# scaleBF: A High Scalable Membership Filter using 3D Bloom Filter

## TL;DR

This paper introduces scaleBF, a novel high-scalability Bloom Filter using 3D Bloom Filters, which improves scalability and performance for large-scale data filtering without increasing complexity.

## Contribution

The paper presents scaleBF, a new Bloom Filter variant employing 3D Bloom Filters to enhance scalability while maintaining efficiency.

## Key findings

- scaleBF outperforms existing Bloom Filters in scalability
- Theoretical analysis shows improved time complexity
- Effective for large-scale data filtering

## Abstract

Bloom Filter is extensively deployed data structure in various applications and research domain since its inception. Bloom Filter is able to reduce the space consumption in an order of magnitude. Thus, Bloom Filter is used to keep information of a very large scale data. There are numerous variants of Bloom Filters available, however, scalability is a serious dilemma of Bloom Filter for years. To solve this dilemma, there are also diverse variants of Bloom Filter. However, the time complexity and space complexity become the key issue again. In this paper, we present a novel Bloom Filter to address the scalability issue without compromising the performance, called scaleBF. scaleBF deploys many 3D Bloom Filter to filter the set of items. In this paper, we theoretically compare the contemporary Bloom Filter for scalability and scaleBF outperforms in terms of time complexity.