TY - GEN
T1 - WebPut
T2 - 35th IEEE International Conference on Data Engineering, ICDE 2019
AU - Shan, Shuangli
AU - Li, Zhixu
AU - Li, Yang
AU - Yang, Qiang
AU - Zhu, Jia
AU - Sharaf, Mohamed
AU - Zhou, Xiaofang
N1 - Funding Information:
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).
Publisher Copyright:
© 2019 IEEE.
PY - 2019/4
Y1 - 2019/4
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.
KW - Data imputation
KW - Incomplete data
KW - Webput
UR - http://www.scopus.com/inward/record.url?scp=85067940433&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85067940433&partnerID=8YFLogxK
U2 - 10.1109/ICDE.2019.00212
DO - 10.1109/ICDE.2019.00212
M3 - Conference contribution
AN - SCOPUS:85067940433
T3 - Proceedings - International Conference on Data Engineering
SP - 1952
EP - 1955
BT - Proceedings - 2019 IEEE 35th International Conference on Data Engineering, ICDE 2019
PB - IEEE Computer Society
Y2 - 8 April 2019 through 11 April 2019
ER -