TRIP: An interactive retrieving-inferring data imputation approach

Handle URI:
http://hdl.handle.net/10754/621293
Title:
TRIP: An interactive retrieving-inferring data imputation approach
Authors:
Li, Zhixu; Qin, Lu; Cheng, Hong; Zhang, Xiangliang ( 0000-0002-3574-5665 ) ; Zhou, Xiaofang
Abstract:
Data imputation aims at filling in missing attribute values in databases. Existing imputation approaches to nonquantitive string data can be roughly put into two categories: (1) inferring-based approaches [2], and (2) retrieving-based approaches [1]. Specifically, the inferring-based approaches find substitutes or estimations for the missing ones from the complete part of the data set. However, they typically fall short in filling in unique missing attribute values which do not exist in the complete part of the data set [1]. The retrieving-based approaches resort to external resources for help by formulating proper web search queries to retrieve web pages containing the missing values from the Web, and then extracting the missing values from the retrieved web pages [1]. This webbased retrieving approach reaches a high imputation precision and recall, but on the other hand, issues a large number of web search queries, which brings a large overhead [1]. © 2016 IEEE.
KAUST Department:
Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division; Computer Science Program
Citation:
Li Z, Qin L, Cheng H, Zhang X, Zhou X (2016) TRIP: An interactive retrieving-inferring data imputation approach. 2016 IEEE 32nd International Conference on Data Engineering (ICDE). Available: http://dx.doi.org/10.1109/ICDE.2016.7498375.
Publisher:
Institute of Electrical and Electronics Engineers (IEEE)
Journal:
2016 IEEE 32nd International Conference on Data Engineering (ICDE)
Conference/Event name:
32nd IEEE International Conference on Data Engineering, ICDE 2016
Issue Date:
25-Jun-2016
DOI:
10.1109/ICDE.2016.7498375
Type:
Conference Paper
Appears in Collections:
Conference Papers; Computer Science Program; Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division

Full metadata record

DC FieldValue Language
dc.contributor.authorLi, Zhixuen
dc.contributor.authorQin, Luen
dc.contributor.authorCheng, Hongen
dc.contributor.authorZhang, Xiangliangen
dc.contributor.authorZhou, Xiaofangen
dc.date.accessioned2016-11-03T06:57:02Z-
dc.date.available2016-11-03T06:57:02Z-
dc.date.issued2016-06-25en
dc.identifier.citationLi Z, Qin L, Cheng H, Zhang X, Zhou X (2016) TRIP: An interactive retrieving-inferring data imputation approach. 2016 IEEE 32nd International Conference on Data Engineering (ICDE). Available: http://dx.doi.org/10.1109/ICDE.2016.7498375.en
dc.identifier.doi10.1109/ICDE.2016.7498375en
dc.identifier.urihttp://hdl.handle.net/10754/621293-
dc.description.abstractData imputation aims at filling in missing attribute values in databases. Existing imputation approaches to nonquantitive string data can be roughly put into two categories: (1) inferring-based approaches [2], and (2) retrieving-based approaches [1]. Specifically, the inferring-based approaches find substitutes or estimations for the missing ones from the complete part of the data set. However, they typically fall short in filling in unique missing attribute values which do not exist in the complete part of the data set [1]. The retrieving-based approaches resort to external resources for help by formulating proper web search queries to retrieve web pages containing the missing values from the Web, and then extracting the missing values from the retrieved web pages [1]. This webbased retrieving approach reaches a high imputation precision and recall, but on the other hand, issues a large number of web search queries, which brings a large overhead [1]. © 2016 IEEE.en
dc.publisherInstitute of Electrical and Electronics Engineers (IEEE)en
dc.titleTRIP: An interactive retrieving-inferring data imputation approachen
dc.typeConference Paperen
dc.contributor.departmentComputer, Electrical and Mathematical Sciences and Engineering (CEMSE) Divisionen
dc.contributor.departmentComputer Science Programen
dc.identifier.journal2016 IEEE 32nd International Conference on Data Engineering (ICDE)en
dc.conference.date16 May 2016 through 20 May 2016en
dc.conference.name32nd IEEE International Conference on Data Engineering, ICDE 2016en
dc.contributor.institutionSchool of Computer Science and Technology, Soochow University, Suzhou, Chinaen
dc.contributor.institutionUniversity of Technology, Sydney, Australiaen
dc.contributor.institutionChinese University of Hong Kong, Chinaen
dc.contributor.institutionUniversity of Queensland, Brisbane, QLD, Australiaen
kaust.authorZhang, Xiangliangen
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