
TL;DR
CacheDiff is a novel sampling method that efficiently selects k random items from a known pool of N items with optimal time and space complexity, suitable for large datasets.
Contribution
The paper introduces CacheDiff, a new sampling algorithm with O(k) time and space complexity, improving efficiency over existing methods.
Findings
Achieves constant time per sample after preprocessing
Uses minimal memory proportional to k
Suitable for large-scale data sampling
Abstract
We present a sampling method called, CacheDiff, that has both time and space complexity of O(k) to randomly select k items from a pool of N items, in which N is known.
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Taxonomy
TopicsAdvanced Database Systems and Queries · Data Management and Algorithms · Time Series Analysis and Forecasting
