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

doi:10.1109/ICDE.2019.00212

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

King Abdullah University of Science and Technology

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

4 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 language	English (US)
Title of host publication	2019 IEEE 35th International Conference on Data Engineering (ICDE)
Publisher	Institute of Electrical and Electronics Engineers (IEEE)
Pages	1952-1955
Number of pages	4
ISBN (Print)	9781538674741
DOIs	https://doi.org/10.1109/ICDE.2019.00212
State	Published - Apr 2019

Bibliographical note

KAUST Repository Item: Exported on 2020-10-01
Acknowledgements: This research is partially supported by National Natural Science Foundation of China (Grant No. 61632016), the Natural Science Research Project of Jiangsu Higher Education Institution (No. 17KJA520003), and the Open Program of Neusoft Corporation (No. SKLSAOP1801).

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10.1109/ICDE.2019.00212

https://repository.kaust.edu.sa/bitstream/10754/655950/1/WebPut-%20A%20Web-Aided%20Data%20Imputation%20System%20for%20the%20General%20Type%20of%20Missing%20String%20Attribute%20Values.pdf

Cite this

@inproceedings{e384421bd197440fb2f5e173a534e390,

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

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.",

author = "Shuangli Shan and Zhixu Li and Yang Li and Qiang Yang and Jia Zhu and Mohamed Sharaf and Xiaofang Zhou",

note = "KAUST Repository Item: Exported on 2020-10-01 Acknowledgements: This research is partially supported by National Natural Science Foundation of China (Grant No. 61632016), the Natural Science Research Project of Jiangsu Higher Education Institution (No. 17KJA520003), and the Open Program of Neusoft Corporation (No. SKLSAOP1801).",

year = "2019",

month = apr,

doi = "10.1109/ICDE.2019.00212",

language = "English (US)",

isbn = "9781538674741",

pages = "1952--1955",

booktitle = "2019 IEEE 35th International Conference on Data Engineering (ICDE)",

publisher = "Institute of Electrical and Electronics Engineers (IEEE)",

}

WebPut: A Web-Aided Data Imputation System for the General Type of Missing String Attribute Values. / Shan, Shuangli; Li, Zhixu; Li, Yang et al.
2019 IEEE 35th International Conference on Data Engineering (ICDE). Institute of Electrical and Electronics Engineers (IEEE), 2019. p. 1952-1955.

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

TY - GEN

T1 - WebPut: A Web-Aided Data Imputation System for the General Type of Missing String Attribute Values

AU - Shan, Shuangli

AU - Li, Zhixu

AU - Li, Yang

AU - Yang, Qiang

AU - Zhu, Jia

AU - Sharaf, Mohamed

AU - Zhou, Xiaofang

N1 - KAUST Repository Item: Exported on 2020-10-01 Acknowledgements: This research is partially supported by National Natural Science Foundation of China (Grant No. 61632016), the Natural Science Research Project of Jiangsu Higher Education Institution (No. 17KJA520003), and the Open Program of Neusoft Corporation (No. SKLSAOP1801).

PY - 2019/4

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N2 - 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.

AB - 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.

UR - http://hdl.handle.net/10754/655950

UR - https://ieeexplore.ieee.org/document/8731367/

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DO - 10.1109/ICDE.2019.00212

M3 - Conference contribution

SN - 9781538674741

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BT - 2019 IEEE 35th International Conference on Data Engineering (ICDE)

PB - Institute of Electrical and Electronics Engineers (IEEE)

ER -

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

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