Exploring Ratings in Subjective Databases

Amer-Yahia, Sihem; Milo, Tova; Youngmann, Brit

doi:10.1145/3448016.3457259

Cited by 5 publications

(2 citation statements)

References 44 publications

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“…Multi-step data exploration: Data exploration is a multi-step process whose goal is to extract insights from data [33,34,56]. Guiding users in performing data exploration is a well-studied task [7,9,22]. Numerous works proposed next-step recommendations [8,16,18,26,34].…”

Section: Related Workmentioning

confidence: 99%

Guided Exploration of Data Summaries

Youngmann¹,

Amer-Yahia²,

Personnaz³

2022

Preprint

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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 Eda4Sum, the problem of guided exploration of data summaries that seeks to sequentially produce connected summaries with the goal of maximizing their cumulative utility. Eda4Sum generalizes one-shot summarization. We propose to solve it with one of two approaches: (i) Top1Sum 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.

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Section: Related Workmentioning

confidence: 99%

Guided Exploration of Data Summaries

Youngmann¹,

Amer-Yahia²,

Personnaz³

2022

Preprint

Self Cite

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“…In cases where the data size is massive, finding the most uniform and diverse parts is more useful. EDA is known as a difficult task, requiring profound analytical skills, experience [1]. Recent work suggested fully automating this process using Reinforcement Learning [2,5].…”

Section: Introductionmentioning

confidence: 99%

Eda4sum

Personnaz

Youngmann²,

Amer-Yahia

2022

Proc. VLDB Endow.

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We demonstrate EDA4Sum, a framework dedicated to generating guided multi-step data summarization pipelines for very large datasets. 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. EDA4Sum leverages Exploratory Data Analysis (EDA) to produce connected summaries in multiple steps, with the goal of maximizing their cumulative utility. A useful summary contains k individually uniform sets that are collectively diverse to be representative of the input data. EDA4Sum accommodates datasets with different characteristics by providing the ability to tune the weights of uniformity, diversity and novelty when generating multi-step summaries. We demonstrate the superiority of multi-step EDA summarization over single-step summarization for summarizing very large data, and the need to provide guidance to domain experts, by interacting with the VLDB'22 participants who will act as data analysts. The application is avilable at https://bit.ly/eda4sum_application.

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