Information filtering and information retrieval: two sides of the same coin?
Communications of the ACM - Special issue on information filtering
The OLAP market: state of the art and research issues
Proceedings of the 1st ACM international workshop on Data warehousing and OLAP
Clustering user queries of a search engine
Proceedings of the 10th international conference on World Wide Web
Modern Information Retrieval
Learning Collaborative Information Filters
ICML '98 Proceedings of the Fifteenth International Conference on Machine Learning
Using Association Rules to Discover Search Engines Related Queries
LA-WEB '03 Proceedings of the First Conference on Latin American Web Congress
Evaluating collaborative filtering recommender systems
ACM Transactions on Information Systems (TOIS)
IEEE Transactions on Knowledge and Data Engineering
Mining search engine query logs for query recommendation
Proceedings of the 15th international conference on World Wide Web
A framework for recommending OLAP queries
Proceedings of the ACM 11th international workshop on Data warehousing and OLAP
Query Recommendations for Interactive Database Exploration
SSDBM 2009 Proceedings of the 21st International Conference on Scientific and Statistical Database Management
Query recommendations for OLAP discovery driven analysis
Proceedings of the ACM twelfth international workshop on Data warehousing and OLAP
Empirical analysis of predictive algorithms for collaborative filtering
UAI'98 Proceedings of the Fourteenth conference on Uncertainty in artificial intelligence
Query recommendation using query logs in search engines
EDBT'04 Proceedings of the 2004 international conference on Current Trends in Database Technology
| Hi-index | 0.00 |
The queries in Online Analytical Processing (OLAP) are user-guided. OLAP is based on a multidimensional data model for complex analytical and ad-hoc queries with a rapid execution time. Those queries are either routed or on-demand revolved around the OLAP task. Most such queries are reusable and optimized in the system. Therefore, the queries recorded in the query logs for completing various OLAP tasks may be reusable. The query logs usually contain a sequence of SQL queries that show the action flows of users for their preference, their interests, and their behaviours during the action. This research investigates the feature extraction to identify query patterns and user behaviours from historical query logs. The expected results will be used to recommend forthcoming queries to help decision makers with data analysis. The purpose of this work is to improve the efficiency and effectiveness of OLAP in terms of computation cost and response time. Furthermore, the proposed OLAP system will be able to adjust some parameters for finding common behaviours from different users that make the recommendation system flexible and user-adaptive.