Improved Sliding Window Algorithms for Clustering and Coverage via Bucketing-Based Sketches
Alessandro Epasto, Mohammad Mahdian, Vahab Mirrokni, Peilin Zhong

TL;DR
This paper introduces a bucketing-based sketch framework for sliding window algorithms, enabling more space-efficient and accurate solutions for clustering, coverage, and diversity maximization in streaming data.
Contribution
The paper presents a novel bucketing-based sketch framework that improves approximation ratios and space efficiency for sliding window algorithms in data streaming.
Findings
Achieves (1±ε)-approximation for k-cover, k-clustering, and diversity maximization.
Reduces space complexity compared to previous algorithms.
Enhances the accuracy of solutions in streaming data scenarios.
Abstract
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 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 -cover, -clustering and diversity maximization problems. For each of the above problems, our algorithm achieves -approximation. Compared with the previous work, it improves both the approximation ratio and the space.
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsData Management and Algorithms · Data Stream Mining Techniques · Stochastic Gradient Optimization Techniques
