Cost reduction for web-based data imputation

Zhixu Li, Shuo Shang, Qing Xie, Xiangliang Zhang

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

10 Scopus citations


Web-based Data Imputation enables the completion of incomplete data sets by retrieving absent field values from the Web. In particular, complete fields can be used as keywords in imputation queries for absent fields. However, due to the ambiguity of these keywords and the data complexity on the Web, different queries may retrieve different answers to the same absent field value. To decide the most probable right answer to each absent filed value, existing method issues quite a few available imputation queries for each absent value, and then vote on deciding the most probable right answer. As a result, we have to issue a large number of imputation queries for filling all absent values in an incomplete data set, which brings a large overhead. In this paper, we work on reducing the cost of Web-based Data Imputation in two aspects: First, we propose a query execution scheme which can secure the most probable right answer to an absent field value by issuing as few imputation queries as possible. Second, we recognize and prune queries that probably will fail to return any answers a priori. Our extensive experimental evaluation shows that our proposed techniques substantially reduce the cost of Web-based Imputation without hurting its high imputation accuracy. © 2014 Springer International Publishing Switzerland.
Original languageEnglish (US)
Title of host publicationDatabase Systems for Advanced Applications
PublisherSpringer Nature
Number of pages15
ISBN (Print)9783319058122
StatePublished - Apr 16 2014

Bibliographical note

KAUST Repository Item: Exported on 2020-10-01

ASJC Scopus subject areas

  • Theoretical Computer Science
  • General Computer Science


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