Automatic Visual Recommendation for Data Science and Analytics

被引：0

作者：

Muniswamaiah, Manoj ^{[1
]}

Agerwala, Tilak ^{[1
]}

Tappert, Charles C. ^{[1
]}

机构：

[1] Pace Univ, Seidenberg Sch CSIS, White Plains, NY 10038 USA

来源：

ADVANCES IN INFORMATION AND COMMUNICATION, VOL 2 | 2020年 / 1130卷

关键词：

Big data; Database; Analytical query; Query optimizer; Data science; Data visualization; Data analyst; BIG-DATA;

D O I：

10.1007/978-3-030-39442-4_11

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

Data visualization is used to extract insight from large datasets. Data scientists repeatedly keep generating different visualizations from the datasets for their hypothesis. Analyzing datasets which has many attributes could be a cumbersome process and lead to errors. The goal of this research paper is to automatically recommend interesting visualization patterns using optimized datasets from different databases. It reduces the time spent on low utility visualizations and displays recommended patterns.

引用

页码：125 / 132

页数：8

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