# Reinforced Approximate Exploratory Data Analysis

arXiv: https://arxiv.org/abs/2212.06225 · HTML: https://mlsanity.com/paper/2212.06225/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 20 of its 52 references that are in the map.
- First posted on arXiv 12 Dec 2022.

## Builds on (most cited first, 20 of 20)

- [Continuous control with deep reinforcement learning](https://mlsanity.com/paper/1509.02971/) (2015)
- [Playing Atari with Deep Reinforcement Learning](https://mlsanity.com/paper/1312.5602/) (2013)
- [Asynchronous Methods for Deep Reinforcement Learning](https://mlsanity.com/paper/1602.01783/) (2016)
- [Dialog-based Interactive Image Retrieval](https://mlsanity.com/paper/1805.00145/) (2018)
- [Interactive Path Reasoning on Graph for Conversational Recommendation](https://mlsanity.com/paper/2007.00194/) (2020)
- [Dialogue Learning with Human Teaching and Feedback in End-to-End Trainable Task-Oriented Dialogue Systems](https://mlsanity.com/paper/1804.06512/) (2018)
- [SECTOR: A Neural Model for Coherent Topic Segmentation and Classification](https://mlsanity.com/paper/1902.04793/) (2019)
- [How to Build User Simulators to Train RL-based Dialog Systems](https://mlsanity.com/paper/1909.01388/) (2019)
- [Conversational Contextual Bandit: Algorithm and Application](https://mlsanity.com/paper/1906.01219/) (2019)
- [Multi-Agent Task-Oriented Dialog Policy Learning with Role-Aware Reward Decomposition](https://mlsanity.com/paper/2004.03809/) (2020)
- [TIAGE: A Benchmark for Topic-Shift Aware Dialog Modeling](https://mlsanity.com/paper/2109.04562/) (2021)
- [Reinforcement Learning from Reformulations in Conversational Question Answering over Knowledge Graphs](https://mlsanity.com/paper/2105.04850/) (2021)
- [Goal-based Course Recommendation](https://mlsanity.com/paper/1812.10078/) (2018)
- [Few-Shot Complex Knowledge Base Question Answering via Meta Reinforcement Learning](https://mlsanity.com/paper/2010.15877/) (2020)
- [VerdictDB: Universalizing Approximate Query Processing](https://mlsanity.com/paper/1804.00770/) (2018)
- [Drill-down: Interactive Retrieval of Complex Scenes using Natural Language Queries](https://mlsanity.com/paper/1911.03826/) (2019)
- [Visualization-Aware Sampling for Very Large Databases](https://mlsanity.com/paper/1510.03921/) (2015)
- [Controlling False Discoveries During Interactive Data Exploration](https://mlsanity.com/paper/1612.01040/) (2016)
- [Goal-driven Command Recommendations for Analysts](https://mlsanity.com/paper/2011.06237/) (2020)
- [Electra: Conditional Generative Model based Predicate-Aware Query Approximation](https://mlsanity.com/paper/2201.12420/) (2022)

## Built on by (most cited first, 1 of 1)

- [Scalable Sampling for High Utility Patterns](https://mlsanity.com/paper/2410.22964/) (2024)

## Abstract (arXiv)

Exploratory data analytics (EDA) is a sequential decision making process where analysts choose subsequent queries that might lead to some interesting insights based on the previous queries and corresponding results. Data processing systems often execute the queries on samples to produce results with low latency. Different downsampling strategy preserves different statistics of the data and have different magnitude of latency reductions. The optimum choice of sampling strategy often depends on the particular context of the analysis flow and the hidden intent of the analyst. In this paper, we are the first to consider the impact of sampling in interactive data exploration settings as they introduce approximation errors. We propose a Deep Reinforcement Learning (DRL) based framework which can optimize the sample selection in order to keep the analysis and insight generation flow intact. Evaluations with 3 real datasets show that our technique can preserve the original insight generation flow while improving the interaction latency, compared to baseline methods.