Data Streams

Models and Algorithms

By: Charu C. Aggarwal (Editor)

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Hardcover | 27 November 2006

At a Glance

Hardcover
380 Pages

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23.5 x 15.88 x 2.54

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Data Streams: Models and Algorithms primarily discusses issues related to the mining aspects of data streams. Recent progress in hardware technology makes it possible for organizations to store and record large streams of transactional data. For example, even simple daily transactions such as using the credit card or phone result in automated data storage, which brings us to a fairly new topic called data streams.

This volume covers mining aspects of data streams comprehensively: each contributed chapter contains a survey on the topic, the key ideas in the field for that particular topic, and future research directions.

Data Streams: Models and Algorithms is intended for a professional audience composed of researchers and practitioners in industry. This book is also appropriate for advanced-level students in computer science.

Industry Reviews

From the reviews:

"This book is the very first attempt to record the challenges and present the solutions currently adopted to deal with the data streams. ... All chapters are written by prominent researchers in the field ... which makes the material in the book invaluable. This book is mainly intended for researchers, graduate students, and developers in industry. ... This book will be very useful for researchers or practitioners in the field of data streams, despite the fast growth of this field. Overall, we highly recommend it." (Yannis Manolopoulos and Maria Kontaki, Computing Reviews, January, 2008)

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You Can Find This Book In

Non-Fiction Mathematics Mathematical Foundation Mathematical Logic Algebra Discrete Mathematics Computing & I.T.Computer Programming & Software Development Algorithms & Data Structures Databases Data Capture & Analysis Computer Networking & Communications

Computer Science Data Mining

List of Figures	p. xi
List of Tables	p. xv
Preface	p. xvii
An Introduction to Data Streams	p. 1
Introduction	p. 1
Stream Mining Algorithms	p. 2
Conclusions and Summary	p. 6
References	p. 7
On Clustering Massive Data Streams: A Summarization Paradigm	p. 9
Introduction	p. 10
The Micro-clustering Based Stream Mining Framework	p. 12
Clustering Evolving Data Streams: A Micro-clustering Approach	p. 17
Micro-clustering Challenges	p. 18
Online Micro-cluster Maintenance: The CluStream Algorithm	p. 19
High Dimensional Projected Stream Clustering	p. 22
Classification of Data Streams: A Micro-clustering Approach	p. 23
On-Demand Stream Classification	p. 24
Other Applications of Micro-clustering and Research Directions	p. 26
Performance Study and Experimental Results	p. 27
Discussion	p. 36
References	p. 36
A Survey of Classification Methods in Data Streams	p. 39
Introduction	p. 39
Research Issues	p. 41
Solution Approaches	p. 43
Classification Techniques	p. 44
Ensemble Based Classification	p. 45
Very Fast Decision Trees (VFDT)	p. 46
On Demand Classification	p. 48
Online Information Network (OLIN)	p. 48
LWClass Algorithm	p. 49
ANNCAD Algorithm	p. 51
SCALLOP Algorithm	p. 51
Summary	p. 52
References	p. 53
Frequent Pattern Mining in Data Streams	p. 61
Introduction	p. 61
Overview	p. 62
New Algorithm	p. 67
Work on Other Related Problems	p. 79
Conclusions and Future Directions	p. 80
References	p. 81
A Survey of Change Diagnosis Algorithms in Evolving Data Streams	p. 85
Introduction	p. 86
The Velocity Density Method	p. 88
Spatial Velocity Profiles	p. 93
Evolution Computations in High Dimensional Case	p. 95
On the use of clustering for characterizing stream evolution	p. 96
On the Effect of Evolution in Data Mining Algorithms	p. 97
Conclusions	p. 100
References	p. 101
Multi-Dimensional Analysis of Data Streams Using Stream Cubes	p. 103
Introduction	p. 104
Problem Definition	p. 106
Architecture for On-line Analysis of Data Streams	p. 108
Tilted time frame	p. 108
Critical layers	p. 110
Partial materialization of stream cube	p. 111
Stream Data Cube Computation	p. 112
Algorithms for cube computation	p. 115
Performance Study	p. 117
Related Work	p. 120
Possible Extensions	p. 121
Conclusions	p. 122
References	p. 123
Load Shedding in Data Stream Systems	p. 127
Load Shedding for Aggregation Queries	p. 128
Problem Formulation	p. 129
Load Shedding Algorithm	p. 133
Extensions	p. 141
Load Shedding in Aurora	p. 142
Load Shedding for Sliding Window Joins	p. 144
Load Shedding for Classification Queries	p. 145
Summary	p. 146
References	p. 146
The Sliding-Window Computation Model and Results	p. 149
Motivation and Road Map	p. 150
A Solution to the BasicCounting Problem	p. 152
The Approximation Scheme	p. 154
Space Lower Bound for BasicCounting Problem	p. 157
Beyond 0's and 1's	p. 158
References and Related Work	p. 163
Conclusion	p. 164
References	p. 166
A Survey of Synopsis Construction in Data Streams	p. 169
Introduction	p. 169
Sampling Methods	p. 172
Random Sampling with a Reservoir	p. 174
Concise Sampling	p. 176
Wavelets	p. 177
Recent Research on Wavelet Decomposition in Data Streams	p. 182
Sketches	p. 184
Fixed Window Sketches for Massive Time Series	p. 185
Variable Window Sketches of Massive Time Series	p. 185
Sketches and their applications in Data Streams	p. 186
Sketches with p-stable distributions	p. 190
The Count-Min Sketch	p. 191
Related Counting Methods: Hash Functions for Determining Distinct Elements	p. 193
Advantages and Limitations of Sketch Based Methods	p. 194
Histograms	p. 196
One Pass Construction of Equi-depth Histograms	p. 198
Constructing V-Optimal Histograms	p. 198
Wavelet Based Histograms for Query Answering	p. 199
Sketch Based Methods for Multi-dimensional Histograms	p. 200
Discussion and Challenges	p. 200
References	p. 202
A Survey of Join Processing in Data Streams	p. 209
Introduction	p. 209
Model and Semantics	p. 210
State Management for Stream Joins	p. 213
Exploiting Constraints	p. 214
Exploiting Statistical Properties	p. 216
Fundamental Algorithms for Stream Join Processing	p. 225
Optimizing Stream Joins	p. 227
Conclusion	p. 230
Acknowledgments	p. 232
References	p. 232
Indexing and Querying Data Streams	p. 237
Introduction	p. 238
Indexing Streams	p. 239
Preliminaries and definitions	p. 239
Feature extraction	p. 240
Index maintenance	p. 244
Discrete Wavelet Transform	p. 246
Querying Streams	p. 248
Monitoring an aggregate query	p. 248
Monitoring a pattern query	p. 251
Monitoring a correlation query	p. 252
Related Work	p. 254
Future Directions	p. 255
Distributed monitoring systems	p. 255
Probabilistic modeling of sensor networks	p. 256
Content distribution networks	p. 256
Chapter Summary	p. 257
References	p. 257
Dimensionality Reduction and Forecasting on Streams	p. 261
Related work	p. 264
Principal component analysis (PCA)	p. 265
Auto-regressive models and recursive least squares	p. 267
Muscles	p. 269
Tracking correlations and hidden variables: Spirit	p. 271
Putting Spirit to work	p. 276
Experimental case studies	p. 278
Performance and accuracy	p. 283
Conclusion	p. 286
Acknowledgments	p. 286
References	p. 287
A Survey of Distributed Mining of Data Streams	p. 289
Introduction	p. 289
Outlier and Anomaly Detection	p. 291
Clustering	p. 295
Frequent itemset mining	p. 296
Classification	p. 297
Summarization	p. 298
Mining Distributed Data Streams in Resource Constrained Environments	p. 299
Systems Support	p. 300
References	p. 304
Algorithms for Distributed Data Stream Mining	p. 309
Introduction	p. 310
Motivation: Why Distributed Data Stream Mining?	p. 311
Existing Distributed Data Stream Mining Algorithms	p. 312
A local algorithm for distributed data stream mining	p. 315
Local Algorithms: definition	p. 315
Algorithm details	p. 316
Experimental results	p. 318
Modifications and extensions	p. 320
Bayesian Network Learning from Distributed Data Streams	p. 321
Distributed Bayesian Network Learning Algorithm	p. 322
Selection of samples for transmission to global site	p. 323
Online Distributed Bayesian Network Learning	p. 324
Experimental Results	p. 326
Conclusion	p. 326
References	p. 329
A Survey of Stream Processing Problems and Techniques in Sensor Networks	p. 333
Challenges	p. 334
The Data Collection Model	p. 335
Data Communication	p. 335
Query Processing	p. 337
Aggregate Queries	p. 338
Join Queries	p. 340
Top-k Monitoring	p. 341
Continuous Queries	p. 341
Compression and Modeling	p. 342
Data Distribution Modeling	p. 343
Outlier Detection	p. 344
Application: Tracking of Objects using Sensor Networks	p. 345
Summary	p. 347
References	p. 348
Index	p. 353
Table of Contents provided by Ingram. All Rights Reserved.

Data Streams

Models and Algorithms

At a Glance

Hardcover

Industry Reviews

Shipping

How to return your order

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