Skip to main content

Contemporary Challenges in Ambient Data Integration for Biodiversity Informatics

  • Conference paper

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 5872))

Abstract

Biodiversity informatics (BDI) information is both highly localized and highly distributed. The temporal and spatial contexts of data collection events are generally of primary importance in BDI studies, and most studies are focused around specific localities. At the same time, data are collected by many groups working independently, but often at the same sites, leading to a distribution of data. BDI data are also distributed over time, due to protracted longitudinal studies, and the continuously evolving meanings of taxonomic names. Ambient data integration provides new opportunities for collecting, sharing, and analyzing BDI data, and the nature of BDI data poses interesting challenges for applications of ADI. This paper surveys recent work on utilization of BDI data in the context of ADI. Topics covered include applying ADI to species identification, data security, annotation and provenance sharing, and coping with multiple competing classification ontologies. We conclude with a summary of requirements for applying ADI to biodiversity informatics.

Work supported by NSF awards IIS-0630033 (David Thau), DBI-0646266 (Robert A. Morris), and IIS-03-25867 (Sean White). The first author would like to thank Shawn Bowers and Bertram Ludäscher for many constructive conversations.

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

Buying options

Chapter
USD   29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD   129.00
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD   169.99
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

Learn about institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Gage, S.H.: Observing the acoustic landscape. In: Estrin, D., Michener, W., Bonito, G. (eds.) Environmental Cyberinfrastructure Needs for Distributed Sensor Networks, August 2003, p. 64 (2003)

    Google Scholar 

  2. Belhumeur, P.N., Chen, D., Feiner, S., Jacobs, D.W., Kress, W.J., Ling, H., Lopez, I., Ramamoorthi, R., Sheorey, S., White, S., Zhang, L.: Searching the world’s herbaria: A system for visual identification of plant species. In: Forsyth, D.A., Torr, P.H.S., Zisserman, A. (eds.) ECCV 2008, Part IV. LNCS, vol. 5305, pp. 116–129. Springer, Heidelberg (2008)

    Chapter  Google Scholar 

  3. Sharkey, M.J.: The all taxa biological inventory of the great smoky mountains national park. The Florida Entomologist 84(4), 556–564 (2001)

    Article  Google Scholar 

  4. Porter, J.H., Nagy, E., Kratz, T.K., Hanson, P., Collins, S.L., Arzberger, P.: New eyes on the world: Advanced sensors for ecology. BioScience 59(5), 385–397 (2009)

    Article  Google Scholar 

  5. Burke, J., Estrin, D., Hansen, M., Parker, A., Ramanathan, N., Reddy, S.: Srivastava: Participatory sensing. In: WSW 2006: Mobile Device Centric Sensor Networks and Applications (2006)

    Google Scholar 

  6. Caruana, R., Elhawary, M., Munson, A., Riedewald, M., Sorokina, D., Fink, D., Hochachka, W.M., Kelling, S.: Mining citizen science data to predict revalence of wild bird species. In: KDD 2006, pp. 909–915. ACM, New York (2006)

    Chapter  Google Scholar 

  7. White, S., Marino, D., Feiner, S.: Designing a mobile user interface for automated species identification. In: Rosson, M.B., Gilmore, D.J. (eds.) CHI, pp. 291–294. ACM, New York (2007)

    Google Scholar 

  8. Ling, H., Jacobs, D.W.: Using the inner-distance for classification of articulated shapes. In: CVPR (2), pp. 719–726. IEEE Computer Society, Los Alamitos (2005)

    Google Scholar 

  9. White, S., Feiner, S., Kopylec, J.: Virtual vouchers: Prototyping a mobile augmented reality user interface for botanical species identification. In: Proc. 3DUI 2006 (IEEE Symp. on 3D User Interfaces), pp. 119–126 (2006)

    Google Scholar 

  10. Walters, J.P., Liang, Z., Shi, W., Chaudhary, V.: Wireless sensor network security: A survey. In: Security in distributed, grid, mobile, and pervasive computing, p. 849. CRC Press, Boca Raton (2007)

    Google Scholar 

  11. Cuevas, A., Khoury, P.E., Gomez, L., Laube, A.: Security patterns for capturing encryption-based access control to sensor data. In: SECURWARE 2008, pp. 62–67 (2008)

    Google Scholar 

  12. Chapman, A.D., Grafton, O.: Guide to Best Practices For Generalizing Sensitive Species Occurrence, version 1. Global Biodiversity Information Facility (2008)

    Google Scholar 

  13. Dong, H., Wang, Z., Morris, R., Sellers, D.: Schema-driven security filter generation for distributed data integration. In: Hot Topics in Web Systems and Technologies, pp. 1–6 (2006)

    Google Scholar 

  14. Henricksen, K., Indulska, J.: Modelling and using imperfect context information. In: PERCOMW 2004, Washington, DC, USA, pp. 33–37. IEEE Computer Society, Los Alamitos (2004)

    Google Scholar 

  15. Calder, M., Morris, R.A., Peri, F.: Machine reasoning about anomalous sensor data (2009) (submitted for publication)

    Google Scholar 

  16. Wang, Z., Dong, H., Kelly, M., Macklin, J.A., Morris, P.J., Morris, R.A.: Filtered-push: A map-reduce platform for collaborative taxonomic data management. In: CSIE 2009. IEEE Computer Society, Los Alamitos (2009)

    Google Scholar 

  17. Ye, J., Coyle, L., Dobson, S., Nixon, P.: Ontology-based models in pervasive computing systems. Knowledge Engineering Review 22(4), 315–347 (2007)

    Google Scholar 

  18. Bowers, S., Madin, J.S., Schildhauer, M.P.: A conceptual modeling framework for expressing observational data semantics. In: Li, Q., Spaccapietra, S., Yu, E., Olivé, A. (eds.) ER 2008. LNCS, vol. 5231, pp. 41–54. Springer, Heidelberg (2008)

    Chapter  Google Scholar 

  19. Koperski, M., Sauer, M., Braun, W., Gradstein, S.: Referenzliste der Moose Deutschlands, vol. 34. Schriftenreihe Vegetationsk (2000)

    Google Scholar 

  20. Peet, R.K.: Taxonomic concept mappings for 9 taxonomies of the genus ranunculus published from 1948 to 2004. Unpublished dataset (June 2005)

    Google Scholar 

  21. Thau, D., Ludascher, B.: Reasoning about taxonomies in first-order logic. Ecological Informatics 2(3), 195–209 (2007)

    Article  Google Scholar 

  22. Thau, D., Bowers, S., Ludaescher, B.: Merging sets of taxonomically organized data using concept mappings under uncertainty. In: Meersman, R., Dillon, T., Herrero, P. (eds.) OTM 2009, Part II. LNCS, vol. 5871, pp. 1103–1120. Springer, Heidelberg (2009)

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2009 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Thau, D., Morris, R.A., White, S. (2009). Contemporary Challenges in Ambient Data Integration for Biodiversity Informatics. In: Meersman, R., Herrero, P., Dillon, T. (eds) On the Move to Meaningful Internet Systems: OTM 2009 Workshops. OTM 2009. Lecture Notes in Computer Science, vol 5872. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-05290-3_14

Download citation

  • DOI: https://doi.org/10.1007/978-3-642-05290-3_14

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-05289-7

  • Online ISBN: 978-3-642-05290-3

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics