WebPut: A Web-Aided Data Imputation System for the General Type of Missing String Attribute Values
KAUST DepartmentKing Abdullah University of Science and Technology, Saudi Arabia
Permanent link to this recordhttp://hdl.handle.net/10754/655950
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AbstractIn 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.
CitationShan, S., Li, Z., Li, Y., Yang, Q., Zhu, J., Sharaf, M., & Zhou, X. (2019). WebPut: A Web-Aided Data Imputation System for the General Type of Missing String Attribute Values. 2019 IEEE 35th International Conference on Data Engineering (ICDE). doi:10.1109/icde.2019.00212
SponsorsThis 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).
Conference/Event name2019 IEEE 35th International Conference on Data Engineering (ICDE)