Data Streams: Algorithms and Applications

Author(s): S Muthukrishnan

Source:
    Journal:Foundations and Trends® in Theoretical Computer Science
    ISSN Print:1551-305X,  ISSN Online:1551-3068
    Publisher:Now Publishers
    Volume 1 Number 2,

Document Type: Article
Pages: 120(117-236)
DOI: 10.1561/0400000002

Abstract: In the data stream scenario, input arrives very rapidly and there is limited memory to store the input. Algorithms have to work with one or few passes over the data, space less than linear in the input size or time significantly less than the input size. In the past few years, a new theory has emerged for reasoning about algorithms that work within these constraints on space, time, and number of passes. Some of the methods rely on metric embeddings, pseudo-random computations, sparse approximation theory and communication complexity. The applications for this scenario include IP network traffic analysis, mining text message streams and processing massive data sets in general. Researchers in Theoretical Computer Science, Databases, IP Networking and Computer Systems are working on the data stream challenges. This article is an overview and survey of data stream algorithmics and is an updated version of?[1].

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