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

More documents

Recommendations

Info

Part II <strong>Refresh</strong> <strong>Strategies</strong> 31
Page 1: THÈSE DE DOCTORAT DE l’UNIVERSIT
Page 5: Acknowledgements First and foremost
Page 9 and 10: Contents Introduction 1 Contributio
Page 11: 7.2 Online Change Estimation for RS
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 46 and 47: fetch new or updated information or
Page 48 and 49: frequency and in-place page update
Page 50 and 51: optimization method. In these works
Page 53 and 54: Chapter 4 Best Effort Refresh Strat
Page 55 and 56: takes its minimum when all ∆Ti sa
Page 57 and 58: s2 published many items immediately
Page 59 and 60: Algorithm 4.1 2Steps Best Effort Re
Page 61 and 62: 4.4 Experimental Evaluation In this
Page 63 and 64: • Uniform refresh strategy: refre
Page 65 and 66: strategies that refresh sources bas
Page 67: Part III Data Dynamics and Online C
Page 70 and 71: months period, starting in March 20
Page 72 and 73: 5.3 Online Estimation Methods Web c
Page 75 and 76: Chapter 6 RSS Feeds Evolution Chara
Page 77 and 78: 6.2 Publication Activity From the d
Page 79 and 80: for testing sources with a low publ
Page 81 and 82: the two data sets and show that fee
Page 83 and 84: Chapter 7 Online Change Estimation
Page 85 and 86: Figure 7.1: Online estimation This
Page 87 and 88: (a) Publication frequency (b) Diver
Page 89 and 90: where α ∈ [0, 1] represents a sm
Page 91 and 92: Choosing the optimal value of the s
Page 93 and 94:
(a) Peaks (b) Uniform (c) Waves Fig
Page 95 and 96:
(a) Peaks (b) Uniform (c) Waves Fig
Page 97 and 98:
variable publication model, because
Page 99 and 100:
We denote by L(λ|pubP rofile) the
Page 101 and 102:
Conclusion and Future Work As the I
Page 103 and 104:
models that reflect different types
Page 105:
optimization goal. Social medias. G
Page 109 and 110:
Appendix A Résumé de la Thèse en
Page 111 and 112:
une liste des abonnées qui peut de
Page 113 and 114:
proposons une architecture et un mo
Page 115 and 116:
List of Figures 1 RSS top ten site
Page 117 and 118:
List of Algorithms 4.1 2Steps Best
Page 119 and 120:
Bibliography [ABP10] George Adam, C
Page 121 and 122:
[dig] Digg. http://digg.com/. [EMT0
Page 123 and 124:
[O’R05] Tim O’Reilly. What Is W
Page 125 and 126:
delkader Hameurlain, Stephen W. Lid
show all

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

You also want an ePaper? Increase the reach of your titles

Delete template?

Save as template?