short-paper

Automatically Generating Data Exploration Sessions Using Deep Reinforcement Learning

Authors:

Amit SomechAuthors Info & Claims

SIGMOD '20: Proceedings of the 2020 ACM SIGMOD International Conference on Management of Data

Pages 1527 - 1537

https://doi.org/10.1145/3318464.3389779

Published: 31 May 2020 Publication History

Abstract

Exploratory Data Analysis (EDA) is an essential yet highly demanding task. To get a head start before exploring a new dataset, data scientists often prefer to view existing EDA notebooks -- illustrative, curated exploratory sessions, on the same dataset, that were created by fellow data scientists who shared them online. Unfortunately, such notebooks are not always available (e.g., if the dataset is new or confidential). To address this, we present ATENA, a system that takes an input dataset and auto-generates a compelling exploratory session, presented in an EDA notebook. We shape EDA into a control problem, and devise a novel Deep Reinforcement Learning (DRL) architecture to effectively optimize the notebook generation. Though ATENA uses a limited set of EDA operations, our experiments show that it generates useful EDA notebooks, allowing users to gain actual insights.

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Cited By

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Index Terms

Automatically Generating Data Exploration Sessions Using Deep Reinforcement Learning
1. Mathematics of computing
  1. Probability and statistics
    1. Statistical paradigms
      1. Exploratory data analysis
2. Theory of computation
  1. Theory and algorithms for application domains
    1. Machine learning theory
      1. Reinforcement learning

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cover image ACM Conferences

SIGMOD '20: Proceedings of the 2020 ACM SIGMOD International Conference on Management of Data

June 2020

2925 pages

ISBN:9781450367356

DOI:10.1145/3318464

General Chairs:
David Maier
Portland State University, USA
,
Rachel Pottinger
University of British Columbia, Canada
,
Program Chairs:
AnHai Doan
University of Wisconsin, USA
,
Wang-Chiew Tan
Megagon Labs, USA
,
Publications Chairs:
Abdussalam Alawini
University of Illinois at Urbana-Champaign, USA
,
Hung Q. Ngo
RelationalAI, USA

Copyright © 2020 ACM.

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Published: 31 May 2020

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Israel Science Foundation
Israel Innovation Authority
Binational US-Israel Science foundation

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SIGMOD/PODS '20

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SIGMOD/PODS '20: International Conference on Management of Data

June 14 - 19, 2020

OR, Portland, USA

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Overall Acceptance Rate 785 of 4,003 submissions, 20%

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Shi YChen BChen YJin ZXu KJiao XGao TCao N(2024)Supporting Guided Exploratory Visual Analysis on Time Series Data with Reinforcement LearningIEEE Transactions on Visualization and Computer Graphics10.1109/TVCG.2023.332720030:1(1172-1182)Online publication date: 1-Jan-2024
https://dl.acm.org/doi/10.1109/TVCG.2023.3327200
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https://doi.org/10.1109/ICDE60146.2024.00372
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