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Infinitely Expandable Filters

Abstract: Filter data structures are widely used to answer approximate set-membership queries in various areas of computer science. A filter needs to be allocated with a given capacity in advance to provide a guarantee over the false positive rate and performance. However, in many applications, the data size is not known in advance, requiring filters to expand dynamically. We show that existing methods for expanding filters do not scale in terms of performance or the false positive rate. This talk will cover InfiniFilter (SIGMOD 2023) and Aleph Filter (VLDB 2024), two new expandable filters that address these challenges. Context: This presentation was given on May 21, 2025 at the Joint Database Systems Seminar shared by the Data Systems Lab at TU Nuremberg, the Systems Group at TU Darmstadt, and the Data Engineering Systems Group at HPI Potsdam. Papers: InfiniFilter: https://dl.acm.org/doi/abs/10.1145/35... Aleph Filter: https://arxiv.org/abs/2404.04703 Bio: Niv Dayan is an assistant professor at the University of Toronto (UofT). He is interested in designing and analyzing data structures for database and storage systems. Before joining UofT, he was a research scientist at Pliops and a technical advisor for Speedb. He was a postdoc at Harvard and Copenhagen University, and his PhD is from the IT University of Copenhagen.