High performance discovery in time series: techniques and case studies
Saved in:
| Main Authors: | , |
|---|---|
| Format: | Book |
| Language: | English |
| Published: |
New York u.a.
Springer
2004
|
| Series: | Monographs in computer science
|
| Subjects: | |
| Online Access: | Inhaltsverzeichnis |
| Item Description: | Literaturverz. S. 181 - 187 |
| Physical Description: | XIII, 190 S. Ill., graph. Darst. |
| ISBN: | 0387008578 9780387008578 |
Staff View
MARC
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Record in the Search Index
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|---|---|
| adam_text | Contents
Preface
.........................................................
vii
Part I Review of Techniques
1
Time Series Preliminaries
....................................... 3
1.1
High Performance Time Series Analysis
.......................... 5
2
Data Reduction and Transformation Techniques
.................... 9
2.1
Fourier Transform
............................................ 10
2.1.1
Orthogonal Function Families
........................... 11
2.1.2
Fourier Series
........................................ 13
2.1.3
Fourier Transform
.................................... 15
2.1.4
Discrete Fourier Transform
............................. 16
2.1.5
Fast Fourier Transform
................................ 25
2.1.6
Discrete Fourier Transform
-
The Bottom Line
............ 26
2.2
Wavelet Transform
............................................ 29
2.2.1
From Fourier Analysis to Wavelet Analysis
............... 29
2.2.2 Haar
Wavelet
......................................... 32
2.2.3
Multiresolution Analysis
............................... 34
2.2.4
Wavelet Transform
.................................... 37
2.2.5
Discrete Wavelet Transform
............................ 41
2.2.6
Wavelets
-
The Bottom Line
............................ 46
2.3
Singular Value Decomposition
.................................. 52
2.3.1
Existence and Uniqueness of Singular Value Decomposition
. 52
2.3.2
Optimali
ty
of Singular Value Decomposition
.............. 56
2.3.3
Data Reduction in Singular Value Decomposition
.......... 57
2.3.4
Singular Value Decomposition
-
The Bottom Line
......... 58
2.4
Sketches
..................................................... 61
2.4.1
Euclidean Distance
.................................... 62
2.4.2
L
p Distance
.......................................... 65
2.4.3
Sketches
-
The Bottom Line
............................ 67
2.5
Comparison of Data Reduction Techniques
....................... 67
2.6
Questions
.................................................... 69
xii Contents
3
Indexing Methods
............................................. 73
3.1
B-tree
....................................................... 73
3.2
KD-B-tree
................................................... 76
3.3
R-tree
....................................................... 78
3.4
Grid Structure
................................................ 81
3.5
Indexes
-
The Bottom Line
..................................... 85
3.6
Questions
.................................................... 85
4
Flexible Similarity Search
....................................... 87
4.1
GEMINI Framework
.......................................... 88
4.2
Shifting and Scaling
........................................... 90
4.3
Time Scaling
................................................. 94
4.4
Local Dynamic Time Warping
.................................. 96
4.5
Questions
.................................................... 100
Partii CaseStudies
5
StatStream
.......................................,........... 103
5.1
Introduction
.................................................. 103
5.2
Data And Queries
............................................. 105
5.2.1
Time Series Data Streams
.............................. 105
5.2.2
Temporal Spans
...................................... 105
5.2.3
Statistics to Monitor
................................... 106
5.3
Statistics Over Sliding Windows
................................ 106
5.3.1
Single Stream Statistics
................................ 107
5.3.2
Correlation Statistics
.................................. 108
5.3.3
Inner Product with Aligned Windows
.................... 108
5.3.4
Inner Product with Unaligned Windows
.................. 110
5.3.5
IO
Performance
....................................... 112
5.3.6
Monitoring Correlations between Data Streams
............ 112
5.3.7
Parallel Implementation
................................ 116
5.4
Software Architecture of the StatStream System
................... 117
5.5
Empirical Study
.............................................. 118
5.5.1
Speed Measurement
................................... 119
5.5.2
Measuring Precision
................................... 120
5.6
Related Work
................................................ 123
5.7
Conclusion
.................................................. 125
5.8
Questions
.................................................... 126
6
Query by Humming
............................................ 127
6.1
Introduction
.................................................. 127
6.2
Related Work
................................................ 128
6.2.1
Insights from HumFinder
.............................. 130
6.3
Query by Humming System
.................................... 130
Contents xiii
6.3.1 User
Humming: The Input Hum-query
................... 130
6.3.2
A Database of Music
.................................. 131
6.3.3
Indexing Databases for Efficient Hum Query Retrieval
...... 133
6.4
Indexing Scheme for Dynamic Time Warping
..................... 134
6.5
Software Architecture of the HumFinder System
................... 139
6.6
Experiments
................................................. 140
6.6.1
Quality of the Query by Humming System
................ 141
6.6.2
Experiments on the DTW Index
......................... 142
6.6.3
Scalability Testing
.................................... 144
6.7
Conclusions
.................................................. 148
6.8
Questions
.................................................... 150
7
Elastic Burst Detection
......................................... 151
7.1
Introduction
.................................................. 151
7.1.1
Problem Statement
.................................... 152
7.1.2
Insights of Omniburst
.................................. 153
7.2
Data Structure and Algorithm
................................... 154
7.2.1
Wavelet Data Structure
................................ 154
7.2.2
Shifted Binary Tree
................................... 155
7.2.3
Streaming Algorithm
.................................. 159
7.2.4
Other Aggregates
..................................... 161
7.2.5
Extension to Two Dimensions
........................... 161
7.3
Software Architecture of the OmniBurst System
................... 162
7.4
Empirical Results of the OmniBurst System
....................... 163
7.4.1
Effectiveness Study
................................... 163
7.4.2
Performance Study
.................................... 164
7.5
Related work
................................................. 168
7.6
Conclusions and Future Work
................................... 172
7.7
Questions
.................................................... 172
8
A Call to Exploration
..........................................175
A Answers to the Questions
....................................... 177
A.2 Chapter
2.................................................... 177
A.3 Chapter
3.................................................... 178
A.4 Chapter
4.................................................... 178
A.5 Chapter
5.................................................... 179
A.6 Chapter
6.................................................... 180
A.7 Chapter
7.................................................... 180
References
...................................................... 181
Index
........................................................... 189
|
| adam_txt |
Contents
Preface
.
vii
Part I Review of Techniques
1
Time Series Preliminaries
. 3
1.1
High Performance Time Series Analysis
. 5
2
Data Reduction and Transformation Techniques
. 9
2.1
Fourier Transform
. 10
2.1.1
Orthogonal Function Families
. 11
2.1.2
Fourier Series
. 13
2.1.3
Fourier Transform
. 15
2.1.4
Discrete Fourier Transform
. 16
2.1.5
Fast Fourier Transform
. 25
2.1.6
Discrete Fourier Transform
-
The Bottom Line
. 26
2.2
Wavelet Transform
. 29
2.2.1
From Fourier Analysis to Wavelet Analysis
. 29
2.2.2 Haar
Wavelet
. 32
2.2.3
Multiresolution Analysis
. 34
2.2.4
Wavelet Transform
. 37
2.2.5
Discrete Wavelet Transform
. 41
2.2.6
Wavelets
-
The Bottom Line
. 46
2.3
Singular Value Decomposition
. 52
2.3.1
Existence and Uniqueness of Singular Value Decomposition
. 52
2.3.2
Optimali
ty
of Singular Value Decomposition
. 56
2.3.3
Data Reduction in Singular Value Decomposition
. 57
2.3.4
Singular Value Decomposition
-
The Bottom Line
. 58
2.4
Sketches
. 61
2.4.1
Euclidean Distance
. 62
2.4.2
L
p Distance
. 65
2.4.3
Sketches
-
The Bottom Line
. 67
2.5
Comparison of Data Reduction Techniques
. 67
2.6
Questions
. 69
xii Contents
3
Indexing Methods
. 73
3.1
B-tree
. 73
3.2
KD-B-tree
. 76
3.3
R-tree
. 78
3.4
Grid Structure
. 81
3.5
Indexes
-
The Bottom Line
. 85
3.6
Questions
. 85
4
Flexible Similarity Search
. 87
4.1
GEMINI Framework
. 88
4.2
Shifting and Scaling
. 90
4.3
Time Scaling
. 94
4.4
Local Dynamic Time Warping
. 96
4.5
Questions
. 100
Partii CaseStudies
5
StatStream
.,. 103
5.1
Introduction
. 103
5.2
Data And Queries
. 105
5.2.1
Time Series Data Streams
. 105
5.2.2
Temporal Spans
. 105
5.2.3
Statistics to Monitor
. 106
5.3
Statistics Over Sliding Windows
. 106
5.3.1
Single Stream Statistics
. 107
5.3.2
Correlation Statistics
. 108
5.3.3
Inner Product with Aligned Windows
. 108
5.3.4
Inner Product with Unaligned Windows
. 110
5.3.5
IO
Performance
. 112
5.3.6
Monitoring Correlations between Data Streams
. 112
5.3.7
Parallel Implementation
. 116
5.4
Software Architecture of the StatStream System
. 117
5.5
Empirical Study
. 118
5.5.1
Speed Measurement
. 119
5.5.2
Measuring Precision
. 120
5.6
Related Work
. 123
5.7
Conclusion
. 125
5.8
Questions
. 126
6
Query by Humming
. 127
6.1
Introduction
. 127
6.2
Related Work
. 128
6.2.1
Insights from HumFinder
. 130
6.3
Query by Humming System
. 130
Contents xiii
6.3.1 User
Humming: The Input Hum-query
. 130
6.3.2
A Database of Music
. 131
6.3.3
Indexing Databases for Efficient Hum Query Retrieval
. 133
6.4
Indexing Scheme for Dynamic Time Warping
. 134
6.5
Software Architecture of the HumFinder System
. 139
6.6
Experiments
. 140
6.6.1
Quality of the Query by Humming System
. 141
6.6.2
Experiments on the DTW Index
. 142
6.6.3
Scalability Testing
. 144
6.7
Conclusions
. 148
6.8
Questions
. 150
7
Elastic Burst Detection
. 151
7.1
Introduction
. 151
7.1.1
Problem Statement
. 152
7.1.2
Insights of Omniburst
. 153
7.2
Data Structure and Algorithm
. 154
7.2.1
Wavelet Data Structure
. 154
7.2.2
Shifted Binary Tree
. 155
7.2.3
Streaming Algorithm
. 159
7.2.4
Other Aggregates
. 161
7.2.5
Extension to Two Dimensions
. 161
7.3
Software Architecture of the OmniBurst System
. 162
7.4
Empirical Results of the OmniBurst System
. 163
7.4.1
Effectiveness Study
. 163
7.4.2
Performance Study
. 164
7.5
Related work
. 168
7.6
Conclusions and Future Work
. 172
7.7
Questions
. 172
8
A Call to Exploration
.175
A Answers to the Questions
. 177
A.2 Chapter
2. 177
A.3 Chapter
3. 178
A.4 Chapter
4. 178
A.5 Chapter
5. 179
A.6 Chapter
6. 180
A.7 Chapter
7. 180
References
. 181
Index
. 189 |
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| id | DE-604.BV021710266 |
| illustrated | Illustrated |
| index_date | 2024-07-02T15:20:13Z |
| indexdate | 2024-07-09T20:42:13Z |
| institution | BVB |
| isbn | 0387008578 9780387008578 |
| language | English |
| oai_aleph_id | oai:aleph.bib-bvb.de:BVB01-014924098 |
| oclc_num | 56035818 |
| open_access_boolean | |
| owner | DE-384 DE-634 DE-188 |
| owner_facet | DE-384 DE-634 DE-188 |
| physical | XIII, 190 S. Ill., graph. Darst. |
| publishDate | 2004 |
| publishDateSearch | 2004 |
| publishDateSort | 2004 |
| publisher | Springer |
| record_format | marc |
| series2 | Monographs in computer science |
| spelling | Shasha, Dennis Elliott 1955- Verfasser (DE-588)123736048 aut High performance discovery in time series techniques and case studies Dennis Shasha ; Yunyue Zhu New York u.a. Springer 2004 XIII, 190 S. Ill., graph. Darst. txt rdacontent n rdamedia nc rdacarrier Monographs in computer science Literaturverz. S. 181 - 187 Tijdreeksen gtt Time series analysis Zeitreihenanalyse (DE-588)4067486-1 gnd rswk-swf Algorithmus (DE-588)4001183-5 gnd rswk-swf Zeitreihenanalyse (DE-588)4067486-1 s Algorithmus (DE-588)4001183-5 s DE-604 Zhu, Yunyue Verfasser aut Digitalisierung UB Augsburg application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=014924098&sequence=000002&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis |
| spellingShingle | Shasha, Dennis Elliott 1955- Zhu, Yunyue High performance discovery in time series techniques and case studies Tijdreeksen gtt Time series analysis Zeitreihenanalyse (DE-588)4067486-1 gnd Algorithmus (DE-588)4001183-5 gnd |
| subject_GND | (DE-588)4067486-1 (DE-588)4001183-5 |
| title | High performance discovery in time series techniques and case studies |
| title_auth | High performance discovery in time series techniques and case studies |
| title_exact_search | High performance discovery in time series techniques and case studies |
| title_exact_search_txtP | High performance discovery in time series techniques and case studies |
| title_full | High performance discovery in time series techniques and case studies Dennis Shasha ; Yunyue Zhu |
| title_fullStr | High performance discovery in time series techniques and case studies Dennis Shasha ; Yunyue Zhu |
| title_full_unstemmed | High performance discovery in time series techniques and case studies Dennis Shasha ; Yunyue Zhu |
| title_short | High performance discovery in time series |
| title_sort | high performance discovery in time series techniques and case studies |
| title_sub | techniques and case studies |
| topic | Tijdreeksen gtt Time series analysis Zeitreihenanalyse (DE-588)4067486-1 gnd Algorithmus (DE-588)4001183-5 gnd |
| topic_facet | Tijdreeksen Time series analysis Zeitreihenanalyse Algorithmus |
| url | http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=014924098&sequence=000002&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA |
| work_keys_str_mv | AT shashadenniselliott highperformancediscoveryintimeseriestechniquesandcasestudies AT zhuyunyue highperformancediscoveryintimeseriestechniquesandcasestudies |