KARL: Fast kernel aggregation queries

Tsz Nam Chan, Man Lung Yiu, Leong Hou

Research output: Chapter in book / Conference proceedingConference article published in proceeding or bookAcademic researchpeer-review

9 Citations (Scopus)


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 state-of-the-art.

Original languageEnglish
Title of host publicationProceedings - 2019 IEEE 35th International Conference on Data Engineering, ICDE 2019
PublisherIEEE Computer Society
Number of pages12
ISBN (Electronic)9781538674741
Publication statusPublished - 1 Apr 2019
Event35th IEEE International Conference on Data Engineering, ICDE 2019 - Macau, China
Duration: 8 Apr 201911 Apr 2019

Publication series

NameProceedings - International Conference on Data Engineering
ISSN (Print)1084-4627


Conference35th IEEE International Conference on Data Engineering, ICDE 2019


  • Kernel aggregation queries
  • Kernel functions

ASJC Scopus subject areas

  • Software
  • Signal Processing
  • Information Systems


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