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short-paper

DORA THE EXPLORER: Exploring Very Large Data With Interactive Deep Reinforcement Learning

Authors:

Aurélien Personnaz,

Sihem Amer-Yahia,

Laure Berti-Equille,

Maximilian Fabricius,

Srividya SubramanianAuthors Info & Claims

CIKM '21: Proceedings of the 30th ACM International Conference on Information & Knowledge Management

Pages 4769 - 4773

https://doi.org/10.1145/3459637.3481967

Published: 30 October 2021 Publication History

Abstract

We demonstrate DORA THE EXPLORER, a system that guides users in finding items of interest in a very large data set. DORA THE EXPLORER provides users with the full spectrum of exploration modes and is driven by Data Familiarity or Curiosity, as well as User Interventions. DORA THE EXPLORER is able to handle data and search scenario complexity, i.e., the difficulty to find scattered/clustered individual records in the data set, and user ability to express what s/he needs. DORA THE EXPLORER relies on Deep Reinforcement Learning that combines intrinsic (curiosity) and extrinsic (familiarity) rewards. DORA's main goal is to support scientific discovery from data. We describe the system architecture and illustrate it with three demonstration scenarios on a 2.6 mil-lion galaxies SDSS, a large sky survey data set1. A video of DORA THE EXPLORER is available at https://bit.ly/dora-demo, the codehttps://github.com/apersonnaz/rl-guided-galaxy-exploration, and the application at https://bit.ly/dora-application

Supplementary Material

MP4 File (Dora_conf_demo_with_sub.mp4)

Dora demonstration - 20 minutes - with subtitles

Download
322.60 MB

MP4 File (Dora_conf_demo_with_sub.mp4)

Dora demonstration - 20 minutes - with subtitles

Download
322.60 MB

References

[1]

S. Amer-Yahia, S. Kleisarchaki, N. K. Kolloju, L. V. Lakshmanan, and R. H. Zamar. 2017. Exploring Rated Datasets with Rating Maps. In WWW.

Digital Library

[2]

O. Bar El, T. Milo, and A. Somech. 2020. Automatically generating data exploration sessions using deep reinforcement learning. In SIGMOD. 1527--1537.

Digital Library

[3]

L. Biewald. 2020. Experiment Tracking with Weights and Biases. https://www.wandb.com/Software available from wandb.com.

[4]

M. Das, S. Thirumuruganathan, S. Amer-Yahia, G. Das, and C. Yu. 2012. Who Tags What? An Analysis Framework. pVLDB Endow., Vol. 5, 11 (2012), 1567--1578.

Digital Library

[5]

K. Dimitriadou, O. Papaemmanouil, and Y. Diao. 2016. AIDE: an active learning-based approach for interactive data exploration. IEEE TKDE, Vol. 28, 11 (2016), 2842--2856.

Digital Library

[6]

M. Eirinaki, S. Abraham, N. Polyzotis, and N. Shaikh. 2013. Querie: Collaborative database exploration. IEEE TKDE, Vol. 26, 7 (2013), 1778--1790.

[7]

X. Ge, Y. Xue, Z. Luo, M. A. Sharaf, and P. K. Chrysanthis. 2016. REQUEST: A scalable framework for interactive construction of exploratory queries. In IEEE Intl. Conf. on Big Data. 646--655.

[8]

M. Kahng, S. B. Navathe, J. T. Stasko, and D. H. P. Chau. 2016. Interactive Browsing and Navigation in Relational Databases. pVLDB Endow., Vol. 9, 12 (2016), 1017--1028. http://www.vldb.org/pvldb/vol9/p1017-kahng.pdf

Digital Library

[9]

N. Kamat, P. Jayachandran, K. Tunga, and A. Nandi. 2014. Distributed and interactive cube exploration. In ICDE. 472--483.

[10]

P. Marcel, N. Labroche, and P. Vassiliadis. 2019. Towards a benefit-based optimizer for Interactive Data Analysis. In EDBT/ICDT.

[11]

V. Mnih, A. P. Badia, M. Mirza, A. Graves, T. P. Lillicrap, T. Harley, D. Silver, and K. Kavukcuoglu. 2016. Asynchronous Methods for Deep Reinforcement Learning. In Proceedings of the 33nd International Conference on Machine Learning, ICML 2016, New York City, NY, USA, June 19-24, 2016. 1928--1937.

Digital Library

[12]

D. Mottin, M. Lissandrini, Y. Velegrakis, and T. Palpanas. 2017. New Trends on Exploratory Methods for Data Analytics. pVLDB Endow., Vol. 10, 12 (2017), 1977--1980.

Digital Library

[13]

B. Omidvar-Tehrani, S. Amer-Yahia, and A. Termier. 2015. Interactive user group analysis. In CIKM. 403--412.

Digital Library

[14]

A. Personnaz, S. Amer-Yahia, L. Berti-Equille, M. Fabricius, and S. Subramanian. 2021. Balancing Familiarity and Curiosity in Data Exploration with Deep Reinforcement Learning. In the 4th International Workshop on Exploiting Artificial Intelligence Techniques for Data Management (aiDM) in conjunction with ACM SIGMOD 2021.

Digital Library

[15]

D. A. Randell, Z. Cui, and A. G. Cohn. 1992. A Spatial Logic Based on Regions and Connection. In Proceedings of the Third International Conference on Principles of Knowledge Representation and Reasoning (KR'92). 165--176.

Digital Library

[16]

M. Seleznova, B. Omidvar-Tehrani, S. Amer-Yahia, and E. Simon. 2020. Guided Exploration of User Groups. pVLDB Endow., Vol. 13, 9 (2020), 1469--1482.

Digital Library

[17]

T. Uno, M. Kiyomi, and H. Arimura. 2004. LCM ver. 2: Efficient mining algorithms for frequent/closed/maximal itemsets. In IEEE ICDM Workshop on Frequent Itemset Mining Implementations (FIMI), Vol. 126.

[18]

N. Yan, C. Li, S. B. Roy, R. Ramegowda, and G. Das. 2010. Facetedpedia: enabling query-dependent faceted search for Wikipedia. In CIKM. 1927--1928.

Digital Library

[19]

L. Zhang and Y. Zhang. 2010. Interactive retrieval based on faceted feedback. In SIGIR. 363--370.

Digital Library

Cited By

Chanson ALabroche NMarcel PPerlata VVassiliadis P(2024)Interestingness Measures for Exploratory Data Analysis: a SurveyNew Trends in Database and Information Systems10.1007/978-3-031-70421-5_2(14-24)Online publication date: 14-Nov-2024
https://doi.org/10.1007/978-3-031-70421-5_2
de Almeida VRibeiro EBouarour NComba JAmer-Yahia S(2023)SHEVA: A Visual Analytics System for Statistical Hypothesis ExplorationProceedings of the VLDB Endowment10.14778/3611540.361163116:12(4102-4105)Online publication date: 12-Sep-2023
https://doi.org/10.14778/3611540.3611631
Cao YXie XHuang K(2023)Learn to Explore: on Bootstrapping Interactive Data Exploration with Meta-learning2023 IEEE 39th International Conference on Data Engineering (ICDE)10.1109/ICDE55515.2023.00135(1720-1733)Online publication date: Apr-2023
https://doi.org/10.1109/ICDE55515.2023.00135
Show More Cited By

Index Terms

DORA THE EXPLORER: Exploring Very Large Data With Interactive Deep Reinforcement Learning
1. Information systems
  1. Information systems applications
    1. Decision support systems
      1. Data analytics

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Published In

cover image ACM Conferences

CIKM '21: Proceedings of the 30th ACM International Conference on Information & Knowledge Management

October 2021

4966 pages

ISBN:9781450384469

DOI:10.1145/3459637

General Chairs:
Gianluca Demartini
The University of Queensland, Australia
,
Guido Zuccon
The University of Queensland, Australia
,
Program Chairs:
J. Shane Culpepper
RMIT University, Australia
,
Zi Huang
The University of Queensland, Australia
,
Hanghang Tong
University of Illinois at Urbana-Champaign, USA

Copyright © 2021 ACM.

© 2021 Association for Computing Machinery. ACM acknowledges that this contribution was authored or co-authored by an employee, contractor or affiliate of a national government. As such, the Government retains a nonexclusive, royalty-free right to publish or reproduce this article, or to allow others to do so, for Government purposes only.

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 30 October 2021

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European Union?s Horizon 2020

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CIKM '21

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CIKM '21: The 30th ACM International Conference on Information and Knowledge Management

November 1 - 5, 2021

Queensland, Virtual Event, Australia

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Overall Acceptance Rate 1,861 of 8,427 submissions, 22%

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CIKM '25

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The 34th ACM International Conference on Information and Knowledge Management

November 10 - 14, 2025

Seoul , Republic of Korea

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Cited By

Chanson ALabroche NMarcel PPerlata VVassiliadis P(2024)Interestingness Measures for Exploratory Data Analysis: a SurveyNew Trends in Database and Information Systems10.1007/978-3-031-70421-5_2(14-24)Online publication date: 14-Nov-2024
https://doi.org/10.1007/978-3-031-70421-5_2
de Almeida VRibeiro EBouarour NComba JAmer-Yahia S(2023)SHEVA: A Visual Analytics System for Statistical Hypothesis ExplorationProceedings of the VLDB Endowment10.14778/3611540.361163116:12(4102-4105)Online publication date: 12-Sep-2023
https://doi.org/10.14778/3611540.3611631
Cao YXie XHuang K(2023)Learn to Explore: on Bootstrapping Interactive Data Exploration with Meta-learning2023 IEEE 39th International Conference on Data Engineering (ICDE)10.1109/ICDE55515.2023.00135(1720-1733)Online publication date: Apr-2023
https://doi.org/10.1109/ICDE55515.2023.00135
Personnaz AYoungmann BAmer-Yahia S(2022)EDA4SUMProceedings of the VLDB Endowment10.14778/3554821.355485115:12(3590-3593)Online publication date: 1-Aug-2022
https://dl.acm.org/doi/10.14778/3554821.3554851
Omidvar-Tehrani BPersonnaz AAmer-Yahia SAl Hasan MXiong L(2022)Guided Text-based Item ExplorationProceedings of the 31st ACM International Conference on Information & Knowledge Management10.1145/3511808.3557141(3410-3420)Online publication date: 17-Oct-2022
https://dl.acm.org/doi/10.1145/3511808.3557141
Schafer NMichel S(2022)BETZE: Benchmarking Data Exploration Tools with (Almost) Zero Effort2022 IEEE 38th International Conference on Data Engineering (ICDE)10.1109/ICDE53745.2022.00224(2385-2398)Online publication date: May-2022
https://doi.org/10.1109/ICDE53745.2022.00224

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