Contemporary Challenges in Ambient Data Integration for Biodiversity Informatics

  • David Thau
  • Robert A. Morris
  • Sean White
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5872)


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.


Access Control Data Integration Pervasive Computing Biodiversity Study Biodiversity Data 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 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. 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)CrossRefGoogle Scholar
  3. 3.
    Sharkey, M.J.: The all taxa biological inventory of the great smoky mountains national park. The Florida Entomologist 84(4), 556–564 (2001)CrossRefGoogle Scholar
  4. 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)CrossRefGoogle Scholar
  5. 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. 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)CrossRefGoogle Scholar
  7. 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. 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. 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. 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. 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. 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. 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. 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. 15.
    Calder, M., Morris, R.A., Peri, F.: Machine reasoning about anomalous sensor data (2009) (submitted for publication)Google Scholar
  16. 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. 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. 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)CrossRefGoogle Scholar
  19. 19.
    Koperski, M., Sauer, M., Braun, W., Gradstein, S.: Referenzliste der Moose Deutschlands, vol. 34. Schriftenreihe Vegetationsk (2000)Google Scholar
  20. 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. 21.
    Thau, D., Ludascher, B.: Reasoning about taxonomies in first-order logic. Ecological Informatics 2(3), 195–209 (2007)CrossRefGoogle Scholar
  22. 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

Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • David Thau
    • 1
  • Robert A. Morris
    • 2
    • 3
  • Sean White
    • 4
    • 5
  1. 1.Dept. of Computer ScienceUniversity of CaliforniaDavis
  2. 2.University of MassachusettsBoston
  3. 3.Harvard University HerbariaCambridge
  4. 4.Dept. of Computer ScienceColumbia UniversityNY
  5. 5.Dept. of BotanySmithsonian InstitutionWashington

Personalised recommendations