High Performance Discovery In Time Series (eBook, PDF)

Techniques and Case Studies
Redaktion: New York University Courant Inst. Mathematical Sciences; Ryan, Donna

• Format: PDF

Produktbeschreibung

This book presents concepts and techniques for describing and analyzing large-scale time-series data streams, which, for example, is critical for complex real-world data in telecommunications, bioinformatics, and finance databases. The work aims at efficient discovery in time series, rather than at prediction, and 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). Database and online Web Services researchers and professionals will appreciate the book's algorithmic contributions, as well as its practical aspects and many case studies. Graduate students studying databases or interested in massive time-series data will find the book an essential resource.

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Produktdetails

• Verlag: Springer US
• Seitenzahl: 190
• Erscheinungstermin: 9. November 2013
• Englisch
• ISBN-13: 9781475740462
• Artikelnr.: 44184639

Autorenporträt

Dennis Shasha, Courant Institute, New York, NY, USA / Yunyue Zhu, Courant Institute, New York, NY, USA

Inhaltsangabe

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.

Rezensionen

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)