13. ELIS
–
Mul*media
Lab
5-stars (Technical Perspective)
Open Linked Data (Tim Berners-Lee)
Make your Stuff available on the Web
Make it available as Structured Data
In a non-proprietary Format
Use URLs to identify Things, so one can point at your Stuff
Link your Data to other People’s Data to provide Context
14. ELIS
–
Mul*media
Lab
5-stars (Organisational Perspective)
Open Data Engagement (Tim Davies)
Be Demand-driven
Provide Context
Support Conversation
Build Skills & Capacity
Collaborate with the Community
15. ELIS
–
Mul*media
Lab
5-stars (Functional Perspective)
Open Data Portal Functionalities (iMinds)
Dataset Registry
Metadata Provider
Co-creation Platform
Data Publishing Platform
Common Data Hub
18. ELIS
–
Mul*media
Lab
15’ Open Data Publishing Framework
e.g.
data.gent.be
opendata.antwerpen.be
19. ELIS
–
Mul*media
Lab
Publishes 2 to 5 Star Data
tdt/core
tdt/input
triple store
20. ELIS
–
Mul*media
Lab
REST-full API for Developers
SPARQL
endpoint
XLS
CSV
JSON
XML
...
triple store
core
RESTful data adapter
e.g. datatank.gent.be/Grondgebied/Straten
or data.irail.be/NMBS/Stations
28. ELIS
–
Mul*media
Lab
Big (Data) Bang in Smart Cities
• Data use is expected to grow by as much as 44 times,
amounting to some 35.2ZB (zettabytes -- a billion terabytes) globally
• Sensors, social media feeds, photos, video and cellphone GPS
signals account for 2.5 quintillion bytes of data per day
• More than 50% global population lives in cities and this number is
forecast to rise to 69% by 2050
• The number of city residents is expected to grow from 3.5 billion
to 5 billion in the next 20 years
• ‘Internet of Things’ Age is approaching: 25 billion devices
connected to the Internet by 2015 and 50 billion by 2020
• Access to public data is estimated to be worth €27 billion in the EU
• ICT-enabled energy efficiency could translate into over €600 billion
worth of cost savings for the public and private sector
32. ELIS
–
Mul*media
Lab
Big (Data) Bang in Energy & Utilities
• From a rigid analog system to a smart, dynamic, digital and automated
energy delivery system
• How much excess energy will be available, when to sell it and whether
the grid can transmit it?
• When and where equipment downtime and power failures are most
likely to occur?
• Which customers are most likely to feed energy back to the grid, and
under what circumstances?
• Which customers are most likely to respond to energy conservation
and demand reduction incentives?
• How to manage the commitment of larger, traditional plants in a
scenario where peaks from distributed generation are becoming
relevant?
52. ELIS
–
Mul*media
Lab
QUESTIONS?
Thoughts?
dr. Erik Mannens
erik.mannens@ugent.be
@erikmannens
53. ELIS
–
Mul*media
Lab
Credits
•
•
•
•
•
•
•
EMC – Greenplum
IBM
Visual.ly
Peter Hinssen
Scott Brinker
Jim Lecinski
David Armano
• Did not have time to check all licenses of the Flickr photos – in my
defense, I did not kill anyone nor did I in any way insult and/or
infringe the CIA, NSA, NDA, or any other JAA (Just Another
Acronym)