VoidWiz: Resolving incompleteness using network effects

If Lisa visits Dr. Brown, and there is no record of the drug he prescribed her, can we find it? Data sources, much to analysts' dismay, are too often plagued with incompleteness, making business analytics over the data difficult. Data entries with incomplete values are ignored, making some anal...

Full description

Saved in:
Bibliographic Details
Published in2014 IEEE 30th International Conference on Data Engineering pp. 1230 - 1233
Main Authors Christodoulakis, Christina, Faloutsos, Christos, Miller, Renee J.
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.03.2014
Subjects
Algorithm design and analysis
Belief propagation
Clinical trials
Data visualization
Drugs
Educational institutions
Prediction algorithms
Online AccessGet full text

Cover

More Information
Summary:If Lisa visits Dr. Brown, and there is no record of the drug he prescribed her, can we find it? Data sources, much to analysts' dismay, are too often plagued with incompleteness, making business analytics over the data difficult. Data entries with incomplete values are ignored, making some analytic queries fail to accurately describe how an organization is performing. We introduce a principled way of performing value imputation on missing values, allowing a user to choose a correct value after viewing possible values and why they were inferred. We achieve this by turning our data into a graph network and performing link prediction on nodes of interest using the belief propagation algorithm.
ISSN:1063-6382
2375-026X
DOI:10.1109/ICDE.2014.6816748