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Stream Data Processing: A Quality of Service Perspective

Modeling, Scheduling, Load Shedding, and Complex Event Processing

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  • © 2009

Overview

Authors:
  1. Qingchun Jiang
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  2. Sharma Chakravarthy
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  • Includes important aspects of a QoS-driven DSMS (Data Stream Management System)
  • Introduces applications where a DSMS can be used and discusses needs beyond the stream processing model
  • Discusses in detail the design and implementation of MavStream

Part of the book series: Advances in Database Systems (ADBS, volume 36)

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Table of contents (12 chapters)

  1. Front Matter

    Pages 1-22
    Download chapter PDF

  2. INTRODUCTION

    • Sharma Chakravarthy, Qingchun Jiang
    Pages 1-7

  3. OVERVIEW OF DATA STREAM PROCESSING

    • Sharma Chakravarthy, Qingchun Jiang
    Pages 9-21

  4. DSMS CHALLENGES

    • Sharma Chakravarthy, Qingchun Jiang
    Pages 23-31

  5. LITERATURE REVIEW

    • Sharma Chakravarthy, Qingchun Jiang
    Pages 33-48

  6. MODELING CONTINUOUS QUERIES OVER DATA STREAMS

    • Sharma Chakravarthy, Qingchun Jiang
    Pages 49-93

  7. SCHEDULING STRATEGIES FOR CQs

    • Sharma Chakravarthy, Qingchun Jiang
    Pages 95-136

  8. LOAD SHEDDING IN DATA STREAM MANAGEMENT SYSTEMS

    • Sharma Chakravarthy, Qingchun Jiang
    Pages 137-166

  9. NFMi: AN INTER-DOMAIN NETWORK FAULT MANAGEMENT SYSTEM

    • Sharma Chakravarthy, Qingchun Jiang
    Pages 167-186

  10. INTEGRATING STREAM AND COMPLEX EVENT PROCESSING

    • Sharma Chakravarthy, Qingchun Jiang
    Pages 187-214

  11. MavStream: DEVELOPMENT OF A DSMS PROTOTYPE

    • Sharma Chakravarthy, Qingchun Jiang
    Pages 215-260

  12. INTEGRATING CEP WITH A DSMS

    • Sharma Chakravarthy, Qingchun Jiang
    Pages 261-285

  13. CONCLUSIONS AND FUTURE DIRECTIONS

    • Sharma Chakravarthy, Qingchun Jiang
    Pages 287-293

  14. Back Matter

    Pages 1-30
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Keywords

About this book

In recent years, a new class of applications has come to the forefront { p- marily due to the advancement in our ability to collect data from multitudes of devices, and process them e ciently. These include homeland security - plications, sensor/pervasive computing applications, various kinds of mo- toring applications, and even traditional applications belonging to nancial, computer network management, and telecommunication domains. These - plications need to process data continuously (and as long as data is available) from one or more sources. The sequence of data items continuously gen- ated by sources is termed a data stream. Because of the possible never-ending nature of a data stream, the amount of data to be processed is likely to be unbounded. In addition, timely detection of interesting changes or patterns or aggregations over incoming data is critical for many of these applications. Furthermore, the data arrival rates may uctuate over a period of time and may be bursty at times. For most of these applications, Quality of Service (or QoS) requirements, such as response time, memory usage, and throughput are extremely important. These application requirements make it infeasible to simply load the incoming data streams into a persistent store and process them e ectively using currently available database management techniques.

Reviews

From the reviews:

“The motivation for this book comes from the need for applications that can process data continuously from one or more sources, with good performance in terms of satisfying the requirements of Quality of Service … . The material of this book can be used for a graduate course, as a reference book for the researcher familiar with this domain, or as a very good introductory text for people interested in the areas of stream and complex event processing.” (Mirel Cosulschi, Zentralblatt MATH, Vol. 1170, 2009)

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