Interactive deduplication using active learning
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
Factorizing complex predicates in queries to exploit indexes
Proceedings of the 2003 ACM SIGMOD international conference on Management of data
Extracting predicates from mining models for efficient query evaluation
ACM Transactions on Database Systems (TODS)
Answering constraint-based mining queries on itemsets using previous materialized results
Journal of Intelligent Information Systems
Embedded predictive modeling in a parallel relational database
Proceedings of the 2006 ACM symposium on Applied computing
MauveDB: supporting model-based user views in database systems
Proceedings of the 2006 ACM SIGMOD international conference on Management of data
Expressive power of an algebra for data mining
ACM Transactions on Database Systems (TODS)
Data Management in the Worldwide Sensor Web
IEEE Pervasive Computing
COMBI-operator - database support for data mining applications
VLDB '03 Proceedings of the 29th international conference on Very large data bases - Volume 29
INCREMENTAL EXTRACTION OF ASSOCIATION RULES IN APPLICATIVE DOMAINS
Applied Artificial Intelligence
Using a reinforced concept lattice to incrementally mine association rules from closed itemsets
KDID'06 Proceedings of the 5th international conference on Knowledge discovery in inductive databases
Incrementally maintaining classification using an RDBMS
Proceedings of the VLDB Endowment
Optimization of association rules extraction through exploitation of context dependent constraints
AI*IA'05 Proceedings of the 9th conference on Advances in Artificial Intelligence
A novel incremental approach to association rules mining in inductive databases
Proceedings of the 2004 European conference on Constraint-Based Mining and Inductive Databases
Mining attribute association in query predicates for access path generation
Proceedings of the 2012 ACM Research in Applied Computation Symposium
Index selection: a query pattern mining based approach
Proceedings of the 2013 Research in Adaptive and Convergent Systems
Hi-index | 0.00 |
Modern relational database systems are beginning to support ad hoc queries on mining models. In this paper, we explore novel techniques for optimizing queries that apply mining models to relational data. For such queries, we use the internal structure of the mining model to automatically derive traditional database predicates. We present algorithms for deriving such predicates for some popular discrete mining models:decision trees, naive Bayes, and clustering.Our experiments on Microsoft SQL Server 2000 demonstrate that these derived predicates can signi?cantly reduce the cost of evaluating such queries.