Interactive cleaning for progressive visualization through composite questions

Yuyu Luo, Chengliang Chai, Xuedi Qin, Nan Tang, Guoliang Li

科研成果: 书/报告/会议事项章节会议稿件同行评审

27 引用 (Scopus)

摘要

In this paper, we study the problem of interactive cleaning for progressive visualization (ICPV): Given a bad visualization V , it is to obtain a "cleaned" visualization V whose distance is far from V , under a given (small) budget w.r.t. human cost. In ICPV, a system interacts with a user iteratively. During each iteration, it asks the user a data cleaning question such as "how to clean detected errors x?", and takes value updates from the user to clean V. Conventional wisdom typically picks a single question (e.g., "Are SIGMOD conference and SIGMOD the same?") with the maximum expected benefit in each iteration. We propose to use a composite question - i.e., a group of single questions to be treated as one question - in each iteration (for example, Are SIGMOD conference in t1 and SIGMOD in t2 the same value, and are t1 and t2 duplicates?). A composite question is presented to the user as a small connected graph through a novel GUI that the user can directly operate on. We propose algorithms to select the best composite question in each iteration. Experiments on real-world datasets verify that composite questions are more effective than asking single questions in isolation w.r.t. the human cost.

源语言英语
主期刊名Proceedings - 2020 IEEE 36th International Conference on Data Engineering, ICDE 2020
出版商IEEE Computer Society
733-744
页数12
ISBN(电子版)9781728129037
DOI
出版状态已出版 - 4月 2020
已对外发布
活动36th IEEE International Conference on Data Engineering, ICDE 2020 - Dallas, 美国
期限: 20 4月 202024 4月 2020

出版系列

姓名Proceedings - International Conference on Data Engineering
2020-April
ISSN(印刷版)1084-4627

会议

会议36th IEEE International Conference on Data Engineering, ICDE 2020
国家/地区美国
Dallas
时期20/04/2024/04/20

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引用此

Luo, Y., Chai, C., Qin, X., Tang, N., & Li, G. (2020). Interactive cleaning for progressive visualization through composite questions. 在 Proceedings - 2020 IEEE 36th International Conference on Data Engineering, ICDE 2020 (页码 733-744). 文章 9101184 (Proceedings - International Conference on Data Engineering; 卷 2020-April). IEEE Computer Society. https://doi.org/10.1109/ICDE48307.2020.00069