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Home > Reference > Research and information: general > Information theory > Cybernetics and systems theory > High Performance Discovery In Time Series: Techniques and Case Studies(Monographs in Computer Science)
High Performance Discovery In Time Series: Techniques and Case Studies(Monographs in Computer Science)

High Performance Discovery In Time Series: Techniques and Case Studies(Monographs in Computer Science)


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About the Book

Time-series data - data arriving in time order, or a data stream - can be found in fields such as physics, finance, music, networking, and medical instrumentation. Designing fast, scalable algorithms for analyzing single or multiple time series can yield scientific discoveries, medical diagnoses, and certainly profits.High Performance Discovery in Time Series presents rapid-discovery techniques for finding portions of time series with many events (i.e., gamma-ray scatterings) and finding closely related time series (i.e., highly correlated price histories, or musical melodies). Such real-time streaming data analysis is critical for complex real-world data in telecommunications, bioinformatics, and finance databases.This new monograph provides a technical survey of concepts and techniques for describing and analyzing large-scale time-series data streams. It offers essential coverage of the topic for database and online web services researchers and professionals, as well as an ideal resource for graduates.

Table of Contents:
1 Time Series Preliminaries.- 2 Data Reduction and Transformation Techniques.- 3 Indexing Methods.- 4 Flexible Similarity Search.- 5 StatStream.- 6 Query by Humming.- 7 Elastic Burst Detection.- 8 A Call to Exploration.- A Answers to the Questions.- A.2 Chapter 2.- A.3 Chapter 3.- A.4 Chapter 4.- A.5 Chapter 5.- A.6 Chapter 6.- A.7 Chapter 7.- References.

Review :
From the reviews: "The goal of the book is to show how to design fast scalable algorithms for the analysis of time series when much data must be analyzed. … A linear time filter is constructed in such a way that no burst will be missed and nearly all false positives are eliminated. … the book aims at efficient discovery in time series and presents practical algorithms for this task." (Jiri Andel, Mathematical Reviews, 2005)  


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Product Details
  • ISBN-13: 9780387008578
  • Publisher: Springer-Verlag New York Inc.
  • Publisher Imprint: Springer-Verlag New York Inc.
  • Height: 235 mm
  • No of Pages: 190
  • Returnable: N
  • Sub Title: Techniques and Case Studies
  • ISBN-10: 0387008578
  • Publisher Date: 01 Jun 2004
  • Binding: Hardback
  • Language: English
  • Returnable: Y
  • Series Title: Monographs in Computer Science
  • Width: 155 mm


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