TRIP: An Interactive Retrieving-Inferring
Data Imputation Approach
Abstract— : TRIP: An Interactive Retrieving-Inferring Data Imputation Approach Data imputation aims at ﬁlling in missing attribute values in databases. Most existing imputation methods to string attribute values are inferring-based approaches, which usually fail to reach a high imputation recall by just inferring missing values from the complete part of the data set. Recently, some retrieving-based methods are proposed to retrieve missing values from external resources such as the World Wide Web, which tend to reach a much higher imputation recall, but inevitably bring a large overhead by issuing a large number of search queries. In this paper, we investigate the interaction between the inferring-based methods and the retrieving-based methods. We show that retrieving a small number of selected missing values can greatly improve the imputation recall of the inferring-based methods. With this intuition, we propose an inTeractive Retrieving-Inferring data imPutation approach < Final Year Projects 2016 >, which performs retrieving and inferring alternately in ﬁlling in missing attribute values in a data set.
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