Roxana - Gabriela HORINCAR Refresh Strategies and Online ... - LIP6

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3.2 Crawling Web Content . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 35 3.2.1 First-time Downloading Strategies . . . . . . . . . . . . . . . . . . . 36 3.2.2 Refresh Strategies . . . . . . . . . . . . . . . . . . . . . . . . . . . . 37 3.3 Crawling RSS Feeds . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 38 3.4 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 39 4 Best Effort Refresh Strategies for RSS Feeds 41 4.1 Best Effort Refresh Strategy for Unsaturated Feeds . . . . . . . . . . . . . . 42 4.1.1 Utility Function . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 44 4.1.2 Utility Monotonicity . . . . . . . . . . . . . . . . . . . . . . . . . . . 45 4.2 2Steps Best Effort Refresh Strategy for Saturated and Unsaturated Feeds . 46 4.3 Refresh Threshold Adjustment . . . . . . . . . . . . . . . . . . . . . . . . . 46 4.4 Experimental Evaluation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 49 4.4.1 Parameter Settings . . . . . . . . . . . . . . . . . . . . . . . . . . . . 49 4.4.2 Comparing Strategies . . . . . . . . . . . . . . . . . . . . . . . . . . 50 4.4.3 Strategy Effectiveness . . . . . . . . . . . . . . . . . . . . . . . . . . 51 4.4.4 Strategy Robustness . . . . . . . . . . . . . . . . . . . . . . . . . . . 53 4.5 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 53 III Data Dynamics and Online Change Estimation 55 5 Related Work 57 5.1 Real RSS Feeds Characteristics . . . . . . . . . . . . . . . . . . . . . . . . . 57 5.2 Change Models . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 59 5.2.1 Homogeneous Poisson Process . . . . . . . . . . . . . . . . . . . . . . 59 5.2.2 Inhomogeneous Poisson Process . . . . . . . . . . . . . . . . . . . . . 59 5.3 Online Estimation Methods . . . . . . . . . . . . . . . . . . . . . . . . . . . 60 5.3.1 Moving Average . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 60 5.3.2 Exponential Smoothing . . . . . . . . . . . . . . . . . . . . . . . . . 60 5.3.3 Curve Fitting . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 61 5.4 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 61 6 RSS Feeds Evolution Characteristics 63 6.1 Data Set Description . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 63 6.2 Publication Activity . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 65 6.3 Publication Periodicity . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 65 6.4 Publication Shapes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 67 6.5 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 69 7 Online Change Estimation Techniques 71 7.1 Online Change Estimation . . . . . . . . . . . . . . . . . . . . . . . . . . . . 71 7.1.1 Online and Offline Change Estimation . . . . . . . . . . . . . . . . . 72 7.1.2 Importance of the Online Change Estimation . . . . . . . . . . . . . 72
7.2 Online Change Estimation for RSS Feeds . . . . . . . . . . . . . . . . . . . 74 7.2.1 Single Variable Publication Model . . . . . . . . . . . . . . . . . . . 74 7.2.2 Periodic Publication Model . . . . . . . . . . . . . . . . . . . . . . . 75 7.2.3 Hybrid Publication Model . . . . . . . . . . . . . . . . . . . . . . . . 77 7.2.4 Smoothing Parameter Adjustment . . . . . . . . . . . . . . . . . . . 77 7.3 Experimental Evaluation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 78 7.3.1 Experimental Setup . . . . . . . . . . . . . . . . . . . . . . . . . . . 78 7.3.2 Online Estimation Evaluation . . . . . . . . . . . . . . . . . . . . . . 79 7.3.3 Integration of Online Estimation with 2Steps Refresh Strategy . . . 82 7.3.4 Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 85 7.4 Statistical Estimation Model . . . . . . . . . . . . . . . . . . . . . . . . . . 85 7.4.1 Homogeneous Poisson Process . . . . . . . . . . . . . . . . . . . . . . 86 7.4.2 Publication Profile . . . . . . . . . . . . . . . . . . . . . . . . . . . . 86 7.4.3 Maximum Likelihood Estimation . . . . . . . . . . . . . . . . . . . . 86 7.5 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 88 Conclusion and Future Work 89 Contributions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 89 Perspectives and Future Work . . . . . . . . . . . . . . . . . . . . . . . . . . 91 IV Appendix 95 A Résumé de la Thèse en Français 97 Contributions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 100 Organisation du Mémoire . . . . . . . . . . . . . . . . . . . . . . . . . . . . 102 List of Figures 103 List of Algorithms 105 Bibliography 107
Page 1: THÈSE DE DOCTORAT DE l’UNIVERSIT
Page 5: Acknowledgements First and foremost
Page 9: Contents Introduction 1 Contributio
Page 14 and 15: each item corresponding to a change
Page 16 and 17: • Information loss. Since each ne
Page 18 and 19: factors and objectives usually targ
Page 21 and 22: Chapter 1 Content-Based Feed Aggreg
Page 23 and 24: Figure 1.2: RSS Aggregator source t
Page 25 and 26: 1.4.1 Feed Semantics: Windows and S
Page 27 and 28: in sel(q) ∈ [0, 1]. We consider t
Page 29 and 30: 1.6.1 Aggregation Network Model Def
Page 31 and 32: instead of multidimensional node pr
Page 33 and 34: Chapter 2 Feed Aggregation Quality
Page 35 and 36: efreshed. We consider separately th
Page 37 and 38: Figure 2.2: Stream and Window diver
Page 39 and 40: 2.2 Feed Completeness In this secti
Page 41: Observe that, as an alternative, we
Page 45 and 46: Chapter 3 Related Work A web crawle
Page 47 and 48: lished content. The big advantage o
Page 49 and 50: uncrawled urls. According to their
Page 51: RSS aggregator is to quickly retrie
Page 54 and 55: consequently maximizes their window
Page 56 and 57: Best Effort Refresh Strategy. Let u
Page 58 and 59: 4.2 2Steps Best Effort Refresh Stra
Page 60 and 61:
However, in the context of content-
Page 62 and 63:
Divergence Values. In a purely pull
Page 64 and 65:
Figure 4.3: Window freshness exact
Page 66 and 67:
Figure 4.4: Tau convergence 54
Page 69 and 70:
Chapter 5 Related Work Modern Web 2
Page 71 and 72:
5.2 Change Models In order to effic
Page 73:
ument that corresponds to a certain
Page 76 and 77:
and serve us as real world data to
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published items and in the shape of
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negligible / insignificant publicat
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of the feed publication activity an
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systems depend on appropriate sourc
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7.2 Online Change Estimation for RS
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Figure 7.4: Periodic publication mo
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Dynamical α Adjustment Heuristics.
Page 92 and 93:
Furthermore, we show the 24-dimensi
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sources are refreshed and thus, how
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(a) Peaks (b) Uniform (c) Waves Fig
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7.4.1 Homogeneous Poisson Process I
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Algorithm 7.1 Lambda Estimation wit
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Feed completeness is based on the s
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s. Its value might be fixed accordi
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Part IV Appendix 95
Page 110 and 111:
un utilisateur de rester informé d
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• Ressources limitées. Non seule
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Organisation du Mémoire Ce mémoir
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7.10 Window freshness . . . . . . .
Page 118 and 119:
106
Page 120 and 121:
[BP98] Sergey Brin and Lawrence Pag
Page 122 and 123:
Triantafillou, and Torsten Suel, ed
Page 124 and 125:
[rssa] RSS Board. http://www.rssboa
Page 126:
Bernhard Thalheim, and Xiaoyang Sea
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Roxana - Gabriela HORINCAR Refresh Strategies and Online ... - LIP6

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