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Q&A | February 27, 2014

Rene Denfeld: IMG Powell’s Q&A: Rene Denfeld



Describe your latest book. The Enchanted is a story narrated by a man on death row. The novel was inspired by my work as a death penalty... Continue »
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    Rene Denfeld 9780062285508

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

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

 

Synopses & Reviews

Publisher Comments:

  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 lead to scientific discoveries, medical diagnoses, and perhaps 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 and return histories, or musical melodies). A typical time-series technique may compute a "consensus" time series--from a collection of time series--to use regression analysis for predicting future time points. By contrast, this book aims at efficient discovery in time series, rather than prediction, and its novelty lies in its algorithmic contributions and its simple, practical algorithms and case studies. It presumes familiarity with only basic calculus and some linear algebra. Topics and Features: *Presents efficient algorithms for discovering unusual bursts of activity in large time-series databases * Describes the mathematics and algorithms for finding correlation relationships between thousands or millions of time series across fixed or moving windows *Demonstrates strong, relevant applications built on a solid scientific basis *Outlines how readers can adapt the techniques for their own needs and goals *Describes algorithms for query by humming, gamma-ray burst detection, pairs trading, and density detection *Offers self-contained descriptions of wavelets, fast Fourier transforms, and sketches as they apply to time-series analysis 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 computer scientists, physicists, medical researchers, financial mathematicians, musicologists, and researchers and professionals who must analyze massive time series. In addition, it can serve as an ideal text/reference for graduate students in many data-rich disciplines.

Synopsis:

This monograph is a technical survey of concepts and techniques for describing and analyzing large-scale time-series data streams. Some topics covered are algorithms for query by humming, gamma-ray burst detection, pairs trading, and density detection. Included are self-contained descriptions of wavelets, fast Fourier transforms, and sketches as they apply to time-series analysis. Detailed applications are built on a solid scientific basis.

Table of Contents

I--REVIEW OF TECHNIQUES: * Time series preliminaries * Data reduction and transformation techniques * Indexing methods * Flexible similarity search II--CASE STUDIES: * StatStream * Query by humming * Elastic burst detection * A call to exploration * Answers to questions * References * Index

Product Details

ISBN:
9781441918420
Author:
New York University
Publisher:
Springer
Author:
University, New York
Location:
New York, NY
Subject:
Information technology
Subject:
Data reduction.
Subject:
Genomics and proteomics
Subject:
Sensors
Subject:
Transformation techniques
Subject:
Models and Principles
Subject:
Information storage and retrieval.
Subject:
Algorithm Analysis and Problem Complexity
Subject:
Computing Methodologies.
Subject:
Performance and Reliability
Subject:
Math Applications in Computer Science
Subject:
Math Applications in Computer Science <P>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 f
Subject:
Internet-Information
Subject:
Online Services - General
Subject:
Computer Science
Subject:
B
Subject:
Information storage and retrieva
Subject:
Computer software
Subject:
Electronic data processing
Subject:
Operating systems (computers)
Copyright:
Edition Description:
1st Edition. Softcover version of original hardcover edition 2004
Series:
Monographs in Computer Science
Publication Date:
20110306
Binding:
TRADE PAPER
Language:
English
Pages:
205
Dimensions:
235 x 155 mm

Related Subjects

Computers and Internet » Computers Reference » General
Computers and Internet » Internet » Information
Computers and Internet » Personal Computers » General
Health and Self-Help » Health and Medicine » Medical Specialties

High Performance Discovery in Time Series: Techniques and Case Studies (Monographs in Computer Science) New Trade Paper
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$180.25 In Stock
Product details 205 pages Springer - English 9781441918420 Reviews:
"Synopsis" by , This monograph is a technical survey of concepts and techniques for describing and analyzing large-scale time-series data streams. Some topics covered are algorithms for query by humming, gamma-ray burst detection, pairs trading, and density detection. Included are self-contained descriptions of wavelets, fast Fourier transforms, and sketches as they apply to time-series analysis. Detailed applications are built on a solid scientific basis.
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