Abstract
In this paper, we propose the first deterministic algorithms to solve the frequency estimation and frequent item problems in the
bounded-deletion
model. We establish the space lower bound for solving the deterministic frequent items problem in the bounded-deletion model, and propose Lazy SpaceSaving
±
and SpaceSaving
±
algorithms with optimal space bound. We develop an efficient implementation of the SpaceSaving
±
algorithm that minimizes the latency of update operations using novel data structures. The experimental evaluations testify that SpaceSaving
±
has accurate frequency estimations and achieves very high recall and precision across different data distributions while using minimal space. Our experiments clearly demonstrate that, if allowed the same space, SpaceSaving± is more accurate than the state-of-the-art protocols with up to
logU
- 1/
logU
of the items deleted, where
U
is the size of the input universe. Moreover, motivated by prior work, we propose Dyadic SpaceSaving
±
, the first deterministic quantile approximation sketch in the bounded-deletion model.
Publisher
Association for Computing Machinery (ACM)
Subject
General Earth and Planetary Sciences,Water Science and Technology,Geography, Planning and Development
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