Overview
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.
Full Product Details
Author: New York University ,
New York University ,
Donna Ryan ,
New York University
Publisher: Springer-Verlag New York Inc.
Imprint: Springer-Verlag New York Inc.
Edition: and and ed.
Dimensions:
Width: 15.50cm
, Height: 1.20cm
, Length: 23.50cm
Weight: 0.482kg
ISBN: 9780387008578
ISBN 10: 0387008578
Pages: 190
Publication Date: 03 June 2004
Audience:
College/higher education
,
Professional and scholarly
,
Tertiary & Higher Education
,
Professional & Vocational
Format: Hardback
Publisher's Status: Active
Availability: In Print
This item will be ordered in for you from one of our suppliers. Upon receipt, we will promptly dispatch it out to you. For in store availability, please contact us.
Reviews
From the reviews: <p> 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 ] A linear time filter is constructed in such a way that no burst will be missed and nearly all false positives are eliminated. a ] the book aims at efficient discovery in time series and presents practical algorithms for this task. (Jiri Andel, Mathematical Reviews, 2005) <p>
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)
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)
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)
