Efficient Attribute Recommendation with Probabilistic Guarantee

We study how to efficiently solve a primitive data exploration problem: Given two ad-hoc predicates which define two subsets of a relational table, find the top-K attributes whose distributions in the two subsets deviate most from each other. The deviation is measured by $\ell1$ or $\ell2$ distance. The exact approach is to query the full table to calculate the deviation for each attribute and then sort them. It is too expensive for large tables. Researchers have proposed heuristic sampling solutions to avoid accessing the entire table for all attributes. However, these solutions have no theoretical guarantee of correctness and their speedup over the exact approach is limited. In this paper, we develop an adaptive querying solution with probabilistic guarantee of correctness and near-optimal sample complexity. We perform experiments in both synthetic and real-world datasets. Compared to the exact approach implemented with a commercial DBMS, previous sampling solutions achieve up to 2× speedup with erroneous answers. Our solution can produce 25× speedup with near-zero error in the answer.