2019/02/28 by Arik Rinberg, Alexander Spiegelman, Rinberg, Arik +11 · 1 citation
Computer Science · #Advanced Database Systems and Queries #Data Management and Algorithms #Data Stream Mining Techniques #Data Structures and Algorithms (cs.DS) #Distributed #FOS: Computer and information sciences #Parallel #and Cluster Computing (cs.DC)
paper · pdf · doi:10.48550/arxiv.1902.10995
openalex publication_date 2019/02/28 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Data sketches are approximate succinct summaries of long streams. They are widely used for processing massive amounts of data and answering statistical queries about it in real-time. Existing libraries producing sketches are very fast, but do not allow parallelism for creating sketches using multiple threads or querying them while they are being built. We present a generic approach to parallelising data sketches efficiently, while bounding the error that such parallelism introduces. Utilising relaxed semantics and the notion of strong linearisability we prove our algorithm's correctness and analyse the error it induces in two specific sketches. Our implementation achieves high scalability while keeping the error small.