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Top-k Similarity Join over Multi-valued Objects

7 years 8 months ago
Top-k Similarity Join over Multi-valued Objects
The top-k similarity joins have been extensively studied and used in a wide spectrum of applications such as information retrieval, decision making, spatial data analysis and data mining. Given two sets of objects U and V, a top-k similarity join returns k pairs of most similar objects from UV. In the conventional model of top-k similarity join processing, an object is usually regarded as a point in a multi-dimensional space and the similarity between two objects is usually measured by distance metrics such as Euclidean distance. However, in many applications an object may be described by multiple values (instances) and the conventional model is not applicable since it does not address the distributions of object instances. In this paper, we study top-k similarity join queries over multi-valued objects. We apply quantile based distance to explore the relative instance distribution among the multiple instances of objects. Efficient and effective techniques to process top-k...
Wenjie Zhang, Jing Xu, Xin Liang, Ying Zhang, Xuem
Added 08 Feb 2012
Updated 08 Feb 2012
Type Conference
Year 2012
Where DASFAA
Authors Wenjie Zhang, Jing Xu, Xin Liang, Ying Zhang, Xuemin Lin
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