Finding Haystacks with Needles: Ranked Search for Data Using Geospatial and Temporal Characteristics

  • V. M. Megler
  • David Maier
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6809)

Abstract

The past decade has seen an explosion in the number and types of environmental sensors deployed, many of which provide a continuous stream of observations. Each individual observation consists of one or more sensor measurements, a geographic location, and a time. With billions of historical observations stored in diverse databases and in thousands of datasets, scientists have difficulty finding relevant observations. We present an approach that creates consistent geospatial-temporal metadata from large repositories of diverse data by blending curated and automated extracts. We describe a novel query method over this metadata that returns ranked search results to a query with geospatial and temporal search criteria. Lastly, we present a prototype that demonstrates the utility of these ideas in the context of an ocean and coastalmargin observatory.

Keywords

spatio-temporal queries querying scientific data metadata 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Geospatial One Stop (GOS), http://gos2.geodata.gov/wps/portal/gos
  2. 2.
    Global Change Master Directory Web Site, http://gcmd.nasa.gov/
  3. 3.
  4. 4.
    Baeza-Yates, R., Ribeiro-Neto, B.: Modern Information Retrieval. ACM Press, New York (1999)Google Scholar
  5. 5.
    Douglas, D.H., Peucker, T.K.: Algorithms for the reduction of the number of points required to represent a digitized line or its caricature. Cartographica 10(2), 112–122 (1973)CrossRefGoogle Scholar
  6. 6.
    Egenhofer, M.J.: Toward the semantic geospatial web. In: Proceedings of the 10th ACM International Symposium on Advances in Geographic Information Systems, pp. 1–4 (2002)Google Scholar
  7. 7.
    Evans, M.P.: Analysing Google rankings through search engine optimization data. Internet Research 17(1), 21–37 (2007)CrossRefGoogle Scholar
  8. 8.
    Goodchild, M.F., Zhou, J.: Finding geographic information: Collection-level metadata. GeoInformatica 7(2), 95–112 (2003)CrossRefGoogle Scholar
  9. 9.
    Goodchild, M.F.: The Alexandria Digital Library Project: Review, Assessment, and Prospects (2004), http://www.dlib.org/dlib/may04/goodchild/05goodchild.html
  10. 10.
    Goodchild, M.F., et al.: Sharing Geographic Information: An Assessment of the Geospatial One-Stop. Annals of the AAG 97(2), 250–266 (2007)Google Scholar
  11. 11.
    Grossner, K.E., et al.: Defining a digital earth system. Transactions in GIS 12(1), 145–160 (2008)CrossRefGoogle Scholar
  12. 12.
    Herring, J.R. (ed.): OpenGIS® Implementation Standard for Geographic information - Simple feature access - Part 1: Common architecture (2010)Google Scholar
  13. 13.
    Hey, T., Trefethen, A.: e-Science and its implications. Philosophical Transactions of the Royal Society of London. Series A: Mathematical, Physical and Engineering Sciences 361(1809), 1809 (2003)CrossRefGoogle Scholar
  14. 14.
    Hey, T., Trefethen, A.E.: The Data Deluge: An e-Science Perspective. In: Berman, F., Fox, G., Hey, T. (eds.) Grid Computing: Making the Global Infrastructure a Reality, pp. 809–824. John Wiley & Sons, Ltd., Chichester (2003)CrossRefGoogle Scholar
  15. 15.
    Hill, L.L., et al.: Collection metadata solutions for digital library applications. J. of the American Soc. for Information Science 50(13), 1169–1181 (1999)CrossRefGoogle Scholar
  16. 16.
    Howe, B., et al.: Scientific Mashups: Runtime-Configurable Data Product Ensembles. Scientific and Statistical Database Management, 19–36 (2009)Google Scholar
  17. 17.
    Kobayashi, M., Takeda, K.: Information retrieval on the web. ACM Comput. Surv. 32, 144–173 (2000)CrossRefGoogle Scholar
  18. 18.
    Lewandowski, D.: Web searching, search engines and Information Retrieval. Information Services and Use 25(3), 137–147 (2005)Google Scholar
  19. 19.
    Lord, P., Macdonald, A.: e-Science Curation Report (2003), http://www.jisc.ac.uk/uploaded_documents/e-ScienceReportFinal.pdf
  20. 20.
    Manning, C.D., et al.: An introduction to information retrieval. Cambridge University Press, Cambridge (2008)CrossRefMATHGoogle Scholar
  21. 21.
    Maron, M.E., Kuhns, J.L.: On relevance, probabilistic indexing and information retrieval. Journal of the ACM (JACM) 7(3), 216–244 (1960)CrossRefGoogle Scholar
  22. 22.
    Miller, C.C.: A Beast in the Field: The Google Maps mashup as GIS/2. Cartographica 41(3), 187–199 (2006)CrossRefGoogle Scholar
  23. 23.
    Miller, H.J., Wentz, E.A.: Representation and Spatial Analysis in Geographic Information Systems. Annals of the AAG 93(3), 574–594 (2003)Google Scholar
  24. 24.
    Montello, D.: The geometry of environmental knowledge. Theories and Methods of Spatio-Temporal Reasoning in Geographic Space, 136–152 (1992)Google Scholar
  25. 25.
    Perlman, E., et al.: Data Exploration of Turbulence Simulations Using a Database Cluster. In: Proceedings of the 2007 ACM/IEEE Conference on Supercomputing, pp. 1–11 (2007)Google Scholar
  26. 26.
    Sharifzadeh, M., Shahabi, C.: The spatial skyline queries. In: Proc. of VLDB, p. 762 (2006)Google Scholar
  27. 27.
    Stolte, E., Alonso, G.: Efficient exploration of large scientific databases. In: Proc. of VLDB, p. 633 (2002)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • V. M. Megler
    • 1
  • David Maier
    • 1
  1. 1.Computer Science DepartmentPortland State UniversityUSA

Personalised recommendations