Inferring Insertion Times and Optimizing Error Penalties in Time-decaying Bloom Filters

Abstract

Current Bloom Filters tend to ignore Bayesian priors as well as a great deal of useful information they hold, compromising the accuracy of their responses. Incorrect responses cause users to incur penalties that are both application- and item-specific, but current Bloom Filters are typically tuned only for static penalties. Such shortcomings are problematic for all Bloom Filter variants, but especially so for Time-decaying Bloom Filters, in which the memory of older items decays over time, causing both false positives and false negatives.

Document Details

Document Type
Pub Defense Publication
Publication Date
Mar 15, 2019
Source ID
10.1145/3284552

Entities

People

  • Chinya V. Ravishankar
  • Jonathan L. Dautrich Jr.

Organizations

  • Google
  • National Science Foundation
  • Office of Naval Research
  • University of California, Riverside

Tags

Readers

  • Adaptive Control and Estimation with Uncertainty in Dynamic Systems.
  • Cybersecurity.
  • Theoretical Analysis.

Technology Areas

  • AI & ML
  • AI & ML - Machine Learning Algorithms