Name
Abstract | ||
|---|---|---|
In many applications the available data give rise to an attributed graph, with the nodes corresponding to the entities of interest, edges to their relationships and attributes on both provide additional characteristics. To mine such data structures we have proposed a visual analytic algebra that enhances the atomic operators of selection, aggregation and a visualization step that allows the user to interact with the data. However, in many settings the user has a certain degree of uncertainty about the desired query; the problem is further compounded if the final results are the product of a series of such uncertain queries. To address this issue, we introduce a probabilistic framework that incorporates uncertainty in the queries and provides a probabilistic assessment of the likelihood of the obtained outcomes. We discuss its technical characteristics and illustrate it on a number of examples. |
Year | DOI | Venue |
|---|---|---|
2010 | 10.1145/1830252.1830273 | MLG@KDD |
Keywords | Field | DocType |
uncertain query,data structure,uncertain predicate,available data,probabilistic framework,technical characteristic,graph,querying graph,atomic operator,certain degree,final result,querying,probabilistic assessment,uncertainty,additional characteristic,visual analytics | Data mining,Data structure,Graph,Visualization,Computer science,Theoretical computer science,Artificial intelligence,Operator (computer programming),Probabilistic logic,Predicate (grammar),Machine learning,Probabilistic framework | Conference |
Citations | PageRank | References |
3 | 0.38 | 14 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Hao Zhou | 1 | 7 | 2.71 |
| Anna A. Shaverdian | 2 | 7 | 2.04 |
| H. V. Jagadish | 3 | 11141 | 2495.67 |
| George Michailidis | 4 | 303 | 35.19 |