# Probabilistic Database Summarization for Interactive Data Exploration

## TL;DR

This paper introduces a probabilistic database summarization method using the Principle of Maximum Entropy, enabling faster approximate query answering for large datasets with improved accuracy over sampling.

## Contribution

It develops a novel probabilistic representation framework for database summarization that enhances query speed and accuracy compared to traditional sampling methods.

## Key findings

- Faster query answering than sampling on large datasets
- Achieves comparable or better accuracy than sampling
- Effectively distinguishes rare and nonexistent values

## Abstract

We present a probabilistic approach to generate a small, query-able summary of a dataset for interactive data exploration. Departing from traditional summarization techniques, we use the Principle of Maximum Entropy to generate a probabilistic representation of the data that can be used to give approximate query answers. We develop the theoretical framework and formulation of our probabilistic representation and show how to use it to answer queries. We then present solving techniques and give three critical optimizations to improve preprocessing time and query accuracy. Lastly, we experimentally evaluate our work using a 5 GB dataset of flights within the United States and a 210 GB dataset from an astronomy particle simulation. While our current work only supports linear queries, we show that our technique can successfully answer queries faster than sampling while introducing, on average, no more error than sampling and can better distinguish between rare and nonexistent values.