A web-based approach to data imputation

Li, Zhixu, Sharaf, Mohamed A., Sitbon, Laurianne, Sadiq, Shazia, Indulska, Marta and Zhou, Xiaofang (2013) A web-based approach to data imputation. World Wide web, 17 5: 873-897. doi:10.1007/s11280-013-0263-z

Author Li, Zhixu
Sharaf, Mohamed A.
Sitbon, Laurianne
Sadiq, Shazia
Indulska, Marta
Zhou, Xiaofang
Title A web-based approach to data imputation
Journal name World Wide web   Check publisher's open access policy
ISSN 1386-145X
Publication date 2013-01-01
Year available 2013
Sub-type Article (original research)
DOI 10.1007/s11280-013-0263-z
Volume 17
Issue 5
Start page 873
End page 897
Total pages 25
Place of publication New York, NY United States
Publisher Springer New York LLC
Language eng
Formatted abstract
In this paper, we present WebPut, a prototype system that adopts a novel web-based approach to the data imputation problem. Towards this, Webput utilizes the available information in an incomplete database in conjunction with the data consistency principle. Moreover, WebPut extends effective Information Extraction (IE) methods for the purpose of formulating web search queries that are capable of effectively retrieving missing values with high accuracy. WebPut employs a confidence-based scheme that efficiently leverages our suite of data imputation queries to automatically select the most effective imputation query for each missing value. A greedy iterative algorithm is proposed to schedule the imputation order of the different missing values in a database, and in turn the issuing of their corresponding imputation queries, for improving the accuracy and efficiency of WebPut. Moreover, several optimization techniques are also proposed to reduce the cost of estimating the confidence of imputation queries at both the tuple-level and the database-level. Experiments based on several real-world data collections demonstrate not only the effectiveness of WebPut compared to existing approaches, but also the efficiency of our proposed algorithms and optimization techniques.
Keyword Data imputation
Incomplete Data
Q-Index Code C1
Q-Index Status Confirmed Code
Institutional Status UQ

Document type: Journal Article
Sub-type: Article (original research)
Collections: Official 2014 Collection
UQ Business School Publications
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Citation counts: TR Web of Science Citation Count  Cited 3 times in Thomson Reuters Web of Science Article | Citations
Scopus Citation Count Cited 6 times in Scopus Article | Citations
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Created: Tue, 29 Oct 2013, 21:05:40 EST by Karen Morgan on behalf of UQ Business School