The Frequent Items Problem in Online Streaming under Various Performance Measures

TL;DR

This paper enhances the understanding of the Frequent Items Problem in online streaming by applying multiple performance measures beyond competitive analysis, revealing new insights into algorithm performance.

Contribution

It provides a detailed comparative analysis of performance measures for the Frequent Items Problem, extending beyond traditional competitive analysis.

Findings

Alternative performance measures offer different insights into algorithm effectiveness.

Competitive analysis has limitations in certain scenarios.

The study advances the understanding of online algorithms' performance evaluation.

Abstract

In this paper, we strengthen the competitive analysis results obtained for a fundamental online streaming problem, the Frequent Items Problem. Additionally, we contribute with a more detailed analysis of this problem, using alternative performance measures, supplementing the insight gained from competitive analysis. The results also contribute to the general study of performance measures for online algorithms. It has long been known that competitive analysis suffers from drawbacks in certain situations, and many alternative measures have been proposed. However, more systematic comparative studies of performance measures have been initiated recently, and we continue this work, using competitive analysis, relative interval analysis, and relative worst order analysis on the Frequent Items Problem.