Data streams: models and algorithms
Saved in:
| Format: | Book |
|---|---|
| Language: | English |
| Published: |
New York, NY
Springer
2007
|
| Series: | Advances in database systems
31 |
| Subjects: | |
| Online Access: | Inhaltstext Inhaltsverzeichnis |
| Physical Description: | XVIII, 354 S. graph. Darst. |
| ISBN: | 0387287590 9780387287591 9780387475349 0387475346 |
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| 245 | 1 | 0 | |a Data streams |b models and algorithms |c ed. by Charu C. Aggarwal |
| 264 | 1 | |a New York, NY |b Springer |c 2007 | |
| 300 | |a XVIII, 354 S. |b graph. Darst. | ||
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| 650 | 4 | |a Datenstrom | |
| 650 | 4 | |a Informatik | |
| 650 | 4 | |a Mathematik | |
| 650 | 4 | |a Algorithms | |
| 650 | 4 | |a Computer science |x Mathematics | |
| 650 | 4 | |a Data mining | |
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| adam_text |
Charu
С.
Aggarwal
IBM
Thomas
J.
Watson Research Center
19
Skyline Drive
Hawthorne NY
10532
Library of Congress Control Number:
2006934111
DATA STREAMS: Models and Algorithms edited by Charu C. Aggarwal
ISBN-
10: 0-387-28759-0
ISBN-
13: 978-0-387-28759-1
e-ISBN-10:
0-387-47534-6
e-ISBN-13:
978-0-387-47534-9
Cover by Will Ladd, NRL Mapping, Charting and Geodesy Branch
utilizing NRL's GIDB® Portal System that can be utilized at
http^/dmap.nrlssc. navy.mil
Printed on acid-free paper.
© 2007
Springer Science+Business Media, LLC.
All rights reserved. This work may not be translated or copied in whole or
in part without the written permission of the publisher (Springer
Science+Business Media, LLC»
233
Spring Street, New York, NY
10013,
USA), except for brief excerpts in connection with reviews or scholarly
analysis. Use in connection with any form of information storage and
retrieval, electronic adaptation, computer software, or by similar or
dissimilar methodology now know or hereafter developed is forbidden.
The use in this publication of trade names, trademarks, service marks and
similar terms, even if the are not identified as such, is not to be taken as
an expression of opinion as to whether or not they are subject to
proprietary rights.
987654321
springer.com
Contents
List of
Figures
xi
Ust
of Tables
xv
Preface
xvii
1
An Introduction to Data Streams
1
Cham
С
Aggarwal
1.
Introduction
1
2.
Stream Mining Algorithms
2
3.
Conclusions and Summary
6
References
7
2
On Clustering Massive Data Streams: A Summarization Paradigm
9
Chant C. Aggarwal, Jiawei Han, Jianyong Wang and Philip S. Yu
1.
Introduction
10
2.
The Micro-clustering Based Stream Mining Framework
12
3.
Clustering Evolving Data Streams: A Micro-clustering Approach
17
3.
1 Micro-clustering Challenges
18
3.2
Online Micro-cluster Maintenance: The CluStream Algo¬
rithm
19
3.3
High Dimensional Projected Stream Clustering
22
4.
Classification of Data Streams: A Micro-clustering Approach
23
4.1
On-Demand Stream Classification
24
5.
Other Applications of Micro-clustering and Research Directions
26
6.
Performance Study and Experimental Results
27
7.
Discussion
36
References
36
3
A Survey of Classification Methods in Data Streams
39
Mohamed
Medhat
Gaber,
Arkady Zaslavsky and Shonali Krishnaswamy
1.
Introduction
39
2.
Research Issues
41
3.
Solution Approaches
43
4.
Classification Techniques
44
4.1
Ensemble Based Classification
45
4.2
Very Fast Decision Trees (VFDT)
46
vi DATA
STREAMS: MODELS AND ALGORITHMS
4.3
On Demand Classification
48
4.4
Online Information Network (OLIN)
48
4.5
LWClass Algorithm
49
4.6
ANNCAD Algorithm
51
4.7
SCALLOP Algorithm
51
5.
Summary
52
References
53
4
Frequent Pattern Mining in Data Streams
61
Ruoming Jin and Gagan Agrawal
1.
Introduction
61
2.
Overview
62
3.
New Algorithm
67
4.
Work on Other Related Problems
79
5.
Conclusions and Future Directions
80
References
81
5
A Survey of Change Diagnosis
85
Algorithms in Evolving Data
Streams
Cham C. Agganval
1.
Introduction
86
2.
The Velocity Density Method
88
2.1
Spatial Velocity Profiles
93
2.2
Evolution Computations in High Dimensional Case
95
2.3
On the use of clustering for characterizing stream evolution
96
3.
On the Effect of Evolution in Data Mining Algorithms
97
4.
Conclusions
100
References
101
6
Multi-Dimensional Analysis of Data
103
Streams Using Stream Cubes
Jiawei Han, Y.
Dora Cai,
Yixin Chen, Guozhu Dong,
Jian
Pei, Benjamin W. Wah, and
Jianyong Wang
1.
Introduction
104
2.
Problem Definition
106
3.
Architecture for On-line Analysis of Data Streams
108
3.1
Tilted time frame
108
3.2
Critical layers
110
3.3
Partial materialization of stream cube
111
4.
Stream Data Cube Computation
112
4.1
Algorithms for cube computation
115
5.
Performance Study
117
6.
Related Work
120
7.
Possible Extensions
121
8.
Conclusions
122
References
123
Contents
vu
7
Load Shedding in Data Stream Systems
127
Brian Babcock, Mayur
Datar
and Rajeev Motwani
1.
Load Shedding for Aggregation Queries
128
1.1
Problem Formulation
129
1.2
Load Shedding Algorithm
133
1.3
Extensions
141
2.
Load Shedding in Aurora
142
3.
Load Shedding for Sliding Window Joins
144
4.
Load Shedding for Classification Queries
145
5.
Summary
146
References
146
8
The Sliding-Window Computation Model and Results
149
Mayur
Datar
and Rajeev Motwani
0.1
Motivation and Road Map
150
1.
A Solution to the
В
ASICCOUNTING Problem
152
1.1
The Approximation Scheme
154
2.
Space Lower Bound for BasicCountING Problem
157
3.
Beyond 0's and l's
158
4.
References and Related Work
163
5.
Conclusion
164
References
166
9
A Survey of Synopsis Construction
169
in Data Streams
Cham
С
Aggarwal, Philip S. Yu
1.
Introduction
169
2.
Sampling Methods
172
2.1
Random Sampling with a Reservoir
174
2.2
Concise Sampling
176
3.
Wavelets
177
3.1
Recent Research on Wavelet Decomposition in Data Streams
182
4.
Sketches
184
4.1
Fixed Window Sketches for Massive Time Series
185
4.2
Variable Window Sketches of Massive Time Series
185
4.3
Sketches and their applications in Data Streams
186
4.4
Sketches with p-stable distributions
190
4.5
The Count-Min Sketch
191
4.6
Related Counting Methods: Hash Functions for Determining
Distinct Elements
193
4.7
Advantages and Limitations of Sketch Based Methods
194
5.
Histograms
196
5.1
One Pass Construction of Equi-depth Histograms
198
5.2
Constructing V-Optimal Histograms
198
5.3
Wavelet Based Histograms for Query Answering
199
5.4
Sketch Based Methods for Multi-dimensional Histograms
200
6.
Discussion and Challenges
200
viii
DATA STREAMS: MODELS AND ALGORITHMS
References
202
10
A Survey of Join Processing in
209
Data Streams
JunyiXie and
Jun
Yang
1. Introduction
209
2.
Model and Semantics
210
3.
State Management for Stream Joins
213
3.1
Exploiting Constraints
214
3.2
Exploiting Statistical Properties
216
4.
Fundamental Algorithms for Stream Join Processing
225
5.
Optimizing Stream Joins
227
6.
Conclusion
230
Acknowledgments
232
References
232
11
Indexing and Querying Data Streams
237
Ahmet Buint, Ambuj K. Singh
1.
Introduction
238
2.
Indexing Streams
239
2.1
Preliminaries and definitions
239
2.2
Feature extraction
240
2.3
Index maintenance
244
2.4
Discrete Wavelet Transform
246
3.
Querying Streams
248
3.1
Monitoring an aggregate query
248
3.2
Monitoring a pattern query
251
3.3
Monitoring a correlation query
252
4.
Related Work
254
5.
Future Directions
255
5.1
Distributed monitoring systems
255
5.2
Probabilistic modeling of sensor networks
256
5.3
Content distribution networks
256
6.
Chapter Summary
257
References
257
12
Dimensionality Reduction and
261
Forecasting on Streams
Spiros Papadimitriou, Jimeng Sun, and
Christos Faloutsos
1.
Related work
264
2.
Principal component analysis (PCA)
265
3.
Auto-regressive models and recursive least squares
267
4.
MUSCLES
269
5.
Tracking correlations and hidden variables: SPIRIT
271
6.
Putting SPIRIT to work
276
7.
Experimental case studies
278
Contents ix
8. Performance
and accuracy
283
9.
Conclusion
286
Acknowledgments
286
References
287
13
A Survey of Distributed Mining of Data Streams
289
Srinivasan Parthasarathy, Amol Ghoting and Matthew Eric Otey
1.
Introduction
289
2.
Outlier and Anomaly Detection
291
3.
Clustering
295
4.
Frequent itemset mining
296
5.
Classification
297
6.
Summarization
298
7.
Mining Distributed Data Streams in Resource Constrained Environ¬
ments
299
8.
Systems Support
300
References
304
14
Algorithms for Distributed
309
Data Stream Mining
Kanishka BhadurL
Kamalika
Das, Krishnamoorthy Sivakumar^ Hillol Kargupta, Ran
Wolffand
Rong Chen
1.
Introduction
310
2.
Motivation: Why Distributed Data Stream Mining?
311
3.
Existing Distributed Data Stream Mining Algorithms
312
4.
A local algorithm for distributed data stream mining
315
4.1
Local Algorithms
:
definition
315
4.2
Algorithm details
316
4.3
Experimental results
318
4.4
Modifications and extensions
320
5.
Bayesian Network Learning from Distributed Data Streams
321
5.1
Distributed Bayesian Network Learning Algorithm
322
5.2
Selection of samples for transmission to global site
323
5.3
Online Distributed Bayesian Network Learning
324
5.4
Experimental Results
326
6.
Conclusion
326
References
329
15
A Survey of Stream Processing
333
Problems and Techniques
in Sensor Networks
Sharmila Subramaniam, Dimitrios Gunopulos
1.
Challenges
334
x
DATA STREAMS: MODELS AND ALGORITHMS
2.
The Data Collection Model
335
3.
Data Communication
335
4.
Query Processing
337
4.1
Aggregate Queries
338
4.2
Join Queries
340
4.3
Top-fc Monitoring
341
4.4
Continuous Queries
341
5.
Compression and Modeling
342
5.1
Data Distribution Modeling
343
5.2
Outlier Detection
344
6.
Application: Tracking of Objects using Sensor Networks
345
7.
Summary
347
References
348
Index
353 |
| adam_txt |
Charu
С.
Aggarwal
IBM
Thomas
J.
Watson Research Center
19
Skyline Drive
Hawthorne NY
10532
Library of Congress Control Number:
2006934111
DATA STREAMS: Models and Algorithms edited by Charu C. Aggarwal
ISBN-
10: 0-387-28759-0
ISBN-
13: 978-0-387-28759-1
e-ISBN-10:
0-387-47534-6
e-ISBN-13:
978-0-387-47534-9
Cover by Will Ladd, NRL Mapping, Charting and Geodesy Branch
utilizing NRL's GIDB® Portal System that can be utilized at
http^/dmap.nrlssc. navy.mil
Printed on acid-free paper.
© 2007
Springer Science+Business Media, LLC.
All rights reserved. This work may not be translated or copied in whole or
in part without the written permission of the publisher (Springer
Science+Business Media, LLC»
233
Spring Street, New York, NY
10013,
USA), except for brief excerpts in connection with reviews or scholarly
analysis. Use in connection with any form of information storage and
retrieval, electronic adaptation, computer software, or by similar or
dissimilar methodology now know or hereafter developed is forbidden.
The use in this publication of trade names, trademarks, service marks and
similar terms, even if the are not identified as such, is not to be taken as
an expression of opinion as to whether or not they are subject to
proprietary rights.
987654321
springer.com
Contents
List of
Figures
xi
Ust
of Tables
xv
Preface
xvii
1
An Introduction to Data Streams
1
Cham
С
Aggarwal
1.
Introduction
1
2.
Stream Mining Algorithms
2
3.
Conclusions and Summary
6
References
7
2
On Clustering Massive Data Streams: A Summarization Paradigm
9
Chant C. Aggarwal, Jiawei Han, Jianyong Wang and Philip S. Yu
1.
Introduction
10
2.
The Micro-clustering Based Stream Mining Framework
12
3.
Clustering Evolving Data Streams: A Micro-clustering Approach
17
3.
1 Micro-clustering Challenges
18
3.2
Online Micro-cluster Maintenance: The CluStream Algo¬
rithm
19
3.3
High Dimensional Projected Stream Clustering
22
4.
Classification of Data Streams: A Micro-clustering Approach
23
4.1
On-Demand Stream Classification
24
5.
Other Applications of Micro-clustering and Research Directions
26
6.
Performance Study and Experimental Results
27
7.
Discussion
36
References
36
3
A Survey of Classification Methods in Data Streams
39
Mohamed
Medhat
Gaber,
Arkady Zaslavsky and Shonali Krishnaswamy
1.
Introduction
39
2.
Research Issues
41
3.
Solution Approaches
43
4.
Classification Techniques
44
4.1
Ensemble Based Classification
45
4.2
Very Fast Decision Trees (VFDT)
46
vi DATA
STREAMS: MODELS AND ALGORITHMS
4.3
On Demand Classification
48
4.4
Online Information Network (OLIN)
48
4.5
LWClass Algorithm
49
4.6
ANNCAD Algorithm
51
4.7
SCALLOP Algorithm
51
5.
Summary
52
References
53
4
Frequent Pattern Mining in Data Streams
61
Ruoming Jin and Gagan Agrawal
1.
Introduction
61
2.
Overview
62
3.
New Algorithm
67
4.
Work on Other Related Problems
79
5.
Conclusions and Future Directions
80
References
81
5
A Survey of Change Diagnosis
85
Algorithms in Evolving Data
Streams
Cham C. Agganval
1.
Introduction
86
2.
The Velocity Density Method
88
2.1
Spatial Velocity Profiles
93
2.2
Evolution Computations in High Dimensional Case
95
2.3
On the use of clustering for characterizing stream evolution
96
3.
On the Effect of Evolution in Data Mining Algorithms
97
4.
Conclusions
100
References
101
6
Multi-Dimensional Analysis of Data
103
Streams Using Stream Cubes
Jiawei Han, Y.
Dora Cai,
Yixin Chen, Guozhu Dong,
Jian
Pei, Benjamin W. Wah, and
Jianyong Wang
1.
Introduction
104
2.
Problem Definition
106
3.
Architecture for On-line Analysis of Data Streams
108
3.1
Tilted time frame
108
3.2
Critical layers
110
3.3
Partial materialization of stream cube
111
4.
Stream Data Cube Computation
112
4.1
Algorithms for cube computation
115
5.
Performance Study
117
6.
Related Work
120
7.
Possible Extensions
121
8.
Conclusions
122
References
123
Contents
vu
7
Load Shedding in Data Stream Systems
127
Brian Babcock, Mayur
Datar
and Rajeev Motwani
1.
Load Shedding for Aggregation Queries
128
1.1
Problem Formulation
129
1.2
Load Shedding Algorithm
133
1.3
Extensions
141
2.
Load Shedding in Aurora
142
3.
Load Shedding for Sliding Window Joins
144
4.
Load Shedding for Classification Queries
145
5.
Summary
146
References
146
8
The Sliding-Window Computation Model and Results
149
Mayur
Datar
and Rajeev Motwani
0.1
Motivation and Road Map
150
1.
A Solution to the
В
ASICCOUNTING Problem
152
1.1
The Approximation Scheme
154
2.
Space Lower Bound for BasicCountING Problem
157
3.
Beyond 0's and l's
158
4.
References and Related Work
163
5.
Conclusion
164
References
166
9
A Survey of Synopsis Construction
169
in Data Streams
Cham
С
Aggarwal, Philip S. Yu
1.
Introduction
169
2.
Sampling Methods
172
2.1
Random Sampling with a Reservoir
174
2.2
Concise Sampling
176
3.
Wavelets
177
3.1
Recent Research on Wavelet Decomposition in Data Streams
182
4.
Sketches
184
4.1
Fixed Window Sketches for Massive Time Series
185
4.2
Variable Window Sketches of Massive Time Series
185
4.3
Sketches and their applications in Data Streams
186
4.4
Sketches with p-stable distributions
190
4.5
The Count-Min Sketch
191
4.6
Related Counting Methods: Hash Functions for Determining
Distinct Elements
193
4.7
Advantages and Limitations of Sketch Based Methods
194
5.
Histograms
196
5.1
One Pass Construction of Equi-depth Histograms
198
5.2
Constructing V-Optimal Histograms
198
5.3
Wavelet Based Histograms for Query Answering
199
5.4
Sketch Based Methods for Multi-dimensional Histograms
200
6.
Discussion and Challenges
200
viii
DATA STREAMS: MODELS AND ALGORITHMS
References
202
10
A Survey of Join Processing in
209
Data Streams
JunyiXie and
Jun
Yang
1. Introduction
209
2.
Model and Semantics
210
3.
State Management for Stream Joins
213
3.1
Exploiting Constraints
214
3.2
Exploiting Statistical Properties
216
4.
Fundamental Algorithms for Stream Join Processing
225
5.
Optimizing Stream Joins
227
6.
Conclusion
230
Acknowledgments
232
References
232
11
Indexing and Querying Data Streams
237
Ahmet Buint, Ambuj K. Singh
1.
Introduction
238
2.
Indexing Streams
239
2.1
Preliminaries and definitions
239
2.2
Feature extraction
240
2.3
Index maintenance
244
2.4
Discrete Wavelet Transform
246
3.
Querying Streams
248
3.1
Monitoring an aggregate query
248
3.2
Monitoring a pattern query
251
3.3
Monitoring a correlation query
252
4.
Related Work
254
5.
Future Directions
255
5.1
Distributed monitoring systems
255
5.2
Probabilistic modeling of sensor networks
256
5.3
Content distribution networks
256
6.
Chapter Summary
257
References
257
12
Dimensionality Reduction and
261
Forecasting on Streams
Spiros Papadimitriou, Jimeng Sun, and
Christos Faloutsos
1.
Related work
264
2.
Principal component analysis (PCA)
265
3.
Auto-regressive models and recursive least squares
267
4.
MUSCLES
269
5.
Tracking correlations and hidden variables: SPIRIT
271
6.
Putting SPIRIT to work
276
7.
Experimental case studies
278
Contents ix
8. Performance
and accuracy
283
9.
Conclusion
286
Acknowledgments
286
References
287
13
A Survey of Distributed Mining of Data Streams
289
Srinivasan Parthasarathy, Amol Ghoting and Matthew Eric Otey
1.
Introduction
289
2.
Outlier and Anomaly Detection
291
3.
Clustering
295
4.
Frequent itemset mining
296
5.
Classification
297
6.
Summarization
298
7.
Mining Distributed Data Streams in Resource Constrained Environ¬
ments
299
8.
Systems Support
300
References
304
14
Algorithms for Distributed
309
Data Stream Mining
Kanishka BhadurL
Kamalika
Das, Krishnamoorthy Sivakumar^ Hillol Kargupta, Ran
Wolffand
Rong Chen
1.
Introduction
310
2.
Motivation: Why Distributed Data Stream Mining?
311
3.
Existing Distributed Data Stream Mining Algorithms
312
4.
A local algorithm for distributed data stream mining
315
4.1
Local Algorithms
:
definition
315
4.2
Algorithm details
316
4.3
Experimental results
318
4.4
Modifications and extensions
320
5.
Bayesian Network Learning from Distributed Data Streams
321
5.1
Distributed Bayesian Network Learning Algorithm
322
5.2
Selection of samples for transmission to global site
323
5.3
Online Distributed Bayesian Network Learning
324
5.4
Experimental Results
326
6.
Conclusion
326
References
329
15
A Survey of Stream Processing
333
Problems and Techniques
in Sensor Networks
Sharmila Subramaniam, Dimitrios Gunopulos
1.
Challenges
334
x
DATA STREAMS: MODELS AND ALGORITHMS
2.
The Data Collection Model
335
3.
Data Communication
335
4.
Query Processing
337
4.1
Aggregate Queries
338
4.2
Join Queries
340
4.3
Top-fc Monitoring
341
4.4
Continuous Queries
341
5.
Compression and Modeling
342
5.1
Data Distribution Modeling
343
5.2
Outlier Detection
344
6.
Application: Tracking of Objects using Sensor Networks
345
7.
Summary
347
References
348
Index
353 |
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| index_date | 2024-07-02T16:51:58Z |
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| language | English |
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| subject_GND | (DE-588)4410055-3 |
| title | Data streams models and algorithms |
| title_auth | Data streams models and algorithms |
| title_exact_search | Data streams models and algorithms |
| title_exact_search_txtP | Data streams models and algorithms |
| title_full | Data streams models and algorithms ed. by Charu C. Aggarwal |
| title_fullStr | Data streams models and algorithms ed. by Charu C. Aggarwal |
| title_full_unstemmed | Data streams models and algorithms ed. by Charu C. Aggarwal |
| title_short | Data streams |
| title_sort | data streams models and algorithms |
| title_sub | models and algorithms |
| topic | Datenstrom Informatik Mathematik Algorithms Computer science Mathematics Data mining Datenstrom (DE-588)4410055-3 gnd |
| topic_facet | Datenstrom Informatik Mathematik Algorithms Computer science Mathematics Data mining |
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