  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.
