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Answering top-k queries with multi-dimensional selections: The ranking cube approach (2006)
| Content Provider | CiteSeerX |
|---|---|
| Author | Xin, Dong Han, Jiawei Cheng, Hong Li, Xiaolei |
| Description | In VLDB Observed in many real applications, a top-k query often consists of two components to reflect a user’s preference: a selection condition and a ranking function. A user may not only propose ad hoc ranking functions, but also use different interesting subsets of the data. In many cases, a user may want to have a thorough study of the data by initiating a multi-dimensional analysis of the top-k query results. Previous work on top-k query processing mainly focuses on optimizing data access according to the ranking function only. The problem of efficient answering top-k queries with multidimensional selections has not been well addressed yet. This paper proposes a new computational model, called ranking cube, for efficient answering top-k queries with multidimensional selections. We define a rank-aware measure for the cube, capturing our goal of responding to multidimensional ranking analysis. Based on the ranking cube, an efficient query algorithm is developed which progressively retrieves data blocks until the top-k results are found. The curse of dimensionality is a well-known challenge for the data cube and we cope with this difficulty by introducing a new technique of ranking fragments. Our experiments on Microsoft’s SQL Server 2005 show that our proposed approaches have significant improvement over the previous methods. 1. |
| File Format | |
| Language | English |
| Publisher Date | 2006-01-01 |
| Access Restriction | Open |
| Subject Keyword | Ranking Cube Approach Previous Method Thorough Study Different Interesting Subset Ranking Function Data Cube Multi-dimensional Selection Many Real Application New Technique Efficient Query Algorithm Top-k Result Multidimensional Selection Data Access Previous Work Top-k Query Processing Many Case Selection Condition Significant Improvement Rank-aware Measure Propose Ad Hoc Top-k Query Result Top-k Query Well-known Challenge Microsoft Sql Server New Computational Model Data Block Ranking Analysis Ranking Cube User Preference Multi-dimensional Analysis |
| Content Type | Text |
| Resource Type | Article |
