Efficient Web-based Data Imputation with Graph Model

TL;DR

This paper presents a graph-based method for web-based data imputation that leverages data dependencies and minimal web access to efficiently fill missing values, outperforming existing approaches.

Contribution

It introduces a novel graph model for dependency representation and two algorithms for keyword generation and value extraction in web-based data imputation.

Findings

The approach efficiently imputes missing data with minimal web access.

Experimental results show superior performance over existing methods.

The method effectively utilizes data dependencies for high-quality imputation.

Abstract

A challenge for data imputation is the lack of knowledge. In this paper, we attempt to address this challenge by involving extra knowledge from web. To achieve high-performance web-based imputation, we use the dependency, i.e.FDs and CFDs, to impute as many as possible values automatically and fill in the other missing values with the minimal access of web, whose cost is relatively large. To make sufficient use of dependencies, We model the dependency set on the data as a graph and perform automatical imputation and keywords generation for web-based imputation based on such graph model. With the generated keywords, we design two algorithms to extract values for imputation from the search results. Extensive experimental results based on real-world data collections show that the proposed approach could impute missing values efficiently and effectively compared to existing approach.