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$uWaterloo LaTeX Thesis Template - UWSpace - University of Waterloo$

Acknowledgments I would like to thank all people who have helped and inspired me during my Ph.D study. It is difficult to overstate my appreciation to my supervisor, Dr. M. Tamer Özsu, who shared with me a lot of his expertise and research insight. His wide area of knowledge, thoughtful advises, understanding and encouraging have laid a good basis for this thesis. I thank him for all the support during my Ph.D study. I am heartily thankful to Dr. Wayne Oldford for his guidance and support during the year of my thesis revision. His insights in statistical analysis and testing is second to none. He enabled me to develop an deeper understanding on the subject. I am grateful Dr. Grant Weddell, Dr. Ajit Singh, and Dr. Raymond Ng for their willingness to serve my committee. I also like to warmly thank Calisto Zuzarte and IBM Toronto Software Lab. for their support. I would like to express my deep gratitude to Dr. Qiang Zhu, my former supervisor during my Master study, for bringing me into the world of research and guiding me through many difficulties. I owe my sincere thanks to everyone whom I have shared experiences in life. This is a long and never ending list so I won’t be able to put all the names down. Lastly, and most importantly, I wish to thank my parents, Tao Jiang and Song Heng. They bore me, raised me, taught me, and loved me. To them I dedicate this thesis. iv
Contents List of Figures ix List of Tables xviii List of Symbols xxi 1 Introduction 1 1.1 Data stream environment . . . . . . . . . . . . . . . . . . 1 1.1.1 Data streams . . . . . . . . . . . . . . . . . . . . . 1 1.1.2 Data stream management systems . . . . . . . . . . 2 1.1.3 Brief history of data streams . . . . . . . . . . . . . 3 1.1.4 Distribution changes in data streams . . . . . . . . 5 1.2 Data stream mining . . . . . . . . . . . . . . . . . . . . . . 6 1.2.1 Models and issues in data mining . . . . . . . . . . 7 1.2.2 Mining data streams: New challenges . . . . . . . . 8 1.3 Motivation . . . . . . . . . . . . . . . . . . . . . . . . . . . 11 1.3.1 Mining data streams with distribution change . . . 12 1.3.2 Multi-dimensional streams . . . . . . . . . . . . . . 13 1.4 Scope and Contributions . . . . . . . . . . . . . . . . . . . 14 1.5 Thesis Outline . . . . . . . . . . . . . . . . . . . . . . . . . 17 v
Page 1 and 2: Mining Time-Changing Data Streams b
Page 3: Abstract Streaming data have gained
Page 7 and 8: 4 Change Detection in Multi-dimensi
Page 9 and 10: List of Figures 1.1 Abstract archit
Page 11 and 12: 3.30 Max duration for detecting tru
Page 13 and 14: 3.62 Max duration for detecting tru
Page 15 and 16: 3.96 Mean duration for detecting tr
Page 17 and 18: 3.129Number of true changes detecte
Page 19: List of Tables 3.1 Stream types gen
Page 22 and 23: I, Ii set of items for transactiona
Page 24 and 25: • Financial and market activities
Page 26 and 27: Figure 1.1: Abstract architecture o
Page 28 and 29: change detection and incremental ma
Page 30 and 31: preliminary analysis over the data
Page 32 and 33: streams is a continuous process and
Page 34 and 35: these approaches may achieve high p
Page 36 and 37: Under the assumption that attribute
Page 38 and 39: itemsets mining. A task-specific st
Page 41 and 42: Chapter 2 Background This chapter s
Page 43 and 44: • Due to performance and storage
Page 45 and 46: S t s i s i+1 s i s i+j W W W W’
Page 47 and 48: Figure 2.3: Example of continuous d
Page 49 and 50: samples so that it can be stored in
Page 51 and 52: Change detection As discussed in Se
Page 53 and 54: Frequency counting and association
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Integration of stream mining system
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Only a few distribution change-dete
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method is inclined to be conservati
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the distribution change cannot refl
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that the probability of a value tha
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|Wr| from Wr + Wt, such that: Discr
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To partition (Wr + Wt) into a numbe
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tings, an experiment framework is d
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Table 3.1: Stream types generated S
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By combining the three window movin
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• Standard deviation of durations
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Figure 3.3: Number of true changes
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0.00 0.02 0.04 0.06 0.08 0.10 0.12
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Figure 3.7: Number of false changes
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Figure 3.9: Mean duration for detec
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Figure 3.11: Mean duration for dete
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Figure 3.13: Standard deviation of
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0.0000 0.0004 0.0008 0.0012 0.00 0.
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0.000 0.002 0.004 0.006 0.000 0.005
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Figure 3.18: Number of true changes
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0.00 0.02 0.04 0.06 0.08 0.10 0.12
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Figure 3.22: Number of false change
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Figure 3.28: Standard deviation of
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0.0000 0.0004 0.0008 0.0012 0.00 0.
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0.000 0.005 0.010 0.015 0.020 0.000
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than fixed window method. Fixed win
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0.00 0.05 0.10 0.15 0.00 0.02 0.04
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0.00 0.05 0.10 0.15 0.00 0.02 0.04
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0.00 0.02 0.04 0.06 0.08 0.10 0.12
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0.000 0.010 0.020 0.030 0.000 0.004
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0.000 0.001 0.002 0.003 0.004 0.005
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0.000 0.002 0.004 0.006 0.008 0.000
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0.000 0.001 0.002 0.003 0.004 0e+00
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The following observations can be m
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technique with smaller τ setting o
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0.00 0.05 0.10 0.15 0.00 0.01 0.02
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0.00 0.05 0.10 0.15 0.20 0.25 0.30
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0.00 0.05 0.10 0.15 0.00 0.05 0.10
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0.00 0.02 0.04 0.06 0.08 0.10 0.12
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0.00 0.01 0.02 0.03 0.04 0.05 0.06
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0.00 0.05 0.10 0.15 0.00 0.01 0.02
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0.00 0.05 0.10 0.15 0.20 0.25 0.30
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These results show that larger τ v
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0.00 0.02 0.04 0.06 0.08 0.10 0.12
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0.00 0.02 0.04 0.06 0.08 0.10 0.12
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0.00 0.02 0.04 0.06 0.08 0.10 0.00
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0.00 0.01 0.02 0.03 0.04 0.000 0.00
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0.000 0.005 0.010 0.015 0.020 0.025
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0.00 0.02 0.04 0.06 0.08 0.10 0.00
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0.00 0.02 0.04 0.06 0.08 0.10 0.00
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3.3.7 Mining streams with periodica
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Choosing important distributions Fo
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containing the representative sets
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an important feature for detecting
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that have arrived in S in the last
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significance level τ. Tables in [9
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Full algorithm The full algorithm o
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values of the change detection dura
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Stream 3 − Exponential Distributi
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0.00 0.02 0.04 0.06 0.08 0.10 0.12
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0.00 0.02 0.04 0.06 0.08 0.10 0.12
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0.00 0.02 0.04 0.06 0.08 0.10 0.00
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0.00 0.02 0.04 0.06 0.08 0.10 Strea
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0.00 0.01 0.02 0.03 0.04 0.00 0.02
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0.00 0.02 0.04 0.06 0.00 0.01 0.02
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0.000 0.002 0.004 0.006 0.008 0.010
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0.00 0.02 0.04 0.06 0.08 0.10 0.000
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0.0000 0.0004 0.0008 0.0012 0.00 0.
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0.000 0.005 0.010 0.015 0.020 0.000
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0.00 0.02 0.04 0.06 0.08 0.10 0.00
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0.00 0.02 0.04 0.06 0.08 0.00 0.02
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0.000 0.004 0.008 0.012 0.000 0.005
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0.000 0.005 0.010 0.015 0.020 0.000
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0.00 0.02 0.04 0.06 0.000 0.002 0.0
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0e+00 2e−04 4e−04 6e−04 8e−
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0e+00 2e−04 4e−04 6e−04 0e+00
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0.00 0.05 0.10 0.15 0.00 0.02 0.04
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0.00 0.02 0.04 0.06 0.08 0.000 0.00
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0.000 0.005 0.010 0.015 0.020 0e+00
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0.00 0.05 0.10 0.15 0.00 0.05 0.10
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0.00 0.02 0.04 0.06 0.000 0.004 0.0
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0.0000 0.0010 0.0020 0.0030 0e+00 2
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Chapter 4 Change Detection in Multi
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Recall that the original approach i
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4.3.1 Building the multi-dimensiona
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4.3.2 Detecting changes When data f
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Algorithm 2 Mean Change Detection f
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For each stream type, there are a t
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0.0 0.1 0.2 0.3 Num of False Change
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0.00 0.01 0.02 0.03 0.04 0.05 0.06
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0.00 0.05 0.10 0.15 0.20 0.25 SD of
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0.00 0.02 0.04 0.06 0.08 0.10 Mean
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4.5.2 Performance comparison with o
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0.00 0.02 0.04 0.06 0.08 0.10 Mean
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Note that KD is a generic approach
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items is too large for memory-inten
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time t and a set of items I = {a, b
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than three) [163]. When the sizes o
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according to certain criteria (Sect
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When the end of WM is reached, it t
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Similarly, if one infrequent item r
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the new frequent itemset with all c
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Step 2.5. No new frequent itemset d
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Updating candidate support For any
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Algorithm 5 UPDATE CANDIDATE SUP 1:
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esult at time t1 may not be consist
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The sizes of the tumbling windows u
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Table 5.2: Results for varying ν v
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Table 5.5: Results for varying λ o
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5.5.5 Memory usage The major memory
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5.6 Summary Mining frequent itemset
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Two techniques are proposed in this
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change detection and mining process
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Bibliography [1] G. Abdulla, T. Cri
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[19] S. Chandrasekaran and M. Frank
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[39] F. David. The moments of the z
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[60] X. Gu, S. Papadimitriou, S. Yu
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[81] J. Kramer and B. Seeger. Seman
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[102] B. Park et al. Reservoir-base
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[124] T. Dasu et al. An information
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[144] W. Shewhart. Statistical Meth
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[166] J. Yu, Z. Chong, H. Lu, and A
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