Metromaps are recognized well in visualization research but are rarely found in applied technologies. Earlier works showcased metromaps as a valid tool for human-robot hybrid learning when mining Big Data. This paper goes one step further and shows that metromaps are good for controlling complexity in search/state space. To accomplish this, any generic context is represented as train lines and stations, where stations can be shared by one or more train lines. Complexity is controlled by focusing on a given station and defining resolution in terms of hop-length on e2e paths to other stations in the overall metromap.

1. Marat Zhanikeev
maratishe@gmail.com
maratishe.github.io
2016/01/22@LOIS研＠FIT
Complexity Resolution Control
PDF: bit.do/160122
for Context Based on Metromaps
#metromaps
#visualization
#complexity
#searchspace

2. .
The Metromap
07 K.Nesbitt "Getting to more abstract places using the metro map metaphor" 8th IV (2004)
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3. .
Metromaps in Practice
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4. .
In Practice: Context Management + AI
Rebot
(careless)
Input
Human
Human
{structure}
(pinpoint)
Select
Browse
(or use otherwise)
Some
Knowledge
(folksonomies,
knowledge bases,
databases, indexes,
ontologies, etc.)
(metromaps )
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5. .
Metromaps: Lines and Stations
01 M.Zhanikeev "Multidimentional Classification Automation with Human Interface based on Metromaps" 4th AII (2015)
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6. .
Data for this Study (tourism)
• comes from real Big Data -- not supposed to share the source, yet public
• tourism stats: location, category (culture, food, ...), class (local/inbound), age
• blue line connects randomly selected stations on each data line
location
category
age
class
location
category
age
class
add
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7. .
Complexity Resolution
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8. .
Concepts and Connections
• applied to decisions which have to deal with a large search space
◦ big data analysis, robotics, social systems
• traditional ontologies → metromaps
• complexity resolution control = growing/shrinking area around the main
station on the metromap
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9. .
Metromap and Distribution Curves
0 5 10 15 20 25 30 35 40 45
Decreasing order
4.55
4.9
5.25
5.6
5.95
6.3
6.65
7
7.35
7.7
8.05
log(1+v)
• the same tourism big
data
• each curve is distribution
of mass across
values=stations
• distributions are often with
hotspots 02
• metromap is a network
that overlays the curves
02 M.Zhanikeev "The Next Generation of Networks is all about Hotspot Distributions and Cut-Through Circuits" IEICE・CQ研 (2015)
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10. .
SameLine and AnyLine Models
B
Line 1
Line 2 Line 3
A
Same -line,
1 station away (from A)
Any line,
2 stations away
(from B)
• same line : can include
other stations on the same line,
but not transfer to another line
• any line: can transfer to
other lines
• note: the assumption is that
some stations are shared by
two or more lines
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11. .
Results
0 10 20 30 40 50
Increasing sequence
5.6
5.95
6.3
6.65
7
7.35
7.7
log(1+growth)
Cross#2 Hops#1
0 20 40 60
Increasing sequence
6.3
6.65
7
7.35
7.7
8.05
8.4
log(1+growth)
Cross#2 Hops#2
0 10 20 30 40 50
Increasing sequence
7
7.5
8
8.5
log(1+growth)
Cross#2 Hops#3
SamelineAnyline
0 20 40 60
Increasing sequence
5.6
6
6.4
6.8
7.2
7.6
log(1+growth)
Cross#3 Hops#1
0 20 40 60
Increasing sequence
6.3
6.65
7
7.35
7.7
8.05
8.4
log(1+growth)
Cross#3 Hops#2
0 20 40 60
Increasing sequence
6.3
6.65
7
7.35
7.7
8.05
8.4
log(1+growth)
Cross#3 Hops#3
• aggregated data:
number of
tourists
• each column is for
a given number of
hops from a randomly
selected station
• each row is for the
number of intersecting
lines
• beware the log scale
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12. .
On the Other Side...
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13. .
Inverse Problem: Growing Search Space
0 5 10 15 20 25 30 35 40 45
Decreasing order
4.55
4.9
5.25
5.6
5.95
6.3
6.65
7
7.35
7.7
8.05
log(1+v)
• the same input: metromap
overlaying distribution
curves
• different objective: maximize
count increment for a
minimum increase in
station coverage
• translated into tourism: how to
maximize increase in
number of tourists at
minimal cost
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14. .
Inverse Problem Results
• response to unit cost, adding only one station on a randomly selected line
0 2.65 5.3 7.95 10.6 13.25 15.9 18.55 21.2 23.85
Decreasing order
0
200
400
600
800
1000
1200
1400
1600
1800
Addedvalue(x10,000people)
agecategorylocationclass
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15. .
That’s all, thank you ...
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16. .
More on Data and Metromaps
Kind of
tourism
Age
Inbound
(inside)
Japan
未成年 若年層 中年層 老年層
Place
北海道 東京都 京都府 福岡県 ……
Kind of
activity
食べる 温泉 …
…
Young people who
come from abroad to
Tokyo on a food tour.
Tourist counts on
similar tours to
Sapporo is much
smaller
What is the cost/benefit
of connecting these?
• if you plot only the raw = base
bigdata, then lines never intersect --
such a metromap is no fun
• the point is to connect all lines by
selecting a specific case = kind of
tourist
• note: this is one of many ways to
generate metromaps in practice
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17. .
You Know This as "Social Media"
• I call it head biting tail model, means: popular items connect to rarely
visited ones
Decreasing order
You can try
to lift the tail
by itself...But it is
much easier
to connect
head with tail.
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