Better Bounds for Frequency Moments in Random-Order Streams
Alexandr Andoni, Andrew McGregor, Krzysztof Onak, Rina Panigrahy
TL;DR
This paper improves the lower bounds on the space complexity for estimating frequency moments in randomly ordered data streams, addressing a gap between theoretical bounds and practical scenarios.
Contribution
It provides tighter lower bounds for space requirements in estimating frequency moments in random-order streams, advancing understanding beyond adversarial models.
Findings
Improved lower bounds for space complexity
Enhanced understanding of random-order stream models
Bridging theoretical gaps in streaming algorithms
Abstract
Estimating frequency moments of data streams is a very well studied problem and tight bounds are known on the amount of space that is necessary and sufficient when the stream is adversarially ordered. Recently, motivated by various practical considerations and applications in learning and statistics, there has been growing interest into studying streams that are randomly ordered. In the paper we improve the previous lower bounds on the space required to estimate the frequency moments of a randomly ordered streams.
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