complexity resolution control for context based on metromaps

Marat [email protected]/01/22@LOIS研＠FIT

Complexity Resolution Control

PDF: bit.do/160122

for Context Based on Metromaps

#metromaps#visualization #complexity #searchspace

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The Metromap

07 K.Nesbitt "Getting to more abstract places using the metro map metaphor" 8th IV (2004)

M.Zhanikeev -- [email protected] Complexity Resolution Control for Context Based on Metromaps -- bit.do/160122 2/17...

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Metromaps in Practice


3/17

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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 )


4/17

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Metromaps: Lines and Stations

01 M.Zhanikeev "Multidimentional Classification Automation with Human Interface based on Metromaps" 4th AII (2015)M.Zhanikeev -- [email protected] Complexity Resolution Control for Context Based on Metromaps -- bit.do/160122 5/17

...

5/17

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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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Complexity Resolution


7/17

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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 mainstation on the metromap


8/17

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Metromap and Distribution Curves

0 5 10 15 20 25 30 35 40 45Decreasing 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 bigdata

• each curve is distributionof mass acrossvalues=stations

• distributions are often withhotspots 02

• metromap is a networkthat 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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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 includeother stations on the same line,but not transfer to another line

• any line: can transfer toother lines

• note: the assumption is thatsome stations are shared bytwo or more lines


10/17

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Results

0 10 20 30 40 50

Increasing sequence

5.6

5.95

6.3

6.65

7

7.35

7.7

log(

1 +

gro

wth

)

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 +

gro

wth

)

Cross#2 Hops#2

0 10 20 30 40 50

Increasing sequence

7

7.5

8

8.5

log(

1 +

gro

wth

)

Cross#2 Hops#3

SamelineAnyline

0 20 40 60

Increasing sequence

5.6

6

6.4

6.8

7.2

7.6

log(

1 +

gro

wth

)

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 +

gro

wth

)

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 +

gro

wth

)

Cross#3 Hops#3

• aggregated data:number oftourists

• each column is fora given number ofhops from a randomlyselected station

• each row is for thenumber of intersectinglines

• beware the log scale


11/17

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On the Other Side...


12/17

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Inverse Problem: Growing Search Space

0 5 10 15 20 25 30 35 40 45Decreasing 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: metromapoverlaying distributioncurves

• different objective: maximizecount increment for aminimum increase instation coverage

• translated into tourism: how tomaximize increase innumber of tourists atminimal cost


13/17

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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 23.85Decreasing order

0200400600800

10001200140016001800

Ad

ded

val

ue (x

10,0

00 p

eopl

e)agecategorylocationclass


14/17

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That’s all, thank you ...


15/17

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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 onsimilar tours toSapporo is much smaller

What is the cost/benefitof connecting these?

• if you plot only the raw = basebigdata, then lines never intersect --such a metromap is no fun

• the point is to connect all lines byselecting a specific case = kind oftourist

• note: this is one of many ways togenerate metromaps in practice


16/17

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You Know This as "Social Media"• I call it head biting tail model, means: popular items connect to rarelyvisited 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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complexity resolution control for context based on metromaps

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