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. We address these issues by introducing inferential filters, which integrate Bayesian priors and information latent in filters to make penalty-optimal, query-specific decisions. We also show how to properly infer insertion times in such filters. Our methods are general, but here we illustrate their application to inferential time-decaying filters to support novel query types and sliding window queries with dynamic error penalties. We present inferential versions of the Timing Bloom Filter and Generalized Bloom Filter. Our experiments on real and synthetic datasets show that our methods reduce penalties for incorrect responses to sliding-window queries in these filters by up to 70% when penalties are dynamic.

中文翻译：

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在时间衰减的布隆过滤器中推断插入时间和优化错误惩罚

当前的布隆过滤器倾向于忽略贝叶斯先验以及它们持有的大量有用信息，从而损害了它们响应的准确性。不正确的响应会导致用户受到特定于应用程序和特定项目的惩罚，但当前的布隆过滤器通常仅针对静态惩罚进行调整。这些缺点对于所有 Bloom Filter 变体都是有问题的，但对于 Time-decaying Bloom Filters 尤其如此，其中旧项目的内存会随着时间的推移而衰减，从而导致误报和误报。我们通过引入来解决这些问题推理的过滤器，它集成了贝叶斯先验和过滤器中潜在的信息，以做出惩罚最优的、特定于查询的决策。我们还展示了如何正确推断此类过滤器中的插入时间。我们的方法是通用的，但在这里我们说明它们的应用推理时间衰减滤波器支持新颖的查询类型和具有动态错误惩罚的滑动窗口查询。我们展示了 Timing Bloom Filter 和 Generalized Bloom Filter 的推理版本。我们在真实和合成数据集上的实验表明，当惩罚是动态的时，我们的方法将这些过滤器中对滑动窗口查询的错误响应的惩罚减少了高达 70%。