Mining association rules between sets of items in large databases
SIGMOD '93 Proceedings of the 1993 ACM SIGMOD international conference on Management of data
SPADE: an efficient algorithm for mining frequent sequences
Machine Learning
Efficient mining of traversal patterns
Data & Knowledge Engineering - Building web warehouse
Efficient Data Mining for Path Traversal Patterns
IEEE Transactions on Knowledge and Data Engineering
Mining Sequential Patterns: Generalizations and Performance Improvements
EDBT '96 Proceedings of the 5th International Conference on Extending Database Technology: Advances in Database Technology
Discovery of Multiple-Level Association Rules from Large Databases
VLDB '95 Proceedings of the 21th International Conference on Very Large Data Bases
ISWC '02 Proceedings of the First International Semantic Web Conference on The Semantic Web
An introduction to description logics
The description logic handbook
Web usage mining: discovery and application of interesting patterns from web data
Web usage mining: discovery and application of interesting patterns from web data
Web usage mining based on probabilistic latent semantic analysis
Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
MapReduce: simplified data processing on large clusters
OSDI'04 Proceedings of the 6th conference on Symposium on Opearting Systems Design & Implementation - Volume 6
A Web Usage Mining Framework for Mining Evolving User Profiles in Dynamic Web Sites
IEEE Transactions on Knowledge and Data Engineering
Smart Miner: a new framework for mining large scale web usage data
Proceedings of the 18th international conference on World wide web
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With the increasing use of dynamic page generation, asynchronous page loading (AJAX) and rich user interaction in the Web, it is possible to capture more information for web usage analysis.While these advances seem a great opportunity to collect more information about web user, the complexity of the usage data also increases. As a result, traditional page-view based web usage mining methods have become insufficient to fully understand web usage behavior. In order to solve the problems with current approaches our framework incorporates semantic knowledge in the usage mining process and produces semantic event patterns from web usage logs. In order to model web usage behavior at a more abstract level, we define the concept of semantic events, event based sessions and frequent event patterns.