Query expansion using local and global document analysis
SIGIR '96 Proceedings of the 19th annual international ACM SIGIR conference on Research and development in information retrieval
A mathematical theory of communication
ACM SIGMOBILE Mobile Computing and Communications Review
An Approach to Integrating Query Refinement in SQL
EDBT '02 Proceedings of the 8th International Conference on Extending Database Technology: Advances in Database Technology
DBXplorer: A System for Keyword-Based Search over Relational Databases
ICDE '02 Proceedings of the 18th International Conference on Data Engineering
Keyword Searching and Browsing in Databases using BANKS
ICDE '02 Proceedings of the 18th International Conference on Data Engineering
Answering Imprecise Queries over Autonomous Web Databases
ICDE '06 Proceedings of the 22nd International Conference on Data Engineering
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
Discover: keyword search in relational databases
VLDB '02 Proceedings of the 28th international conference on Very Large Data Bases
Probabilistic ranking of database query results
VLDB '04 Proceedings of the Thirtieth international conference on Very large data bases - Volume 30
Introduction to Information Retrieval
Introduction to Information Retrieval
Answering approximate queries over autonomous web databases
Proceedings of the 18th international conference on World wide web
| Hi-index | 0.00 |
There has been a great deal of interest in recent years on ranking query results in relational databases. This paper presents a novel method to rank objects (e.g., tuples) by exploiting the correlations among their attribute values. Given a query, each attribute value is assigned a score according to mutual occurrences with the query and its distribution status in the columns of the attribute. These attribute value scores are aggregated to get a final score for an object. Furthermore, a concept vector is proposed to provide a synopsis of the attribute value in a given database. A concept vector is utilized to get the similar objects. Experimental results demonstrate the performance of our ranking method, RAVC (Ranking with Attribute Value Correlation), in terms of search quality and efficiency.