Learning probabilistic models of link structure
The Journal of Machine Learning Research
Substructure similarity search in graph databases
Proceedings of the 2005 ACM SIGMOD international conference on Management of data
ACM SIGKDD Explorations Newsletter
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IEEE Transactions on Knowledge and Data Engineering
Mining search engine query logs for query recommendation
Proceedings of the 15th international conference on World Wide Web
Creating probabilistic databases from information extraction models
VLDB '06 Proceedings of the 32nd international conference on Very large data bases
On Query Completion in Web Search Engines Based on Query Stream Mining
WI '07 Proceedings of the IEEE/WIC/ACM International Conference on Web Intelligence
Flexible query answering on graph-modeled data
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ICDE '08 Proceedings of the 2008 IEEE 24th International Conference on Data Engineering
Algebraic visual analysis: the Catalano phone call data set case study
Proceedings of the ACM SIGKDD Workshop on Visual Analytics and Knowledge Discovery: Integrating Automated Analysis with Interactive Exploration
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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.