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High Performance Discovery In Time Series Techniques 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 o...

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Bibliographic Details
Corporate Author: New York University
Other Authors: Ryan, Donna (Editor)
Format: eBook
Language:English
Published: New York, NY Springer New York 2004, 2004
Edition:1st ed. 2004
Series:Monographs in Computer Science
Subjects:
Models Of Computation
Computer Science
Computer Science / Mathematics
Hardware Performance And Reliability
Artificial Intelligence
Information Storage And Retrieval
Algorithms
Computers
Information Storage And Retrieval Systems
Mathematical Applications In Computer Science
Online Access:
Collection: Springer Book Archives -2004 - Collection details see MPG.ReNa
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

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