We gave this talk at the "Open Data Projects cluster meeting" organised by the European Commission on 07-08th September 2015, in Brussels. It was part of the Session IV: Sustainability and business strategies.

1. Session
IV:
Sustainability
and
business
strategies
Processing
of
Open
Data
using
Terradue
Cloud
Pla<orm
Hervé
Caumont
Open
Data
Projects
cluster
mee2ng
07-­‐08th
September
2015,
Brussels

2. Point of Contact :
Hervé Caumont, Program Manager
http://www.terradue.com
• Italian SME, created in 2006
– ESA ESRIN spin-off (Rome)
• UK subsidiary, created in 2011
– Space Cluster (Harwell Oxford)
• 12 start-upers
– 5 different EU nationalities
Cloud Services for Earth Sciences

3. Agenda
•
A
criBcal
need
to
solve
helping
Open
science
to
address
Societal
issues
•
Three
challenges
ahead
with
the
exploita5on
of
Earth
observa5on
Open
Data
•
Terradue
Cloud
Pla<orm’s
Business
Model:
new
types
of
partnerships
based
on
openness
•
SupporBng
sustainable
services:
what
we
have
learnt
about
Open
Data

4.
A
criBcal
need
to
solve
Open
Science
addressing
environment-­‐related
societal
issues,
globally

5. Our customers Earth sciences
Climate
Change

6. Ocean
ecosystem

7. GeoHazards
monitoring

8. EO
data
is
there

11. …but
Earth
Science
applicaBons
• are
s2ll
in
some
early
stages.
• Only
a
few
opera2onal
processes
transferred
to
the
industry,
mostly
within
niche
markets,
• mainly
outside
of
the
“Open
Web”
and
its
poten2al
to
scale
them
out
globally.
• EO
datasets
remain
very
technical
products,
• with
no
Web-­‐na2ve
process
matured
enough,
hampering
innovaBons
that
would
impact
societal
challenges
globally.
11

12. The
Web
as
a
Pla<orm

13. Data-­‐intensive
science
is
the
4th
paradigm
The
Internet
can
do
more
“You
can
be
reading
a
paper
by
someone
and
then
go
off
and
look
at
their
original
data.
You
can
even
redo
their
analysis.
Or
you
can
be
looking
at
some
data
and
then
go
off
and
find
out
all
the
literature
about
this
data.
Such
a
capability
will
increase
the
‘informaBon
velocity’
of
the
sciences
and
will
improve
the
scien2fic
produc2vity
of
researchers”
– Jim
Gray,
The
4th
paradigm,
2009,
MicrosoV
Research

14.
Three
challenges
ahead
with
the
exploitaBon
of
Earth
observaBon
Open
Data

15. 3
proverbs
of
Pla<orm
operaBons

16. If
you
can't
beat
'em,
join
'em
APIs
Build
innova2on
without
owning
all
assets

17.
No
man
is
an
island
Pla<orm
economy
Seek
partnerships
that
challenge
your
idea
If
you
can't
beat
'em,
join
'em
APIs
Build
innova2on
without
owning
all
assets

18. When
in
Rome,
do
as
the
Romans
do
Network
effects
Exploit
the
Web
at
its
full
scale
If
you
can't
beat
'em,
join
'em
APIs
Build
innova2on
without
owning
all
assets
No
man
is
an
island
Pla<orm
economy
Seek
partnerships
that
challenge
your
idea

19. Maximizing
e-­‐collaboraBon
EC
FP7
MELODIES

22. SenBnel-­‐1
Data
Access
EC
FP7
Sen2nels
Synergy
Framework

23. Promoting Earth Observation Services
EOP
<-­‐>
SAFE
Data
mirroring
strategies
OpenSearch
protocol

24. Open
Source
InSAR
Processors
ESA
Thema2c
Plaaorms

25. hbps://geohazards-­‐tep.eo.esa.int/
> > >

26.
Terradue
Cloud
Pla<orm’s
Business
Model
New
types
of
partnerships
based
on
openness

27. ESA
BiDS’14
Conference
hbp://dx.doi.org/10.5281/zenodo.12728

28. Terradue
Cloud
Pla<orm
Concept
of
operaBon

29. 29
Hosted
CommuniBes
5
Climate
Change
Carbon
market
Climatology
Oceanography
Ship
logis2cs
Land
management
Water
quality
Deser2fica2on
Biodiversity
Urban
accoun2ng
Volcanology
Seismic
analysis
Disaster
Risk
Reduc2on
EC
FP7
GEOWOW
(2011-­‐2013)
EC
FP7
SenSyF
(2013-­‐2015)
EC
FP7
MELODIES
(2014-­‐2016)
ESA
Exploita2on
Plaaorms
(2014
onwards)
8
14
ì
35+
H2020
EcoPoten2al
(2015-­‐2018)
30+
Campaigns
(2013
onwards)

30. 30
Virtual
Machines
for
developers
my
RPMs
!

31. 31
Cloud
deployments
ready
to
scale
out

32. 32
Cloud
Storage
on
request

33. 33
Citable
applicaBons
&
datasets

34. Pla<orm
as
a
Service
• Producer
Decks
(hybrid)
• Deploy
Cloud
Appliance
clusters
on
public
clouds
• ElasBc
Catalogues
• S3
data
nodes
• Templates
(hybrid)
• Community
Hubs
on
private
cloud
• Define
OneFlow
services
• Hadoop
Sandboxes
on
private
cloud
• Build
ApplicaBons
Developers
Integrators
Producers
Data
Providers

36.
SupporBng
sustainable
services
What
we
have
learnt
about
Open
Data

37. We
are
aiming
at
• Rapid
prototyping
and
benchmarking
of
algorithms
• Seamless
data
access
whatever
stage
of
the
process
• Automated
data
processing
for
non
expert
users
37

38. What
is
costly
?
• Open
Data
access
is
s2ll
fragile,
it
does
not
inherited
yet
the
resilience
of
the
Web.
Linked
Open
Data
is
one
step
ahead
in
that
direc2on.
• The
required
knowledge
stack
for
valuing
Open
Data
is
widely
spread,
and
no
single
organiza2on
can
afford
the
cost
to
control
it
all.
• The
legal
constraints
with
dissemina2ng
informa2on
products
based
on
Open
Data
are
s2ll
varied,
and
are
2me-­‐consuming
to
understand
well.

39. What
we’ve
learnt
about
Open
Data
• Open
Data
benefits
cannot
take
up
without
early
adopter
services,
growing
the
plaaorm
with
us
• S2ll
need
to
beber
communicate
on
the
available
Cloud
services:
PaaS,
DaaS,
SaaS
are
suppor2ng
the
Informa2on-­‐as-­‐a-­‐Service
end
game
• It’s
2me
to
spread
the
word
on
business
models
for
plaaorm
opera2ons
:
Open
Science,
e-­‐Gov,
commercial,
…

40. We
can
go
even
further
;)
h`p://melodiesproject.eu
h`p://www.terradue.com
@MelodiesProject
@terradue

41. References
• Terradue
Cloud
Plaaorm
–
Developer
Cloud
Sandbox
documenta2on
hbp://docs.terradue.com/developer-­‐sandbox
• EC
FP7
SenSyF
–
Sen2nels
Synergy
Framework
hbp://www.sensyf.eu/
• ESA
Geohazards
Exploita2on
Plaaorm
hbps://geohazards-­‐tep.eo.esa.int/
• ESA
Big
Data
From
Space
Conference
2014
hbp://congrexprojects.com/2014-­‐events/BigDatafromSpace/
• Cantonese
proverbs
J
hbp://www.dramafever.com/news/81-­‐cantonese-­‐proverbs-­‐explained-­‐in-­‐
one-­‐beau2ful-­‐poster/
41