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The problem of approximate matching is thus transformed into one of computing nearest neighbors using a spatial index.
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Another prime concern is efficient query execution, which can be resolved by using a spatial indexing structure for fast data access.
The algorithm first preprocesses the compound dataset by embedding it into a high-dimensional Euclidean space and generating a spatial index using LSH.
One of these approximation approaches utilizes a spatial index using locality sensitivity hashing (LSH) to perform the fast nearest neighbor search in Euclidean space.
This limitation can be addressed by embedding compounds in Euclidean space and building for them a spatial index using induced vectors in embedding space.
In most cases, a SAM uses an explicit structure called a spatial index.
Habitat fragmentation in meadows of Posidonia oceanica, the most important and abundant seagrass in the Mediterranean Sea, was investigated at a region-wide spatial scale using a synthetic ecological index, the Patchiness Index (PI).
Using a performance index based on spatial overlay analysis, we found that the fundamental eco-land covered up to 65% of the ecological redlining areas at the national scale, but not in some physical geographical regions.
Indexing can be used on one or several fields (implemented using B-trees), with the possibility of using two-dimensional spatial indexes for geometry-based data [10].
To achieve fair comparisons, we also used a grid-based spatial index in GROMACS [9] for the RNG and NN queries.
In this study, we investigated changes in landscape structure and the ecological consequences of urbanization in the Twin Cities Metropolitan Area using spatial indices, change analysis, and geospatial modeling.
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