Mining association rules between sets of items in large databases
SIGMOD '93 Proceedings of the 1993 ACM SIGMOD international conference on Management of data
Optimal aggregation algorithms for middleware
Journal of Computer and System Sciences - Special issu on PODS 2001
Evaluating Top-k Queries over Web-Accessible Databases
ICDE '02 Proceedings of the 18th International Conference on Data Engineering
Automatic categorization of query results
SIGMOD '04 Proceedings of the 2004 ACM SIGMOD international conference on Management of data
Proceedings of the 2006 ACM SIGMOD international conference on Management of data
Ordering the attributes of query results
Proceedings of the 2006 ACM SIGMOD international conference on Management of data
Probabilistic information retrieval approach for ranking of database query results
ACM Transactions on Database Systems (TODS)
Query result ranking over e-commerce web databases
CIKM '06 Proceedings of the 15th ACM international conference on Information and knowledge management
Journal of Artificial Intelligence Research
Hi-index | 0.00 |
To deal with the problem of too many results returned from a Web database in response to a user query, this paper proposes a novel approach, which takes advantage of the contextual preferences to precompute a few representative orders of tuples and uses them to expeditiously provide ranked answers factoring in the information contained in the query. Contextual preferences take the form that item i1 is preferred to item i2 with an interest degree in the context of X. This paper formally defines contextual preferences, provides algorithms for creating tuple orders, clustering orders and processing queries, and presents experimental results to show their efficiency.