SIGMOD '00 Proceedings of the 2000 ACM SIGMOD international conference on Management of data
ICDE '95 Proceedings of the Eleventh International Conference on Data Engineering
Webview selection from user access patterns
Proceedings of the ACM first Ph.D. workshop in CIKM
Data mining-based materialized view and index selection in data warehouses
Journal of Intelligent Information Systems
CBAR: an efficient method for mining association rules
Knowledge-Based Systems
State transfer graph: an efficient tool for webview maintenance
WAIM'05 Proceedings of the 6th international conference on Advances in Web-Age Information Management
Clustering-based materialized view selection in data warehouses
ADBIS'06 Proceedings of the 10th East European conference on Advances in Databases and Information Systems
MOWS: macro and micro online webview selection
Proceedings of the 3rd International Conference on Web Intelligence, Mining and Semantics
| Hi-index | 0.00 |
In this paper, we propose an approach, which is based on web usage mining techniques, to recommend webviews to be materialized. The webview materialization is a term used to represent the transformation of dynamic web data into equivalent static web data. That is the creation of a static instance of a dynamic web page, at a certain point in time. In this work we will extend our previous approach [6] which concerns the use of sequential patterns to recommend the materialized webviews. Firstly we analyze the DIWS historic to extract the frequent sequential patterns and the frequent association rules. Then we use these repetitive behaviors to calculate a materialization weight for each webview. The webviews with high materialization weights are the most recommended for the materialization. Our experiment results show that our approach reduces the materialization risk more than those using only the recent time period to select the materialized webviews.