%0 Book Section
%9 OS CH : Chapitres d'ouvrages scientifiques
%A Personnaz, A.
%A Amer-Yahia, S.
%A Berti-Equille, Laure
%A Fabricius, M.
%A Subramanian, S.
%T Dora the explorer : exploring very large data with interactive deep reinforcement learning authors' copy [demonstration paper]
%B CIKM'21 : proceedings of the 30th ACM International Conference on Information and Knowledge Management
%C New York
%D 2021
%L fdi:010083557
%G ENG
%I ACM
%@ 978-1-4503-8446-9
%P 4769-4773
%R 10.1145/3459637.3481967
%U https://www.documentation.ird.fr/hor/fdi:010083557
%> https://horizon.documentation.ird.fr/exl-doc/pleins_textes/2021-11/010083557.pdf
%W Horizon (IRD)
%X 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 million galaxies SDSS, a large sky survey data set1. A video of dora the explorer is available at https://bit.ly/dora-demo, the code https://github.com/apersonnaz/rl-guided-galaxy-exploration, and the application at https://bit.ly/dora-application.
%B CIKM'21.International Conference on Information and Knowledge Management
%8 2021/11/01-05
%$ 122 ; 020