ACM Transactions on Database Systems (TODS)
A first course in database systems
A first course in database systems
Exploratory mining and pruning optimizations of constrained associations rules
SIGMOD '98 Proceedings of the 1998 ACM SIGMOD international conference on Management of data
Using a knowledge cache for interactive discovery of association rules
KDD '99 Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining
Efficient and extensible algorithms for multi query optimization
SIGMOD '00 Proceedings of the 2000 ACM SIGMOD international conference on Management of data
ACM SIGKDD Explorations Newsletter
ICDE '95 Proceedings of the Eleventh International Conference on Data Engineering
Fast Algorithms for Mining Association Rules in Large Databases
VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
Answering queries using views: A survey
The VLDB Journal — The International Journal on Very Large Data Bases
Efficiently mining frequent trees in a forest
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
State of the art of graph-based data mining
ACM SIGKDD Explorations Newsletter
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
Multiple-Objective Compression of Data Cubes in Cooperative OLAP Environments
ADBIS '08 Proceedings of the 12th East European conference on Advances in Databases and Information Systems
Journal of Intelligent Information Systems
Three strategies for concurrent processing of frequent itemset queries using FP-growth
KDID'06 Proceedings of the 5th international conference on Knowledge discovery in inductive databases
ISMIS'08 Proceedings of the 17th international conference on Foundations of intelligent systems
Data access paths in processing of sets of frequent itemset queries
ISMIS'11 Proceedings of the 19th international conference on Foundations of intelligent systems
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With an increasing use of data mining tools and techniques, we envision that a Knowledge Discovery and Data Mining System (KDDMS) will have to support and optimize for the following scenarios: 1) Sequence of Queries: A user may analyze one or more datasets by issuing a sequence of related complex mining queries, and 2) Multiple Simultaneous Queries: Several users may be analyzing a set of datasets concurrently, and may issue related complex queries.This paper presents a systematic mechanism to optimize for the above cases, targeting the class of mining queries involving frequent pattern mining on one or multiple datasets. We present a system architecture and propose new algorithms to simultaneously optimize multiple such queries and use a knowledgeable cache to store and utilize the past query results. We have implemented and evaluated our system with both real and synthetic datasets. Our experimental results show that our techniques can achieve a speedup of up to a factor of 9, compared with the systems which do not support caching or optimize for multiple queries.