Skip to main content
SpringerLink
Log in

Exploratory Analysis of Light Curves: A Case-Study in Astronomy Data Understanding

  • Conference paper
Databases in Networked Information Systems (DNIS 2014)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 8381))

Included in the following conference series:

  • 1416 Accesses

Abstract

Data acquisition in Biology and Astronomy has seen unprecedented growth in volume since the turn of the century. It will not be an exaggeration to state that the needs of these two sciences are pushing computer science research to new frontiers. The focus of this paper is astronomy, which since inception of Virtual Observatory and commissioning of massive sky surveys is gasping for knowledge in data deluge.

Astrocomputing, which subsumes Astroinformatics, is a recent multi-disciplinary field of research with computer science and astronomy at the core. In this article we dwell upon the opportunities and challenges for machine learning and data mining research thrown open by this emerging discipline. We present a case study of an ongoing work on exploratory analysis of unclassified light curves. Though scientific analysis and interpretation of the results of the study are pending, the exercise demonstrates the merit of customized exploratory approach for study. The approach is general and can be applied to light curves obtained from any survey. Owing to the gargantuan scale of astronomy data processing requirements, we discuss scalability of the proposed method.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 39.99
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. http://www.ivoa.net/

  2. http://www.sdss.org/

  3. http://crts.caltech.edu/

  4. http://ptf.caltech.edu/iptf/

  5. http://www.sciops.esa.int/index.php?project=ASTROF&page=index

  6. http://wise.ssl.berkeley.edu/astronomers.html

  7. http://www.lsst.org/lsst/

  8. https://www.skatelescope.org/

  9. http://www.astro.princeton.edu/PBOOK/datasys/datasys.htm

  10. http://www.startap.net/starlight/

  11. http://boinc.berkeley.edu/

  12. http://avyakta.caltech.edu/science/datasets/SAMSI_DC/index.html

  13. http://nirgun.caltech.edu:8000/scripts/description.html

  14. Mahabal, A., Djorgovski, S., Drake, A., Donalek, C., et al.: Discovery, classification, and scientific exploration of transient events from the Catalina Real-time Transient Survey, arXiv:1111.0313v1

    Google Scholar 

  15. Aggarwal, C.C. (ed.): Data Streams - Models and Algorithms. Advances in Database Systems, vol. 31. Springer (2007)

    Google Scholar 

  16. Agrawal, R., Gehrke, J., Gunopulos, D., Raghavan, P.: Automatic subspace clustering of high dimensional data for data mining applications. SIGMOD Rec. 27(2), 94–105 (1998)

    Article  Google Scholar 

  17. Szalay, A.S., Kunszt, P., Thakar, A., Gray, J., Slutz, D.: The Sloan Digital Sky Survey and its Archive, arXiv:astro-ph/9912382v1

    Google Scholar 

  18. Ball, N.M., Brunner, R.J.: Data mining and machine learning in astronomy. International Journal of Modern Physics D 19(07), 1049–1106 (2010)

    Article  MATH  Google Scholar 

  19. Bhaduri, K., Das, K., Borne, K.D., Giannella, C., Mahule, T., Kargupta, H.: Scalable, asynchronous, distributed eigen monitoring of astronomy data streams. Statistical Analysis and Data Mining 4(3), 336–352 (2011)

    Article  MathSciNet  Google Scholar 

  20. Bhatnagar, V., Dobariyal, R., Jain, P., Mahabal, A.: Data understanding using semi-supervised clustering. In: CIDU, pp. 118–123. IEEE (2012)

    Google Scholar 

  21. Bhatnagar, V., Kaur, S., Chakravarthy, S.: Clustering data streams using grid-based synopsis. Knowledge and Information Systems, 1–26 (2013)

    Google Scholar 

  22. Chen, Y., Alspaugh, S., Katz, R.: Interactive analytical processing in big data systems: A cross-industry study of mapreduce workloads. Proc. VLDB Endow. 5(12) (August 2012)

    Google Scholar 

  23. de Andrade Silva, J., Faria, E.R., Barros, R.C., Hruschka, E.R., de Carvalho, A.C.P.L.F., Gama, J.: Data stream clustering: A survey. ACM Comput. Surv. 46(1), 13 (2013)

    Google Scholar 

  24. Dean, J., Ghemawat, S.: Mapreduce: Simplified data processing on large clusters. In: Proceedings of the 6th Symposium on Operating System Design and Implementation, pp. 137–150 (2004)

    Google Scholar 

  25. Domingos, P., Hulten, G.: Mining high-speed data streams. In: Proceedings of the Sixth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 71–80. ACM, New York (2000)

    Chapter  Google Scholar 

  26. Dutta, H., Giannella, C., Borne, K.D., Kargupta, H.: Distributed top-k outlier detection from astronomy catalogs using the demac system. In: SDM (2007)

    Google Scholar 

  27. The Apache Software Foundation. Welcome to HadoopTM; Distributed File System (2007)

    Google Scholar 

  28. Han, J., Kamber, M., Pei, J.: Data Mining: Concepts and Techniques, 3rd edn. Morgan Kaufmann Publishers Inc., San Francisco (2011)

    Google Scholar 

  29. Heer, J., Kandel, S.: Interactive analysis of big data. XRDS 19(1), 50–54 (2012)

    Article  Google Scholar 

  30. Heer, J., Shneiderman, B.: Interactive dynamics for visual analysis. Commun. ACM 55(4), 45–54 (2012)

    Article  Google Scholar 

  31. Mambretti, M.B.J., DeFanti, T.: Starlight: Next-generation communication services, exchanges, and global facilities (chapter). Advances in Computer 80, 191–207 (2010)

    Article  Google Scholar 

  32. Karloff, H., Suri, S., Vassilvitskii, S.: A model of computation for mapreduce. In: Proceedings of the Twenty-First Annual ACM-SIAM Symposium on Discrete Algorithms, SODA 2010, pp. 938–948. Society for Industrial and Applied Mathematics, Philadelphia (2010)

    Chapter  Google Scholar 

  33. Keim, D.A.: Visual exploration of large data sets. Commun. ACM 44(8), 38–44 (2001)

    Article  Google Scholar 

  34. Borne, K.D.: Astroinformatics: A 21st Century Approach to Astronomy, arXiv:0909.3892v1

    Google Scholar 

  35. Borne, K.D.: Scientific Data Mining in Astronomy, arXiv:0911.0505v1

    Google Scholar 

  36. Nigro, S.E.G.C., Oscar, H., Xodo, D.H.: Data Mining with Ontologies: Implementations, Findings, and Frameworks. IGI Global (2008)

    Google Scholar 

  37. Lupton, R., Gunn, J.E., Ivezic, Z., Knapp, G.R., Kent, S., Yasuda, N.: The SDSS Imaging Pipelines, arXiv:astro-ph/0101420v2

    Google Scholar 

  38. Kaur, S., Saxena, R., Khanna, D., Bhatnagar, V.: Comparing data processing frameworks for scalable clustering. To appear in Proceedings of FLAIRS 2014, to be held in (May 2016)

    Google Scholar 

  39. Simoff, S.J., Maher, M.L.: Ontology-based multimedia data mining for design information retrieval. In: Proceedings of ACSE Computing Congress, vol. 320, ACSE, Cambridge (1998)

    Google Scholar 

  40. Singh, S., Vajirkar, P., Lee, Y.: Context-based data mining using ontologies. In: Song, I.-Y., Liddle, S.W., Ling, T.-W., Scheuermann, P. (eds.) ER 2003. LNCS, vol. 2813, pp. 405–418. Springer, Heidelberg (2003)

    Chapter  Google Scholar 

  41. Thompson, D., Burke-Spolaor, S., Deller, A., Majid, W., Palaniswamy, D., Tingay, S., Wagstaff, K., Wayth, R.: Real time adaptive event detection in astronomical data streams: Lessons from the very long baseline array. IEEE Intelligent Systems 99, 1 (2013)

    Google Scholar 

  42. York, D.G., et al.: The Sloan Digital Sky Survey: Technical Summary. Astron. J. 120, 1579–1587 (2000)

    Article  Google Scholar 

  43. Zudilova-Seinstra, E., Adriaansen, T., van Liere, R.: Trends in Interactive Visualization: State-of-the-Art Survey, 1st edn. Springer Publishing Company, Incorporated (2008)

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Hans Raj College, University of Delhi, Delhi, India

    Aditi Mittal

  2. Independent Researcher, India

    Abhishek Santra

  3. South Asian University, New Delhi, India

    Vasudha Bhatnagar

  4. Acharya Narendra Dev College, University of Delhi, Delhi, India

    Dhriti Khanna

Authors
  1. Aditi Mittal
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Abhishek Santra
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Vasudha Bhatnagar
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Dhriti Khanna
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Editors and Affiliations

  1. University of Aizu, Aizu Wakamatsu Shi, 965-8580, Fukushima Ken, Japan

    Aastha Madaan

  2. University of Aizu, Aizu Wakamatsu, 965-8580, Fukushima, Japan

    Shinji Kikuchi

  3. Graduate Department of Computer and Information Systems, University of Aizu, Ikki Machi, Aizu-Wakamatsu, 965-8580, Fukushima, Japan

    Subhash Bhalla

Rights and permissions

Reprints and permissions

Copyright information

© 2014 Springer International Publishing Switzerland

About this paper

Cite this paper

Mittal, A., Santra, A., Bhatnagar, V., Khanna, D. (2014). Exploratory Analysis of Light Curves: A Case-Study in Astronomy Data Understanding. In: Madaan, A., Kikuchi, S., Bhalla, S. (eds) Databases in Networked Information Systems. DNIS 2014. Lecture Notes in Computer Science, vol 8381. Springer, Cham. https://doi.org/10.1007/978-3-319-05693-7_5

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-05693-7_5

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-05692-0

  • Online ISBN: 978-3-319-05693-7

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation