TRIP: An interactive retrieving-inferring data imputation approach

Li, Z; Qin, L; Cheng, H; Zhang, X; Zhou, X

TRIP: An interactive retrieving-inferring data imputation approach

Li, Z Qin, L

Cheng, H Zhang, X Zhou, X

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Publication Type:: Conference Proceeding
Citation:: 2016 IEEE 32nd International Conference on Data Engineering, ICDE 2016, 2016, pp. 1462 - 1463
Issue Date:: 2016-06-22

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Field	Value	Language
dc.contributor.author	Li, Z	en_US
dc.contributor.author	Qin, L https://orcid.org/0000-0001-6068-5062	en_US
dc.contributor.author	Cheng, H	en_US
dc.contributor.author	Zhang, X	en_US
dc.contributor.author	Zhou, X	en_US
dc.date.issued	2016-06-22	en_US
dc.identifier.citation	2016 IEEE 32nd International Conference on Data Engineering, ICDE 2016, 2016, pp. 1462 - 1463	en_US
dc.identifier.isbn	9781509020195	en_US
dc.identifier.uri	http://hdl.handle.net/10453/131192
dc.description.abstract	© 2016 IEEE. 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].	en_US
dc.relation.ispartof	2016 IEEE 32nd International Conference on Data Engineering, ICDE 2016	en_US
dc.relation.isbasedon	10.1109/ICDE.2016.7498375	en_US
dc.title	TRIP: An interactive retrieving-inferring data imputation approach	en_US
dc.type	Conference Proceeding
utslib.for	080101 Adaptive Agents and Intelligent Robotics	en_US
utslib.for	080109 Pattern Recognition and Data Mining	en_US
pubs.embargo.period	Not known	en_US
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Strength - CAI - Centre for Artificial Intelligence
utslib.copyright.status	closed_access
pubs.publication-status	Published	en_US

Abstract:

© 2016 IEEE. 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].

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http://hdl.handle.net/10453/131192