Hostname: page-component-586b7cd67f-2plfb Total loading time: 0 Render date: 2024-11-23T21:40:40.651Z Has data issue: false hasContentIssue false
  • English
  • Français

From Sky to Earth: Data Science Methodology Transfer

Published online by Cambridge University Press:  30 May 2017

Ashish A. Mahabal Open the ORCID record for Ashish A. Mahabal [Opens in a new window] ,
Daniel Crichton ,
S. G. Djorgovski ,
Emily Law  and
John S. Hughes
Ashish A. Mahabal
Affiliation:
Present address:Center for Data Driven Discovery, Caltech, Pasadena, CA 91125 email: [email protected], [email protected]
Daniel Crichton
Affiliation:
Center for Data Science and Technology, Jet Propulsion Laboratory, Pasadena, CA, 91109 email: [email protected], [email protected], [email protected]
S. G. Djorgovski
Affiliation:
Present address:Center for Data Driven Discovery, Caltech, Pasadena, CA 91125 email: [email protected], [email protected]
Emily Law
Affiliation:
Center for Data Science and Technology, Jet Propulsion Laboratory, Pasadena, CA, 91109 email: [email protected], [email protected], [email protected]
John S. Hughes
Affiliation:
Center for Data Science and Technology, Jet Propulsion Laboratory, Pasadena, CA, 91109 email: [email protected], [email protected], [email protected]
Rights & Permissions [Opens in a new window]

Abstract

Core share and HTML view are not available for this content. However, as you have access to this content, a full PDF is available via the ‘Save PDF’ action button.

We describe here the parallels in astronomy and earth science datasets, their analyses, and the opportunities for methodology transfer from astroinformatics to geoinformatics. Using example of hydrology, we emphasize how meta-data and ontologies are crucial in such an undertaking. Using the infrastructure being designed for EarthCube - the Virtual Observatory for the earth sciences - we discuss essential steps for better transfer of tools and techniques in the future e.g. domain adaptation. Finally we point out that it is never a one-way process and there is enough for astroinformatics to learn from geoinformatics as well.

Keywords

Data ScienceMachine LearningMeta-dataOntologies
Type
Contributed Papers
Information
Proceedings of the International Astronomical Union , Volume 12 , Symposium S325: Astroinformatics , October 2016 , pp. 17 - 26
Copyright
Copyright © International Astronomical Union 2017 

References

Becker, A. C., Wittman, D. M., Boeshaar, , et al., 2004, ApJ, 611, 418 CrossRefGoogle Scholar
Brown, A. G. A., Vallenari, A., & Prusti, T. and the Gaia Collaboration, 2016, A&A, 595, 2 Google Scholar
Brunner, R., Djorgovski, S. G., & Szalay, A. (eds.) 2001, Virtual Observatories of the Future, ASPCS, vol. 225. San Francisco: Astron. Soc. Pacific Google Scholar
Djorgovski, S. G. & Williams, R. 2005, ASPCS, 345, 517. San Francisco: Astron. Soc. Pacific Google Scholar
Djorgovski, S. G., Drake, A. J., Mahabal, A. A., et al., 2011, in “The First Year of MAXI: Monitoring Variable X-ray Sources”, eds. Mihara, T. & Kawai, N., Tokyo: JAXA Special Publ., https://arxiv.org/abs/1102.5004 Google Scholar
Drake, A. J., Djorgovski, S. G., Mahabal, A., et al., 2012, IAUS, 285, 306 Google Scholar
Drake, A. J., Graham, M. J., Djorgovski, S. G., et al., 2014, ApJS, 213, 9 CrossRefGoogle Scholar
Fox, A., Eichelberger, C., Hughes, J., & Lyon, S., 2013, IEEE Big Data Conference, Santa Clara, CAGoogle Scholar
Hall, J. R., Ibrahim, K., Klaus, T. C., et al., 2010, Proc. SPIE, Vol. 7737, 77370HGoogle Scholar
Hanisch, R. J. ADASS XIX, Eds. Mizumoto, Y., & Morita, Koh-Ichiro, and Ohishi, Masatoshi, 2010, ASPCS, 434, 65 Google Scholar
Hughes, J. S., Crichton, D. J., & Mattmann, C. A., 2009, IEEE International Conference on Information Reuse & IntegrationGoogle Scholar
Hughes, J. S., Crichton, D. J., et al., in Special Issue on Enabling Open and Interoperable Access to Planetary Science and Heliophysics Databases and Tools, To be published.Google Scholar
Laurino, O., Busko, I., Cresitello-Dittmar, M., et al., 2013, ASPC, 475, 275 Google Scholar
Laurino, O., Budynkiewicz, J., Busko, , et al. 2014, Astronomy and Computing, 7, 81 Google Scholar
Mahabal, A. A., Djorgovski, S. G., Drake, A. J., et al., 2011, BASI, 39, 387 Google Scholar
Pence, W. D., Chiappetti, L., Page, C. G., Shaw, R. A., & Stobie, E., 2010, A&A, 524, 42 Google Scholar
Raskin, R. & Pan, M., 2003, in ISWC-03, Sanibel Island, Florida, Eds. Naveen Ashish and Carole GobleGoogle Scholar
Ricker, G. R., Winn, J. N., Vanderspek, R., et al., 2015, JATIS, 1, 4003 Google Scholar
Rutledge, G. K., Crichton, D. J., & Alpert, J., 2014, in Earthzine: Earth Science Informatics Theme, posted 29 March 2014Google Scholar
Uschold, M. & Gruninger, M., 2004, SIGMOD Record, 33Google Scholar
Williams, R. J., Barthelmy, S., Denny, R., et al., 2012, in SPIE Conference 8448: Observatory Operations: Strategies, Processes, and Systems IVGoogle Scholar
Zachman, J. A., 1987, IBM Syst. J., 26, 276 Google Scholar
All URLs accessed 18 Dec 2016.Google Scholar