2021/10/29 by Epasto, Alessandro, Mahdian, Mohammad, Mirrokni, Vahab +1 · 1 citation
#Data Structures and Algorithms (cs.DS) #FOS: Computer and information sciences
paper · doi:10.48550/arxiv.2110.15533
Streaming computation plays an important role in large-scale data analysis. The sliding window model is a model of streaming computation which also captures the recency of the data. In this model, data arrives one item at a time, but only the latest W data items are considered for a particular problem. The goal is to output a good solution at the end of the stream by maintaining a small summary during the stream. In this work, we propose a new algorithmic framework for designing efficient sliding window algorithms via bucketing-based sketches. Based on this new framework, we develop space-efficient sliding window algorithms for k-cover, k-clustering and diversity maximization problems. For each of the above problems, our algorithm achieves (1± ε)-approximation. Compared with the previous work, it improves both the approximation ratio and the space.