Frequent Elements with Witnesses in Data Streams
Christian Konrad

TL;DR
This paper extends the classic frequent elements problem in data streams to include associated meta-data, providing optimal algorithms for both insertion-only and insertion-deletion models with space complexity bounds.
Contribution
It introduces the witness version of the frequent elements problem, incorporating meta-data, and presents provably optimal algorithms with tight space bounds for both stream models.
Findings
Optimal space complexity algorithms for insertion-only streams.
Optimal space bounds for insertion-deletion streams.
Extension of frequent elements detection to include meta-data.
Abstract
Detecting frequent elements is among the oldest and most-studied problems in the area of data streams. Given a stream of data items in , the objective is to output items that appear at least times, for some threshold parameter , and provably optimal algorithms are known today. However, in many applications, knowing only the frequent elements themselves is not enough: For example, an Internet router may not only need to know the most frequent destination IP addresses of forwarded packages, but also the timestamps of when these packages appeared or any other meta-data that "arrived" with the packages, e.g., their source IP addresses. In this paper, we introduce the witness version of the frequent elements problem: Given a desired approximation guarantee and a desired frequency , where is the frequency of the most…
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