Abstract
Top-k querying can significantly improve the performance of web-based business intelligence applications such as price comparison and product recommendation systems. Top-k retrieval involves finding a limited number of records in a relational database that are most similar to user-specified attribute-value pairs. This paper extends the cost-based query-mapping method for top-k retrieval by incorporating data skewness in range estimation. Experiments on real world and synthetic multi-attribute data sets show that incorporating data skewness provides a robust performance across different types of data sets, query sets, distance functions, and histograms.
Original language | English (US) |
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Pages (from-to) | 258-273 |
Number of pages | 16 |
Journal | Decision Support Systems |
Volume | 57 |
Issue number | 1 |
DOIs | |
State | Published - Jan 2014 |
Keywords
- Cost model
- Query processing
- Query-mapping
- RDBMSs
- Top-k query
ASJC Scopus subject areas
- Management Information Systems
- Information Systems
- Developmental and Educational Psychology
- Arts and Humanities (miscellaneous)
- Information Systems and Management