Handling very large numbers of association rules in the analysis of microarray data
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
On computing, storing and querying frequent patterns
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
Optimizing subset queries: a step towards SQL-based inductive databases for itemsets
Proceedings of the 2004 ACM symposium on Applied computing
Pruning and Visualizing Generalized Association Rules in Parallel Coordinates
IEEE Transactions on Knowledge and Data Engineering
Opportunity map: a visualization framework for fast identification of actionable knowledge
Proceedings of the 14th ACM international conference on Information and knowledge management
A Visual Data Mining Framework for Convenient Identification of Useful Knowledge
ICDM '05 Proceedings of the Fifth IEEE International Conference on Data Mining
Rule interestingness analysis using OLAP operations
Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining
Opportunity map: identifying causes of failure - a deployed data mining system
Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining
A model for managing collections of patterns
Proceedings of the 2007 ACM symposium on Applied computing
Managing large collections of data mining models
Communications of the ACM - Alternate reality gaming
Cost-based query optimization for complex pattern mining on multiple databases
EDBT '08 Proceedings of the 11th international conference on Extending database technology: Advances in database technology
Minimum-Size Bases of Association Rules
ECML PKDD '08 Proceedings of the 2008 European Conference on Machine Learning and Knowledge Discovery in Databases - Part I
A comparative study of signature based indexes for efficient retrieval of temporal patterns
Proceedings of the International Conference & Workshop on Emerging Trends in Technology
A novel incremental approach to association rules mining in inductive databases
Proceedings of the 2004 European conference on Constraint-Based Mining and Inductive Databases
A unified framework for heterogeneous patterns
Information Systems
A performance study of three disk-based structures for indexing and querying frequent itemsets
Proceedings of the VLDB Endowment
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Rule mining is an important data mining task that has been applied to numerous real-world applications. Often a rule mining system generates a large number of rules and only a small subset of them is really useful in applications. Although there exist some systems allowing the user to query the discovered rules, they are less suitable for complex ad hoc querying of multiple data mining rulebases to retrieve interesting rules. In this paper, we propose a new powerful rule query language Rule-QL for querying multiple rulebases that is modeled after SQL and has rigorous theoretical foundations of a rule-based calculus. In particular, we first propose a rule-based calculus RC based on the first-order logic, and then present the language Rule-QL that is at least as expressive as the safe fragment of RC. We also propose a number of efficient query evaluation techniques for Rule-QL and test them experimentally on some representative queries to demonstrate the feasibility of Rule-QL.