A database perspective on knowledge discovery
Communications of the ACM
Efficiently mining long patterns from databases
SIGMOD '98 Proceedings of the 1998 ACM SIGMOD international conference on Management of data
SPADE: an efficient algorithm for mining frequent sequences
Machine Learning
Multi-dimensional sequential pattern mining
Proceedings of the tenth international conference on Information and knowledge management
Mining Sequential Patterns: Generalizations and Performance Improvements
EDBT '96 Proceedings of the 5th International Conference on Extending Database Technology: Advances in Database Technology
ICDE '95 Proceedings of the Eleventh International Conference on Data Engineering
PrefixSpan: Mining Sequential Patterns by Prefix-Projected Growth
Proceedings of the 17th International Conference on Data Engineering
SPIRIT: Sequential Pattern Mining with Regular Expression Constraints
VLDB '99 Proceedings of the 25th International Conference on Very Large Data Bases
A New SQL-like Operator for Mining Association Rules
VLDB '96 Proceedings of the 22th International Conference on Very Large Data Bases
Mining Various Patterns in Sequential Data in an SQL-like Manner
ADBIS '99 Proceedings of the Third East European Conference on Advances in Databases and Information Systems
Mining Algorithms for Sequential Patterns in Parallel: Hash Based Approach
PAKDD '98 Proceedings of the Second Pacific-Asia Conference on Research and Development in Knowledge Discovery and Data Mining
Proceedings of the IIS'2002 Symposium on Intelligent Information Systems
Efficient Constraint-Based Sequential Pattern Mining Using Dataset Filtering Techniques
Proceedings of the Baltic Conference, BalticDB&IS 2002 - Volume 1
A perspective on inductive databases
ACM SIGKDD Explorations Newsletter
An Efficient Algorithm for Mining Frequent Sequences by a New Strategy without Support Counting
ICDE '04 Proceedings of the 20th International Conference on Data Engineering
Fast discovery of sequential patterns in large databases using effective time-indexing
Information Sciences: an International Journal
Generalization of pattern-growth methods for sequential pattern mining with gap constraints
MLDM'03 Proceedings of the 3rd international conference on Machine learning and data mining in pattern recognition
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This paper introduces MineSP, a relational-like operator to mine sequential patterns from databases. It also shows how an inductive query can be translated into a traditional query tree augmented with MineSP nodes. This query tree is then optimized, choosing the mining algorithm that best suits the constraints specified by the user and the execution environment conditions. The SPMiner prototype system supporting our approach is also presented.