Name
Abstract | ||
|---|---|---|
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. |
Year | DOI | Venue |
|---|---|---|
2014 | 10.1109/ICDE.2014.6816748 | ICDE |
Keywords | Field | DocType |
link prediction,belief propagation algorithm,voidwiz system,analytic queries,data entries,learning (artificial intelligence),business analytics,data analysis,data sources,network effects,data visualisation,belief maintenance,value imputation,graph network,data visualization,prediction algorithms,clinical trials,algorithm design and analysis,belief propagation | Data mining,Computer science,Prediction algorithms,Artificial intelligence,Missing data,Belief propagation,Graph,Data visualization,Algorithm design,Business analytics,Imputation (statistics),Machine learning,Database | Conference |
ISSN | Citations | PageRank |
1084-4627 | 1 | 0.37 |
References | Authors | |
7 | 3 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Christina Christodoulakis | 1 | 19 | 2.54 |
| Christos Faloutsos | 2 | 27972 | 4490.38 |
| Renée J. Miller | 3 | 3545 | 373.59 |