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KARL: Fast Kernel Aggregation Queries
Tsz Nam Chan1,2; Man Lung Yiu2; Leong Hou U3
2019
Conference Namethe 35th IEEE International Conference on Data Engineering (ICDE)
Source PublicationProceedings of the 35th IEEE International Conference on Data Engineering (ICDE)
Conference Date2019
Conference PlaceMacao
Abstract

Kernel functions support a broad range of applications that require tasks like density estimation, classification, or outlier detection. In these tasks, a common online operation is to compute the weighted aggregation of kernel function values with respect to a set of points. Scalable aggregation methods are still unknown for typical kernel functions (e.g., Gaussian kernel, polynomial kernel, and sigmoid kernel) and weighting schemes. In this paper, we propose a novel and effective bounding technique to speedup the computation of kernel aggregation. We further boost its efficiency by leveraging index structures and exploiting index tuning opportunities. In addition, our technique is extensible to different types of kernel functions and weightings. Experimental studies on many real datasets reveal that our proposed method achieves speedups of 2.5–738 over the stateof-the-art.

Language英语
Fulltext Access
Document TypeConference paper
CollectionFaculty of Science and Technology
DEPARTMENT OF COMPUTER AND INFORMATION SCIENCE
Affiliation1.Department of Computer Science, The University of Hong Kong
2.Department of Computing, Hong Kong Polytechnic University
3.Department of Computer and Information Science, University of Macau
Recommended Citation
GB/T 7714
Tsz Nam Chan,Man Lung Yiu,Leong Hou U. KARL: Fast Kernel Aggregation Queries[C],2019.
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