Evolution of the mashup ecosystem by copying

[email protected] Mashups 2010

Evolution of the mashup ecosystem by copying

Michael WeissSolange Sari

Technology Innovation Management (TIM)

www.carleton.ca/tim1


Objective

• Mashups are applications that combine data and services provided through APIs with user data

• New application development model: opportunistic programming, uses a bricolage approach

• Creation of mashups supported by an ecosystem of data providers, mashup platforms, and users

• Research questions– How do mashup developers select APIs?– How do mashup developers learn to develop mashups?

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Relevance

• Users/platforms: can benefit from/offer tools that better support the way users work

• Directory providers: their role is to facilitate the selection of APIs and learning of developers

• Data providers: need to understand which APIs their APIs are used together most (interoperability)

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Previous work

• Examined structure and growth of mashup ecosystem using visualization and network analysis to identify members and their relationships

• Opportunistic programming studies how developers use online resources in problem solving

• Research on innovation: (re)combination shortens learning curve, modularity allows mix-and-match

• Models of network growth: preferential attachment

• Copying and duplication mechanisms in describing the growth of the web and biological networks

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• As answer to research questions, we examine to what degree developers create mashups by copying other mashups: copy of the mashup “blueprint”


Hypothesis

5

Number of copies/mashup

1 5 10 50 100 500 1000

5e-04

5e-03

5e-02

5e-01

Number of copies

Cum

ula

tive p

robabili

ty

GoogleMaps

Flickr

Flickr/GoogleMaps

YouTube

GoogleMaps/YouTube

GoogleMaps/Twitter

Amazon/GoogleMaps/YouTube

Amazon/GoogleMaps

Snapshot on 08/16/10ProgrammableWebSnapshot on 08/16/10ProgrammableWebSnapshot on 08/16/10ProgrammableWebMashups 4983 100%Not copied 1528 31%

Blueprints 341 7%

Copies of blueprints

3114 62%

Not copied


Copying model

• Mashup ecosystem as network of mashups and APIs: a link indicates that a mashup uses an API

• Assumption: mashups all have m APIs

• Initialize network: – Create m0 ≥ m APIs, one mashup

• Grow network from t=m0 + 1 to t=N: – Add new API with probability p

– With probability 1-p, choose a mashup as a template– For each API in template, copy the API with probability α, or

choose a new API at random with probability 1-α

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Example

• Initial network: APIs 1 and 2, mashup 3

• Thin solid lines indicate random selection

7

3

1

2

t

t

API

Mashup


Example

• Growth: add a new mashup (4)

• Thick solid lines indicate “copies” relationship

• Thin dashed lines indicate copying

8

3

1

2

4

t

t

Full copy

API

Mashup


Example

• Growth: add a new API (5)

9

3

1

2

5

4

t

t

Full copy

API

Mashup


Example

• Growth: add a new mashup (6)

• Thin solid lines indicate random selection

10

3

61

2

5

4

t

t

Full copy

Partial copy

API

Mashup


Research method

• Calibrate simulation parameters– N: combined actual number of APIs and mashups– m = 2: good approximation of average actual APIs / mashup– p: number of APIs / N (all as of 08/16/10)

• Simulate mashup ecosystem evolution– Vary α over range 0.0 to 1.0, keep m = 2 fixed

– Run each simulation multiple times and terminate when 95% confidence interval is sufficient for the optimization

• Determine best fit of simulated distribution of mashups / API with actual using two fitting methods: sum of squared error fit, and power law fit

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Actual distribution

• Distribution of mashups / API follows Zipf’s law: plotting frequency of mashups relative to rank results in a line with slope close to -1 in a log-log plot

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1 2 5 10 20 50 100 200 500

15

10

50

100

500

Rank

Nu

mb

er

of

ma

sh

up

s

ActualGoogleMaps Flickr

YouTube

Twitter

-0.990


Sum of squared error fit

• Underestimates contribution of top-ranked API

• Overestimates the number of APIs used by at least one mashup by 45% (1020 vs 703)

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0.2 0.4 0.6 0.8

2e+06

4e+06

6e+06

8e+06

1e+07

Copying factor (!)

Su

m o

f sq

ua

red

err

or

1 2 5 10 20 50 100 200 500

15

10

50

100

500

Rank

Nu

mb

er

of

ma

sh

up

s

Actual

Simulated (sum of squared error)α = 0.798


Power law fit

• Slightly overestimates contribution of top API

• Overestimates the number of APIs used by at least one mashup by 22% (859 vs 703)

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1 2 5 10 20 50 100 200 500

15

10

50

100

500

Rank

Nu

mb

er

of

ma

sh

up

s

Actual

Simulated (power law)

0.2 0.4 0.6 0.8

0.0

0.5

1.0

1.5

2.0

2.5

Copying factor (!)

Po

we

r la

w c

oe

ffic

ien

t e

rro

r

α = 0.855


Cumulative contribution of APIs

• Sum of squared error fit underestimates number of APIs that contributed to 50% of API uses

• Power law fit overestimates number of APIs that contributed to 50% of API uses

15

1 2 5 10 20 50 100 200 500

0.2

0.4

0.6

0.8

1.0

Rank

Cu

mu

lative

co

ntr

ibu

tio

n


Discussion

• Both methods obtained their best fit for a high copying factor: this suggests that most mashups are created by modifying the an existing blueprint

• Power law fit more closely approximates actual Zipf distribution, however, sum of squared error fit offers a better match of actual degrees of APIs in midrange

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Insights for stakeholders

• Confirmation of practices directories follow:– List combinations of APIs into mashups– Keep track of developers of mashups– Provide tutorials on mashup development

• Directory providers should make blueprints more apparent: also list frequency of blueprints

• Users benefit as they can look at blueprints to select APIs that work well together and as examples

• API providers learn which other APIs are frequently combined with their API: incentive to interoperate

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Conclusion

• Results indicate that copying plays a significant role in the evolution of the mashup ecosystem

• However, we cannot rule out other factors that could explain how mashup ecosystem grows

• Copying hypothesis in line with current thinking about innovation: eg MacArthur’s Nature of Technology

• Other current and future work:– Extend simulation to include mashups of different size– Test copying hypothesis empirically: we currently examine

hereditary relationships between mashups– Examine link between copying and diversity of ecosystem

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