femy-joseph/SeeDB-Efficient-visualization-recommendations

We propose SeeDB, a visualization recommendation engine to facilitate fast visual analysis: given a subset of data to be studied, SeeDB intelligently explores the space of visualizations, evaluates promising visualizations for trends, and recommends those it deems most "useful" or "interesting".

SeeDB: efficient data-driven visualization recommendations to support visual analytics

We implemented the algorithm based on the definition in Section 2 of the paper, Shared-based Optimization (through query rewriting) in Section 4.1, and Pruning-based Optimization (using Hoeffding-Serfling inequality) in Section 4.2. In evaluation, We usec the census data set. Set the user-specified query to include the married people, and the reference query to include unmarried people. Used the K-L Divergence as the utility measure.

The Project Consists of Code and Data folders

Code : Preprocess.py : Data preprocessing

Main.py :Share-based optimization
Pruning.py :Pruning based optimization
Utils.py :helper functions

Data : Census dataset