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Data Stream Management

Processing High-Speed Data Streams

  • Minos Garofalakis
  • Johannes Gehrke
  • Rajeev Rastogi

Part of the Data-Centric Systems and Applications book series (DCSA)

Table of contents

  1. Front Matter
    Pages I-VII
  2. Minos Garofalakis, Johannes Gehrke, Rajeev Rastogi
    Pages 1-9
  3. Foundations and Basic Stream Synopses

    1. Front Matter
      Pages 11-11
    2. Michael B. Greenwald, Sanjeev Khanna
      Pages 45-86
    3. Graham Cormode, Minos Garofalakis
      Pages 87-102
    4. Phillip B. Gibbons
      Pages 121-147
    5. Mayur Datar, Rajeev Motwani
      Pages 149-165
  4. Mining Data Streams

    1. Front Matter
      Pages 167-167
    2. Sudipto Guha, Nina Mishra
      Pages 169-187
    3. Geoff Hulten, Pedro Domingos
      Pages 189-208
    4. Gurmeet Singh Manku
      Pages 209-219
  5. Advanced Topics

    1. Front Matter
      Pages 239-239
    2. Alin Dobra, Minos Garofalakis, Johannes Gehrke, Rajeev Rastogi
      Pages 241-261
    3. S. Muthukrishnan, Martin Strauss
      Pages 263-281
    4. Graham Cormode, Piotr Indyk
      Pages 283-300
    5. Minos Garofalakis
      Pages 301-314
  6. System Architectures and Languages

    1. Front Matter
      Pages 315-315
    2. Arvind Arasu, Brian Babcock, Shivnath Babu, John Cieslewicz, Mayur Datar, Keith Ito et al.
      Pages 317-336
    3. Uğur Çetintemel, Daniel Abadi, Yanif Ahmad, Hari Balakrishnan, Magdalena Balazinska, Mitch Cherniack et al.
      Pages 337-359
    4. N. Laptev, B. Mozafari, H. Mousavi, H. Thakkar, H. Wang, K. Zeng et al.
      Pages 361-386
    5. Corinna Cortes, Kathleen Fisher, Daryl Pregibon, Anne Rogers, Frederick Smith
      Pages 387-408
    6. Daniel Abadi, Samuel Madden, Wolfgang Lindner
      Pages 409-428
  7. Applications

    1. Front Matter
      Pages 429-429
    2. Charles D. Cranor, Theodore Johnson, Oliver Spatscheck
      Pages 431-449
    3. Yanlei Diao, Michael J. Franklin
      Pages 451-471
    4. Eleftherios Soulas, Dennis Shasha
      Pages 473-497
    5. Spiros Papadimitriou, Anthony Brockwell, Christos Faloutsos
      Pages 499-528
    6. Minos Garofalakis, Johannes Gehrke, Rajeev Rastogi
      Pages 529-537

About this book

Introduction

We live in the era of “Big Data”: Petabytes of digital information are generated daily, and need to be processed and analyzed for interesting patterns and trends. Besides volume, a defining characteristic of Big Data is its velocity; that is, data is instantiated in the form of continuous, high-speed data streams that arrive at rapid rates, and need to be processed and analyzed on a continuous (24x7) basis. Such data streams pose very difficult challenges for conventional data-management architectures, which are built primarily on the concept of persistent, static data collections. This volume focuses on the theory and practice of data stream management, and the novel challenges this emerging domain poses for data-management algorithms, systems, and applications. The collection of chapters, contributed by authorities in the field, offers a comprehensive introduction to both the algorithmic/theoretical foundations of data streams, as well as the streaming systems and applications built in different domains.

A short introductory chapter provides a brief summary of some basic data streaming concepts and models, and discusses the key elements of a generic stream query processing architecture. Subsequently, Part I focuses on basic streaming algorithms for some key analytics functions (e.g., quantiles, norms, join aggregates, heavy hitters) over streaming data. Part II then examines important techniques for basic stream mining tasks (e.g., clustering, classification, frequent itemsets). Part III discusses a number of advanced topics on stream processing algorithms, and Part IV focuses on system and language aspects of data stream processing with surveys of influential system prototypes and language designs. Part V then presents some representative applications of streaming techniques in different domains (e.g., network management, financial analytics). Finally, the volume concludes with an overview of current data streaming products and new application domains (e.g. cloud computing, big data analytics, and complex event processing), and a discussion of future directions in this exciting field.

The book provides a comprehensive overview of core concepts and technological foundations, as well as various systems and applications, and is of particular interest to students, lecturers and researchers in the area of data stream management.

 

Keywords

Data streams Data model extensions Database management system engines Data mining Sensor networks XML query languages

Editors and affiliations

  • Minos Garofalakis
    • 1
  • Johannes Gehrke
    • 2
  • Rajeev Rastogi
    • 3
  1. 1.University Campus - KounoupidianaSchool of ECE, Techn. Univ. of Crete University Campus - KounoupidianaChaniaGreece
  2. 2.Microsoft CorporationRedmondUSA
  3. 3.Amazon India BangaloreIndia

Bibliographic information

  • DOI https://doi.org/10.1007/978-3-540-28608-0
  • Copyright Information Springer-Verlag Berlin Heidelberg 2016
  • Publisher Name Springer, Berlin, Heidelberg
  • eBook Packages Computer Science
  • Print ISBN 978-3-540-28607-3
  • Online ISBN 978-3-540-28608-0
  • Series Print ISSN 2197-9723
  • Series Online ISSN 2197-974X
  • Buy this book on publisher's site