Big Data Breaking Barriers –First Steps on a Long Trail

Sven Schade (@innovatearth)

Institute for Environment and SustainabilityDigital Earth and Reference Data Unit

www.jrc.ec.europa.eu

Serving society

Stimulating innovation

Supporting legislation

36th ISRSE11 May 2015Berlin, Germany

Including contributions from:

Massimo Craglia, Davide De Marchi, Irene Eleta, Jacopo Grazzini,

Jiří Hradec, Alexander Kotsev, Frank Ostermann, Nicole Ostländer,

Francesco Pantisano, Elena Roglia, Cristina Sanchez, Sven Schade,

Spyridon Spyratos, Chrisa Tsinaraki, Lorenzino Vaccari, as well as

the two visiting scientists (Levente Juhász and Sergi Trilles)

Organisational Background -Digital Earth and Reference Data unit

• INSPIRE: Implementation, Maintenance and Evolution

• Open Data Policy of the JRC

• Complex data handling with geospatial informatics

2

Content

• Brief introduction to Big (Geospatial) Data

• Highlights from 10 completed case studies

• Discussion of 3 barriers

• Conclusions

3

This talk provides on overview of work that was largely carried out in

2014, with some reflections on a possible way ahead.

The content presents my personal view and does not necessarily reflect

the position of the European Commission.

[sourc

e:

jim

mie

joe.c

om

]

4Computer science & system engineering

[sourc

e:

big

data

landscape.c

om

]

[sourc

e:

NBD

RA, N

IST]

5

• Volume: remote sensing (images or point clouds in 2/3D), or intense modelling

(e.g. immediate and medium range weather forecasting and climate modelling)

• Velocity: large single volumes or continuous inputs - of the same type but from

massive amounts of sources (e.g. in the context of the IoT)

• Variety: given any place on earth (or elsewhere), we already

today receive spatially-related data sets and streams for

multiple sources. These grow and accumulate over time.

• Veracity: reference data and the differentiation between “authoritative” sources

and user-contributed contend discussed in the geospatial and

statistics communities

[sourc

e:

wik

imedia

.org

]

Geospatial information science

[sourc

e:

M. Cra

glia]

5

• Volume: remote sensing (images or point clouds in 2/3D), or intense modelling

(e.g. immediate and medium range weather forecasting and climate modelling)

• Velocity: large single volumes or continuous inputs - of the same type but from

massive amounts of sources (e.g. in the context of the IoT)

• Variety: given any place on earth (or elsewhere), we already

today receive spatially-related data sets and streams for

multiple sources. These grow and accumulate over time.

• Veracity: reference data and the differentiation between “authoritative” sources

and user-contributed contend discussed in the geospatial and

statistics communities

[sourc

e:

wik

imedia

.org

]

Geospatial information science

[sourc

e:

M. Cra

glia]

63D platform for geospatial data handling

3D browser based viewer

EU3D advanced desktop application

Both building on of the “Core003” data set, a Very High Resolution (VHR) optical coverage over the member and cooperating countries of the European Environment Agency (EEA) that was generated from SPOT-5 data through multi-spectral 2.5 meters resolution data ortho-rectified with a geo-location accuracy of less than 5 meters Root Mean Square Error (RMSE)

[source:D. De Marchi]

[source: D. De Marchi]

7Investigating usage potential of social media platforms

Using social network analysis to sense social behaviour

Using social media platforms to complement authoritative vector data

Using new database technologies to store and query social media data

[source:S. Spyratos et al.]

[source: I. Eleta, J. Grazzini and E. Roglia]

[source: L. Juhász]

8Sensing technologies and the Internet of Things

Service-enabled sensing platform for the environment

Real-time event detection from sensor networks

[source:S. Trilles et al.]

[source: A. Kotsev, F. Pantisano et al.]

9Handling complex data integration

New modes for multi-sensory integration

Visualisations of complex metadata

Model transparency

[source: J. Hradec]

[source: N. Ostländer]

[source:F. Ostermann and S. Schade]

10

Do not expect (only) one (Digital Earth) platform!

Establish data flows between existing systems and enable the exchange of lessons learned

Technical barriers

• Rich choice of implementations available

• Implications from technical choices and re-use

11Discussion

[sourc

e:

pix

abay.c

om

]

Enable knowledge transfers across the sciences

Do not (only) work together!

• Potential useful data does not reside in

well-know communities any more

• Desired to use data and tools from

other sciences and vice versa

12

Semantic barriers

[sourc

e:

pix

abay.c

om

]

Discussion

13

Organisational barriers

• Beneficiaries of applications do not only

reside in (data) science

• Adding value by including “foreigners”

and opening new “markets”

Increase efforts on social and behavioral aspects

Do not (only) address the technocratic dimension! [s

ourc

e:

pix

abay.c

om

]

Discussion

• These are only a few examples and findings

• Many case studies have been developed across the globe

• Many of us took their first steps on the long trail of successful and useful

knowledge extraction from (small and big) data

14Conclusion

[source: wikimedia.org]

• These are only a few examples and findings

• Many case studies have been developed across the globe

• Many of us took their first steps on the long trail of successful and useful

knowledge extraction from (small and big) data

We are currently investigating the use of RM-ODP to integrate the findings of any number of case studies.It seems promising to use this standard methodology to describe information systems that is already widely used in the geospatial information domain to develop a high-level description of Digital Earth platforms and provide guidance for case specific re-use.

14

[source: pixabay.com]

Conclusion

[source: wikimedia.org]

• These are only a few examples and findings

• Many case studies have been developed across the globe

• Many of us took their first steps on the long trail of successful and useful

knowledge extraction from (small and big) data

Final slide, reallyThank you!

[source: wikimedia.org]

We are currently investigating the use of RM-ODP to integrate the findings of any number of case studies.It seems promising to use this standard methodology to describe information systems that is already widely used in the geospatial information domain to develop a high-level description of Digital Earth platforms and provide guidance for case specific re-use.

[source: pixabay.com]