Statistical analysis of massive data streams : proceedings of a workshop / Committee on Applied and Theoretical Statistics.

Format:

Book

Language:

English

Subjects (All):

Mathematical statistics.

Statistics--Data processing.

Statistics.

Physical Description:

1 online resource (395 p.)

Edition:

1st ed.

Place of Publication:

Washington, D.C. : National Academy Press, 2004.

Language Note:

English

Summary:

Massive data streams--large quantities of data that arrive continuously--are becoming increasingly commonplace in many areas of science and technology. Consequently development of analytical methods for such streams is of growing importance. To address this issue, the National Security Agency asked the NRC to hold a workshop to explore methods for analysis of streams of data so as to stimulate progress in the field. This report presents the results of that workshop. It provides presentations that focused on five different research areas where massive data streams are present: atmospheric and meteorological data; high-energy physics; integrated data systems; network traffic; and mining commercial data streams. The goals of the report are to improve communication among researchers in the field and to increase relevant statistical science activity.

Contents:

STATISTICAL ANALYSIS OF MASSIVE DATA STREAMS

Copyright

ACKNOWLEDGEMENT OF REVIEWERS

Preface and Workshop Rationale

Sallie Keller-McNulty Welcome and Overview of Sessions

TRANSCRIPT OF PRESENTATION

James Schatz Welcome and Overview of Sessions

Douglas Nychka, Chair of Session on Atmospheric and Meteorological Data Introduction by Session Chair

John Bates Exploratory Climate Analysis Tools for Environmental Satellite and Weather Radar Data

ABSTRACT OF PRESENTATION

1. Introduction

2. Philosophy of the use of remote sensing data for climate monitoring

Amy Braverman Statistical Challenges in the Production and Analysis of Remote Sensing Earth Science Data at the Jet…

Ralph Milliff Global and Regional Surface Wind Field Inferences from Spaceborne Scatterometer Data

Global and Regional Surface Wind Field Inferences Given Spaceborne Scatterometer Data Ralph F.Milliff

GLOBAL AND REGIONAL SURFACE WIND FIELD INFERENCES FROM SPACE-BORNE SCATTEROMETER DATA

Blending QSCAT and Weather-Center Analysis Winds

Bayesian Inference for Surface Winds in the Labrador Sea

Bayesian Hierarchical Model for Surface Winds in the Tropics

A Bayesian Hierarchical Air-Sea Interaction Model

References

Figure Captions

Summary

Report from Breakout Group

David Scott, Chair of Session on High-Energy Physics Introduction by Session Chair

Robert Jacobsen Statistical Analysis of High Energy Physics Data

Paul Padley Some Challenges in Experimental Particle Physics Data Streams

TRANSCRIPT OF PRESENTATION.

Miron Livny Data Grids (or, A Distributed Computing View of High Energy Physics)

Daryl Pregibon Keynote Address: Graph Mining-Discovery in Large Networks

Sallie Keller-McNulty, Chair of Session on Integrated Data Systems Introduction by Session Chair

J.Douglas Beason Global Situational Awareness

Kevin Vixie Incorporating Invariants in Mahalanobis Distance-Based Classifiers: Applications to Face Recognition

INCORPORATING INVARIANTS IN MAHALANOBIS DISTANCE BASED CLASSIFIERS: APPLICATION TO FACE RECOGNITION

I. INTRODUCTION

II. COMBINING WITHIN CLASS COVARIANCES AND LINEAR APPROXIMATIONS TO INVARIANCES

III. FACE RECOGNITION RESULTS

IV. CONCLUSIONS

V. ACKNOWLEDGMENT

REFERENCES

John Elder Ensembles of Models: Simplicity (of Function) Through Complexity (of Form)

Mark Hansen Untitled Presentation

Wendy Martinez, Chair of Session on Network Traffic Introduction by Session Chair

William Cleveland FSD Models for Open-Loop Generation of Internet Packet Traffic

Johannes Gehrke Processing Aggregate Queries over Continuous Data Streams

Edward Wegman Visualization of Internet Packet Headers

Paul Whitney Toward the Routine Analysis of Moderate to Large-Size Data

Leland Wilkinson, Chair of Session on Mining Commercial Streams of Data Introduction by Session Chair

Lee Rhodes A Stream Processor for Extracting Usage Intelligence from High-Momentum Internet Data

A STREAM PROCESSOR FOR EXTRACTING USAGE INTELLIGENCE FROM HIGH-MOMENTUM INTERNET DATA

1. INTRODUCTION

2. BUSINESS CHALLENGES FOR THE NSPs

3. SOURCES AND TYPES OF DATA

3.1 USAGE MEs

3.2 SESSION MEs

3.3 REFERENCE DATA

4. DATA STREAMS AND RIVERS

5. IUM HIGH-LEVEL ARCHITECTURE

6. STREAM COLLECTION AND NORMALIZATION

7. STREAM RULE PROCESSING

8. RULE CHAINS AND ASSOCIATED DATA STRUCTURES

9. STATISTICS FROM STREAMS

9.1 CAPTURE MODELS

9.2 CAPTURE MODEL AGGREGATION

9.3 DRILL FORWARD

9.4 USER INTERACTION WITH STREAMING MODELS

10. SUMMARY

ACKNOWLEDGMENTS

Pedro Domingos A General Framework for Mining Massive Data Streams

A GENERAL FRAMEWORK FOR MINING MASSIVE DATA STREAMS

Abstract

1 The Problem

2 The Framework

3 Time-Changing Data

4 Conclusion

Reference

Andrew Moore kd- R- Ball- and Ad- Trees: Scalable Massive Science Data Analysis

Concluding Comments.

Notes:

Bibliographic Level Mode of Issuance: Monograph

ISBN:

1-280-20873-2

9786610208739

0-309-59302-6

OCLC:

567850849