com/search?q=cache:abygIyZBFLIJ:www.ag-ai.nl/download/17445-21-3-art.Parekh.pdf+&cd=8&hl=en&ct=clnk&gl=us&client=safari)


The following comparison between Descriptive and Diagnostic Data Analytics was also discussed:


Descriptive vs Diagnostic.png

Providing examples, use cases, and additional understanding is highly encouraged.


Interested in participating, please contact Steve:

Our telecon concluded with discussion on authoring a paper along the lines of: Types of Data Analytics Utilized in (the various data analysis phases of) Earth Science.

The following work plan that can lead us to the development of such a paper:

1. Take what we learn, refine, and define about the different types of Data Analytics

- Descriptive Analytics - Diagnostic Analytics - Discoveritive Analytics - Predictive Analytics - Prescriptive Analytics

2. Associate exemplary Earth science use cases to each type

3. Associate Data Analytics techniques/tools to each type

4. Associate user categories to each type

5. Describe skills and expertise needed for each type

- 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:

  • November 20, 3:00 EST
  • Agenda (as of now)

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

- ESDA Activities - Discuss definitions for Discoveritive and Predictive Analytics

- Publish potential - Discuss Cluster collaborative paper loosely entitled: Data Analytics in Earth Science Research… but we can discuss

- Winter ESIP Meeting session planning