WebPut: A Web-Aided Data Imputation System for the General Type of Missing String Attribute Values

Shuangli Shan, Zhixu Li, Yang Li, Qiang Yang, Jia Zhu, Mohamed Sharaf, Xiaofang Zhou

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Scopus citations

Abstract

In this demonstration, we present an end-to-end web-aided data imputation prototype system named WebPut. WebPut consults the Web for imputing the missing values in a local database when the traditional inferring-based imputation method has difficulties in getting the right answers. Specifically, WebPut investigates the interaction between the local inferring-based imputation methods and the web-based retrieving methods and shows that retrieving a small number of selected missing values can greatly improve the imputation recall of the inferring-based methods. Besides, WebPut also incorporates a crowd intervention component that can get advice from humans in case that the web-based imputation methods may have difficulties in making the right decisions. We demonstrate, step by step, how WebPut fills an incomplete table with each of its components.
Original languageEnglish (US)
Title of host publication2019 IEEE 35th International Conference on Data Engineering (ICDE)
PublisherInstitute of Electrical and Electronics Engineers (IEEE)
Pages1952-1955
Number of pages4
ISBN (Print)9781538674741
DOIs
StatePublished - Apr 2019

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