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Some probabilistic properties were refined. Added a-priori-fpp (for both
the entire filter and each subset), added a-priori-isep (inter-set
errors probability). Flotation feature was removed.
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@@ -9,7 +9,7 @@ Spatial Bloom Filters have been first proposed for use in location-privacy appli
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The libSBF-cpp repository contains the C++ implementation of the SBF data structure. The SBF class is provided, as well as various methods for managing the filter:
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- once the filter is constructed, the user can insert elements into it through the `Insert` method. The `Check` method, on the contrary, is used to verify weather an element belongs to one of the mapped sets.
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- methods `SetAreaFpp`, `GetFilterSparsity`, `GetFilterFpp`, `GetAreaEmersion`and `GetAreaFlotation` allow to compute and return several probabilistic properties of the constructed filter.
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- methods `SetAreaFpp`, `GetFilterSparsity`, `GetFilterFpp`and `GetAreaEmersion` allow to compute and return several probabilistic properties of the constructed filter.
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- finally, two methods are provided to print out the filter: `PrintFilter` prints the filter and related statistics to the standard output whereas `SaveToDisk` writes the filter onto a CSV file.
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For more details on the implementation, and how to use the library please refer to the [homepage](http://sbf.csr.unibo.it/"SBF project homepage") of the project.
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