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.
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References
Geospatial One Stop (GOS), http://gos2.geodata.gov/wps/portal/gos
Global Change Master Directory Web Site, http://gcmd.nasa.gov/
The Google Maps Javascript API V3, http://code.google.com/apis/maps/documentation/javascript/
Baeza-Yates, R., Ribeiro-Neto, B.: Modern Information Retrieval. ACM Press, New York (1999)
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)
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)
Evans, M.P.: Analysing Google rankings through search engine optimization data. Internet Research 17(1), 21–37 (2007)
Goodchild, M.F., Zhou, J.: Finding geographic information: Collection-level metadata. GeoInformatica 7(2), 95–112 (2003)
Goodchild, M.F.: The Alexandria Digital Library Project: Review, Assessment, and Prospects (2004), http://www.dlib.org/dlib/may04/goodchild/05goodchild.html
Goodchild, M.F., et al.: Sharing Geographic Information: An Assessment of the Geospatial One-Stop. Annals of the AAG 97(2), 250–266 (2007)
Grossner, K.E., et al.: Defining a digital earth system. Transactions in GIS 12(1), 145–160 (2008)
Herring, J.R. (ed.): OpenGIS® Implementation Standard for Geographic information - Simple feature access - Part 1: Common architecture (2010)
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)
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)
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)
Howe, B., et al.: Scientific Mashups: Runtime-Configurable Data Product Ensembles. Scientific and Statistical Database Management, 19–36 (2009)
Kobayashi, M., Takeda, K.: Information retrieval on the web. ACM Comput. Surv. 32, 144–173 (2000)
Lewandowski, D.: Web searching, search engines and Information Retrieval. Information Services and Use 25(3), 137–147 (2005)
Lord, P., Macdonald, A.: e-Science Curation Report (2003), http://www.jisc.ac.uk/uploaded_documents/e-ScienceReportFinal.pdf
Manning, C.D., et al.: An introduction to information retrieval. Cambridge University Press, Cambridge (2008)
Maron, M.E., Kuhns, J.L.: On relevance, probabilistic indexing and information retrieval. Journal of the ACM (JACM) 7(3), 216–244 (1960)
Miller, C.C.: A Beast in the Field: The Google Maps mashup as GIS/2. Cartographica 41(3), 187–199 (2006)
Miller, H.J., Wentz, E.A.: Representation and Spatial Analysis in Geographic Information Systems. Annals of the AAG 93(3), 574–594 (2003)
Montello, D.: The geometry of environmental knowledge. Theories and Methods of Spatio-Temporal Reasoning in Geographic Space, 136–152 (1992)
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)
Sharifzadeh, M., Shahabi, C.: The spatial skyline queries. In: Proc. of VLDB, p. 762 (2006)
Stolte, E., Alonso, G.: Efficient exploration of large scientific databases. In: Proc. of VLDB, p. 633 (2002)
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Megler, V.M., Maier, D. (2011). Finding Haystacks with Needles: Ranked Search for Data Using Geospatial and Temporal Characteristics. In: Bayard Cushing, J., French, J., Bowers, S. (eds) Scientific and Statistical Database Management. SSDBM 2011. Lecture Notes in Computer Science, vol 6809. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-22351-8_4
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DOI: https://doi.org/10.1007/978-3-642-22351-8_4
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-22350-1
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