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Data Mining - Opportunity and Challenge

Data Mining - Opportunity and Challenge. Chair - Al Holm Scribe - Steve Beckwith .ppt - Chuck Pullen Fly on Wall - Lee Anne Willson John Good Bill Dillon. Concept Summary.

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Data Mining - Opportunity and Challenge

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  1. Data Mining - Opportunity and Challenge Chair - Al Holm Scribe - Steve Beckwith .ppt - Chuck Pullen Fly on Wall - Lee Anne Willson John Good Bill Dillon

  2. Concept Summary • Organized data mining may offer a new way for AAVSO to serve the professional astronomy community while encouraging new membership and new life for some existing members. • Some changes to existing data collection and management would be needed. • Entwined with entire concept of data quality of visual observations.

  3. The Promise of Data Mining • Activity for beginner to advanced member • Requires no telescope or observations • Analysis desperately needed, in both AAVSO and other data sets • May appeal to new types of “observers”

  4. The Peril of Data Mining • Requires infrastructure • Infrastructure = $ • Requires Training • Training = $ • Requires evaluation of AAVSO data quality • Evaluation = pain!

  5. Concept Overview- Data Enhancement • Existing data base needs to be upgraded to “validated” • Rapid, machine-based screening of data necessary to keep interest • Some measure of data quality necessary, on both visual, PEP and CCD data. • Vol. archival of FITS CCD frames, with pro level header data, important for NVO (more later).

  6. Concept Overview - Infrastructure • Cross platform, free to user, validated software, from simple educational level to high quality analytical . • Multi level query • Rapid access to new data, perhaps with quality flags • In depth training - quantum enhancement of HOA

  7. Concept Overview- Marketing • Who are the miners? • Recruit via the web, non-astronomy based technology media, Sci. Amer., SETI@HOME example • Be inclusive of diverse levels, 4th graders to PhDs, but be wary of results. • Define goals and recognize work.

  8. Concept Overview - Clients • Who uses the output? • Members • Scientific Community • Educators • The curious...

  9. What about the mass of unevaluated survey data? • Data analysis starts at home • If we build it - they will come • Perfect our system in house first, then invite others to join us.

  10. Potholes on the Info. Super Hwy. • Interest? • Funding (NVO funds possible) • Sociopolitical • Data flow constrictions (CCD Images) • Automation of data quality evaluation

  11. Recommendations • Develop working group to consult. • Combine efforts with overall NVO participation. • Update HOA to reflect new opportunities • Prepare for change…

  12. Conclusion AAVSO’s mission to promote the science of variable stars requires, in addition to the collection of data, using an integrated suite of database design/upgrade, software, and training for data analysis.

  13. CCD/film Data Archival • Important future contribution • Well within goals of AAVSO • Separate but related with data mining • Address as part of NVO

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