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Spatial Indexing of Large Volume Bathymetric Data

Spatial Indexing of Large Volume Bathymetric Data. by Bradford G. Nickerson University of New Brunswick Faculty of Computer Science Fredericton, New Brunswick Canada joint work with Feng Gao. Bathymetric Data Observation. Simrad ME70 transducer array. Frequency range: 70 to 120 kHz

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Spatial Indexing of Large Volume Bathymetric Data

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  1. Spatial Indexing of Large Volume Bathymetric Data by Bradford G. Nickerson University of New Brunswick Faculty of Computer Science Fredericton, New Brunswick Canada joint work with Feng Gao

  2. Bathymetric Data Observation Simrad ME70 transducer array Frequency range: 70 to 120 kHz 800 Tx + 800 Rx channels

  3. Vessel Track

  4. Profiles

  5. Edit + GriddingContours

  6. Navigation Errors – Oops!

  7. Experimental DataConceptionBay, Nfld.57,192profilesin 49 lines130 MB

  8. Summary • R-tree average 4.6 times less time to search R-tree indices (compared to Morton code) • Space requirement about 1.8% of original data size • Range deletion average 17 times faster compared to original R-tree deletion • RDBMS (INGRES) took an average of 1,535 times more CPU time for search, 4.5 times more space

  9. Curse of Dimensionality • k-d tree range search time O(n1-1/d+F), where F is the number of points in range [Lee and Wong, 1977] For uniform, random points in [0,1]d, query square W with side length ,  = d-th root of E(F)/n. e.g. E(F)/n=0.001, for d=2, =0.0011/2=0.03; for d=20, =0.0011/20=0.79

  10. Sensor Web Language (SWL) Software Architecture

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