Presenter: Dr. Jan Neumann, Research Manager, Comcast

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0
0.5
1
1.5
2
2.5
3
3.5
4
0 168 336 504 672 840
Similarity
Δ Hours
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0
0.5
1
1.5
2
2.5
3
3.5
4
0 168 336 504 672 840
Similarity
Δ Hours
Daily pattern:
views in similar hours of the
days are more similar
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0
0.5
1
1.5
2
2.5
3
3.5
4
0 168 336 504 672 840
Similarity
Δ Hours
Weekly pattern:
views in similar days of the
week are more similar
Daily pattern:
views in similar hours of the
days are more similar
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•

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0
0.5
1
1.5
2
2.5
3
3.5
4
0 168 336 504 672 840
Similarity
Δ Hours
Weekly pattern:
views in similar days of the
week are more similar
Daily pattern:
views in similar hours of the
days are more similar
Overall decay:
recent views are more similar
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•

9. 0
0.02
0.04
0.06
0.08
0.1
0 168 336
Similarity
∆ Hours
0
0 168 336 504 672 840
Similarity
Δ Hours
VOD Daypart View Similarity

10. 0
1
2
3
4
0 168 336 504 672 840
Similarity
VOD Daypart View Similarity
0
0.1
0.2
0.3
0.4
0 168 336
Repeatpercentage
∆ Hours
Repeated-Search Pattern
0
0.1
0 168 336
Similarity
∆ Hours
Linear(Station-level) View Similarity

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12. Number of Signals
0.7
7
= New Signal
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0%
20%
40%
60%
80%
100%
Top 5 Top 10 Top 20
Only Social
Nielsen + DVR
Nielsen + DVR +
Social

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18. 20.00
25.00
30.00
35.00
40.00
45.00
50.00
55.00
60.00
65.00
70.00
0.40 0.60 0.80 1.00 1.20 1.40 1.60
Recall
Resolution (Weighted Sum, Normalized by User Study)
Recall vs. Resolution
BaseLine
CoMPASS
CoMPASS++
ViaccessOrca
DigitalSmith
Rovi
ContentWise
Gravity
MortarData
ThinkAnalytics
Compass (Trend)
Various
internal
and
external
Algorithms

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26. Picture courtesy of Cisco

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29. Baseline w/
100% cache
size
NetworkBWSavedbyCache

30. Baseline w/
100% cache
size
Prediction w/
30% cache
size
NetworkBWSavedbyCache

31. Baseline w/
100% cache
size
Prediction w/
30% cache
size
Prediction w/
60% cache size
NetworkBWSavedbyCache

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