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The module contains the implementation of the technique presented in [1] and [2].


For compiling instructions, please see CompileDoc.

An example of how to use the module is available in sepia/


The main class of the module is Sepia which is declared in sepia/sepia.h.

The main methods of Sepia are:

  Sepia(const vector<string> &dataset, 
        unsigned thresholdMin,
        unsigned thresholdMax);
  Sepia(const vector<string> &dataset,
        const string &filename);

  void build();
  void saveData(const string &filename) const;
  float getEstimateSelectivity(const string &query, unsigned editdist) const;

The main idea is that the user can create a Sepia object by specifying a vector of strings (dataset) and a few extra parameters (for details see [1]) or load an existing one from a file. If the object was not loaded, then it needs to be built. Next, the user has the option of saving the object to a file. In order to estimate the selectivity of a given string, the user calls select.


The performance results are available in [1] and [2].


[1] Liang Jin and Chen Li: Selectivity Estimation for Fuzzy String Predicates in Large Data Sets. VLDB 2005: 397-408
[2] Liang Jin, Chen Li, Rares Vernica: SEPIA: estimating selectivities of approximate string predicates in large Databases. VLDB J. 17(5): 1213-1229 (2008)