Frontiers of Information Technology & Electronic Engineering
>> 2024,
Volume 25,
Issue 3
doi:
10.1631/FITEE.2300480
A visual analysis approach for data imputation via multi-party tabular data correlation strategies
Affiliation(s): The State Key Lab of CAD & CG, Zhejiang University, Hangzhou 310058, China; Wuchan Zhongda Digital Technology Co., Ltd., Hangzhou 310020, China; Zhejiang Metals and Materials Co., Ltd., Hangzhou 310005, China; less
Received: 2023-07-17
Accepted: 2024-03-25
Available online: 2024-03-25
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Abstract
is an essential pre-processing task for , aimed at filling in incomplete data. However, conventional methods can only partly alleviate using isolated tabular data, and they fail to achieve the best balance between accuracy and efficiency. In this paper, we present a novel visual analysis approach for . We develop a multi-party tabular data association strategy that uses intelligent algorithms to identify similar columns and establish column correlations across multiple tables. Then, we perform the initial imputation of incomplete data using correlated data entries from other tables. Additionally, we develop a visual analysis system to refine candidates. Our interactive system combines the multi-party approach with expert knowledge, allowing for a better understanding of the relational structure of the data. This significantly enhances the accuracy and efficiency of , thereby enhancing the quality of and the intrinsic value of data assets. Experimental validation and user surveys demonstrate that this method supports users in verifying and judging the associated columns and similar rows using their domain knowledge.
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