Back to previous page
Conference Publication

Exploration of Data Summaries

CNRS, France
Sihem Amer-Yahia
CNRS, France
Aurélien Personnaz
Brit Youngmann
accepted in:
PVLDB 2022
Yet to be published
April 20, 2022
Read full article

Data summarization is the process of producing interpretable and representative subsets of an input dataset. It is usually performed following a one-shot process with the purpose of finding the best summary. A useful summary contains k individually uniform sets that are collectively diverse to be representative. Uniformity addresses interpretability and diversity addresses representativity. Finding such as summary is a difficult task when data is highly diverse and large. We examine the applicability of Exploratory Data Analysis (EDA) to data summarization and formalize the problem of guided exploration of data summaries that seeks to sequentially produce connected summaries with the goal of maximizing their cumulative utility. Our problem generalizes one-shot summarization. We propose to solve it with one of two approaches: (i) TOPSUM that chooses the most useful summary at each step; (ii) RLSUM that trains a policy with Deep Reinforcement Learning that rewards an agent for finding a diverse and new collection of uniform sets at each step. We compare these approaches with one-shot summarization and top-performing EDA solutions. We run extensive experiments on three large datasets. Our results demonstrate the superiority of our approaches for summarizing very large data, and the need to provide guidance to domain experts.

Will be available soon to download

Get in touch

Thank you! Your submission has been received!
Oops! Something went wrong while submitting the form, try again please.