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THE BIG DATA EUROPE
PROJECT:
STATUS & NEXT STEPS
SC6 Workshop, Cologne
05 December
2016
Supporting the Societal Domains with Big Data
Technology
BigDataEurope Project
16-déc.-16www.big-data-europe.eu
Stakeholder Engagement
Cycle
 Present action, showcase
deployments
 Raise awareness about BDE
results, what they mean for
stakeholders
 Collect requirements to drive
further development
16-déc.-16
www.big-data-europe.eu
M12M6 M18 M24 M30
Data Value Chain Evolution
16-déc.-16
Extraction, Curation Quality, Linking,
Integration
Publication,
Visualization, Analysis
Extraction, Curation, Quality,
Linking, Integration, Publication,
Visualization, Analysis
Health
Transport
Security
Extraction Curation Quality Linking Integration Publication Visualization Analysis
Data
Repositories
Linked
Open Data
TIME
Food SocietiesClimate Energy
Proprietary,
‘locked-in’
solutions
OS Solutions,
Big Data
Stackswww.big-data-europe.eu
A flexible, generic platform for (Big) Data
Value Chain Deployment
Big Data Integrator
16-déc.-16www.big-data-europe.eu
Big Data Integrator
 Prototype developed by BDE
o Incorporates existing BD technology
o Facilitates integration and deployment
 Main points of the architecture
o Dockerization
o Support layer, including integrated UI
o Semantification layer
16-déc.-16www.big-data-europe.eu
Generic Architecture
16-déc.-16www.big-data-europe.eu
 Plug-and-play BD
Platform
 Cloud-deployment ready
 Domain independent,
Customisable
 Stacks Open Source solutions
BDI Prototype
Releases
1. [July 2016]
2. December 2016
3. ….
Demonstrating the Societal Value through 7
Pilot ‘Real-world’ use-cases
BigDataEurope Pilots
16-déc.-16www.big-data-europe.eu
7 Pilots
◎ BDI Platform Instantiations
o Allow end-users to easily deploy functionality in own system
environment
o Modularized Docker approach - easier to replace components
o Reduces effort to keep 3rd party software updated & integrated
◎ 7 Societal Challenge Pilots
o Aligned with 7 European Commision H2020 Societal
Challenges
o Real-world use-cases (Data, Objectives, Solutions)
o Some pilots have different data & objectives but a similar16-déc.-16www.big-data-europe.eu
SC1: Pharmacology research
16-déc.-16
www.big-data-europe.eu
Life
Science
s &
Health
• Query a large
number of
datasets, some
large
• Existing elaborate
ingestion and
homogenization by
OpenPHACTS
• Extensive toolset
developed by OPF
and others
Objective: Large-scale heterogeneous
pharma-research data linking & integration
SC1: Architecture &
Components
16-déc.-16www.big-data-europe.eu
• Replicate Open PHACTS
functionality on the BDE
infrastructure using OS solutions
• Based on Virtuoso, proprietary
distributed database
• Apply to other domains (e.g.
Agriculture)
• Porting to BDI gives flexibility
and enables new
functionalities
• Logging & system health
monitoring
SC2: Viticulture resources
16-déc.-16www.big-data-europe.eu
Food and
Agricultur
e
Objective: Automate publication ingestion
and thematic classification• AgInfra is a major
infrastructure for
agriculture
researchers,
serving cross-
linked
bibliography, data,
and processing
services
www.big-data-europe.eu
SC2: Architecture &
Components
• BDI deployed as an
external infrastructure for
processing text (viticulture
publications)
• Storing and processing
text at a larger scale than
AgInfra can currently
manage
SC3: Predictive maintenance
16-déc.-16www.big-data-europe.eu
Energy
• Wind turbine monitoring
applies computational
models to sensor data
streams
• Models are weekly re-
parameterized using
week’s data from
multiple turbines
Objective: Real-time turbine monitoring
stream processing and analytics
www.big-data-europe.eu
• Existing in-house non-scalable solution for
model parameterization
• Reliable Fortran software for data analysis
• Efficient, but not scalable to data volume
• Developing a BDI orchestrator
• Re-uses existing software unmodified
• Makes it easy to apply in parallel to many
datasets and manage the outputs
SC3: Architecture &
Components
SC4: Traffic conditions
estimation
16-déc.-16www.big-data-europe.eu
Transpor
t
• Combines:
• Traffic modelling from
historical data
• Current measurements
from a taxi fleet of 1200
vehicles
Objective: Estimation of real-time traffic
conditions in Thessaloniki
16-déc.-16www.big-data-europe.eu
• New Flink
implementations of map
matching and traffic
prediction algorithms
• BDI provides access to
varied data sources
• PostGIS database
with city map
• ElasticSearch
database of
historical data
• Kafka stream of real-
time data
SC4: Architecture &
Components
SC5: Climate modelling
16-déc.-16www.big-data-europe.eu
Climate
• Preparing modelling experiments
• Slicing, transforming, combining
datasets
• Submission and retrieval from
modelling infrastructure
• Discovering and re-using
previously computed derivatives
• Lineage annotation: computer
derivatives from datasets and model
parameters
• Finding appropriate past runs avoids
Objective: Supporting data-intensive climate
research
• BDI offers:
• Hive for managing
data in a way that
can be retrieved and
manipulated, rather
than file blocks
• Cassandra stores
structured and textual
metadata for
searching headers
and lineage
• Existing infrastructure; stable, reliable software for parallel computation of
models
• BDI is deployed as an external infrastructure for preparing and managing
datasets
SC5: Architecture &
Components
SC6: Municipality budgets
16-déc.-16www.big-data-europe.eu
Social
Science
s
• Ingestion of budget and
budget execution data
• Multiple municipalities in
varied formats and data
models
Objective: Homogenized Budgetary data made
available for analysis and comparison
16-déc.-16www.big-data-europe.eu
• BDI deployed as
ingestion and storage
infrastructure for external
tools
• Homogenizes variety of
data (JSON, CSV,
XML, etc.)
• Exposes data as
SPARQL endpoint
serving homogenized
data
• Existing analytics and visualization tools
• Use SPARQL queries to retrieve only the relevant slices of the overall data
SC6: Architecture &
Components
SC7: Change detection &
verification
16-déc.-16www.big-data-europe.eu
Secure
Societie
s
• Events are extracted from text
published by news agencies
and on social networking sites
• Events are geo-located and
relevant changes are detected
by comparing current and
previous satellite images
Objective: Detect and Verify Events based on
Satellite Imagery, News and Social Media
16-déc.-16www.big-data-europe.eu
Event Detection
Change Detection
• Re-implementation of change
detection algorithms for Spark
• Parallel orchestrator for text
analytics
• Re-uses existing software
• Scales to many input streams
• BDI provides:
• Cassandra for text content and
metadata
• Strabon GIS store for detected
change location
• Homogeneous access to both for
analysis and visualization
SC7: Architecture &
Components
Free Workshops, Hangouts & Webinars
BigDataEurope Activities
16-déc.-16www.big-data-europe.eu
2nd round of Societal
Workshops
16-déc.-16www.big-data-europe.eu
Transport 22 September 2016 Brussel
s
Collocated with Big Data for
Transport, Tisa workshop
Food&Agri 30 September 2016 Brussel
s
Collocated with DG AGRI
WP2018-20 stakeholder
consultation
Energy 4 October 2016 Brussel
s
Collocated with EC H2020 Info
Day on “Smart Grids and
Storage”
Climate 11 October 2016 Brussel
s
Collocated with Melodies
Project Event – Exploiting Open
Data
Security 18 October 2016 Brussel
s
Standalone Workshop
Societies 5 December 2016 Cologne Collocated with EDDI16- 8th
Other Activities
 Fresh set (7) of Societal Workshops in 2017
 Various SC-focussed and general hangouts,
follow!
o General (technical): 2 this year More to follow!
o SC6: 2 so far, next in the next weeks
o Recordings & Presentations available online!
o Keep track on BDE Website (Events)
16-déc.-16www.big-data-europe.eu
WEB: www.big-data-europe.eu EMAIL: info@big-data-
europe.eu
BIG DATA INTEGRATOR
www.github.com/big-data-europe
PROJECT COORDINATION (Fraunhofer IAIS)
Prof. Sören Auer, auer © cs.uni-bonn · de
> Dr. Simon Scerri, scerri © cs.uni-bonn · de
EIS Department/Group,
Questions & Contacts
www.big-data-europe.eu
16-déc.-16
#BigDataEurope
leads the Fraunhofer
Big Data Alliance

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BDE SC6 workshop - introduction 2016

  • 1. THE BIG DATA EUROPE PROJECT: STATUS & NEXT STEPS SC6 Workshop, Cologne 05 December 2016
  • 2. Supporting the Societal Domains with Big Data Technology BigDataEurope Project 16-déc.-16www.big-data-europe.eu
  • 3. Stakeholder Engagement Cycle  Present action, showcase deployments  Raise awareness about BDE results, what they mean for stakeholders  Collect requirements to drive further development 16-déc.-16 www.big-data-europe.eu M12M6 M18 M24 M30
  • 4. Data Value Chain Evolution 16-déc.-16 Extraction, Curation Quality, Linking, Integration Publication, Visualization, Analysis Extraction, Curation, Quality, Linking, Integration, Publication, Visualization, Analysis Health Transport Security Extraction Curation Quality Linking Integration Publication Visualization Analysis Data Repositories Linked Open Data TIME Food SocietiesClimate Energy Proprietary, ‘locked-in’ solutions OS Solutions, Big Data Stackswww.big-data-europe.eu
  • 5. A flexible, generic platform for (Big) Data Value Chain Deployment Big Data Integrator 16-déc.-16www.big-data-europe.eu
  • 6. Big Data Integrator  Prototype developed by BDE o Incorporates existing BD technology o Facilitates integration and deployment  Main points of the architecture o Dockerization o Support layer, including integrated UI o Semantification layer 16-déc.-16www.big-data-europe.eu
  • 7. Generic Architecture 16-déc.-16www.big-data-europe.eu  Plug-and-play BD Platform  Cloud-deployment ready  Domain independent, Customisable  Stacks Open Source solutions BDI Prototype Releases 1. [July 2016] 2. December 2016 3. ….
  • 8. Demonstrating the Societal Value through 7 Pilot ‘Real-world’ use-cases BigDataEurope Pilots 16-déc.-16www.big-data-europe.eu
  • 9. 7 Pilots ◎ BDI Platform Instantiations o Allow end-users to easily deploy functionality in own system environment o Modularized Docker approach - easier to replace components o Reduces effort to keep 3rd party software updated & integrated ◎ 7 Societal Challenge Pilots o Aligned with 7 European Commision H2020 Societal Challenges o Real-world use-cases (Data, Objectives, Solutions) o Some pilots have different data & objectives but a similar16-déc.-16www.big-data-europe.eu
  • 10. SC1: Pharmacology research 16-déc.-16 www.big-data-europe.eu Life Science s & Health • Query a large number of datasets, some large • Existing elaborate ingestion and homogenization by OpenPHACTS • Extensive toolset developed by OPF and others Objective: Large-scale heterogeneous pharma-research data linking & integration
  • 11. SC1: Architecture & Components 16-déc.-16www.big-data-europe.eu • Replicate Open PHACTS functionality on the BDE infrastructure using OS solutions • Based on Virtuoso, proprietary distributed database • Apply to other domains (e.g. Agriculture) • Porting to BDI gives flexibility and enables new functionalities • Logging & system health monitoring
  • 12. SC2: Viticulture resources 16-déc.-16www.big-data-europe.eu Food and Agricultur e Objective: Automate publication ingestion and thematic classification• AgInfra is a major infrastructure for agriculture researchers, serving cross- linked bibliography, data, and processing services
  • 13. www.big-data-europe.eu SC2: Architecture & Components • BDI deployed as an external infrastructure for processing text (viticulture publications) • Storing and processing text at a larger scale than AgInfra can currently manage
  • 14. SC3: Predictive maintenance 16-déc.-16www.big-data-europe.eu Energy • Wind turbine monitoring applies computational models to sensor data streams • Models are weekly re- parameterized using week’s data from multiple turbines Objective: Real-time turbine monitoring stream processing and analytics
  • 15. www.big-data-europe.eu • Existing in-house non-scalable solution for model parameterization • Reliable Fortran software for data analysis • Efficient, but not scalable to data volume • Developing a BDI orchestrator • Re-uses existing software unmodified • Makes it easy to apply in parallel to many datasets and manage the outputs SC3: Architecture & Components
  • 16. SC4: Traffic conditions estimation 16-déc.-16www.big-data-europe.eu Transpor t • Combines: • Traffic modelling from historical data • Current measurements from a taxi fleet of 1200 vehicles Objective: Estimation of real-time traffic conditions in Thessaloniki
  • 17. 16-déc.-16www.big-data-europe.eu • New Flink implementations of map matching and traffic prediction algorithms • BDI provides access to varied data sources • PostGIS database with city map • ElasticSearch database of historical data • Kafka stream of real- time data SC4: Architecture & Components
  • 18. SC5: Climate modelling 16-déc.-16www.big-data-europe.eu Climate • Preparing modelling experiments • Slicing, transforming, combining datasets • Submission and retrieval from modelling infrastructure • Discovering and re-using previously computed derivatives • Lineage annotation: computer derivatives from datasets and model parameters • Finding appropriate past runs avoids Objective: Supporting data-intensive climate research
  • 19. • BDI offers: • Hive for managing data in a way that can be retrieved and manipulated, rather than file blocks • Cassandra stores structured and textual metadata for searching headers and lineage • Existing infrastructure; stable, reliable software for parallel computation of models • BDI is deployed as an external infrastructure for preparing and managing datasets SC5: Architecture & Components
  • 20. SC6: Municipality budgets 16-déc.-16www.big-data-europe.eu Social Science s • Ingestion of budget and budget execution data • Multiple municipalities in varied formats and data models Objective: Homogenized Budgetary data made available for analysis and comparison
  • 21. 16-déc.-16www.big-data-europe.eu • BDI deployed as ingestion and storage infrastructure for external tools • Homogenizes variety of data (JSON, CSV, XML, etc.) • Exposes data as SPARQL endpoint serving homogenized data • Existing analytics and visualization tools • Use SPARQL queries to retrieve only the relevant slices of the overall data SC6: Architecture & Components
  • 22. SC7: Change detection & verification 16-déc.-16www.big-data-europe.eu Secure Societie s • Events are extracted from text published by news agencies and on social networking sites • Events are geo-located and relevant changes are detected by comparing current and previous satellite images Objective: Detect and Verify Events based on Satellite Imagery, News and Social Media
  • 23. 16-déc.-16www.big-data-europe.eu Event Detection Change Detection • Re-implementation of change detection algorithms for Spark • Parallel orchestrator for text analytics • Re-uses existing software • Scales to many input streams • BDI provides: • Cassandra for text content and metadata • Strabon GIS store for detected change location • Homogeneous access to both for analysis and visualization SC7: Architecture & Components
  • 24. Free Workshops, Hangouts & Webinars BigDataEurope Activities 16-déc.-16www.big-data-europe.eu
  • 25. 2nd round of Societal Workshops 16-déc.-16www.big-data-europe.eu Transport 22 September 2016 Brussel s Collocated with Big Data for Transport, Tisa workshop Food&Agri 30 September 2016 Brussel s Collocated with DG AGRI WP2018-20 stakeholder consultation Energy 4 October 2016 Brussel s Collocated with EC H2020 Info Day on “Smart Grids and Storage” Climate 11 October 2016 Brussel s Collocated with Melodies Project Event – Exploiting Open Data Security 18 October 2016 Brussel s Standalone Workshop Societies 5 December 2016 Cologne Collocated with EDDI16- 8th
  • 26. Other Activities  Fresh set (7) of Societal Workshops in 2017  Various SC-focussed and general hangouts, follow! o General (technical): 2 this year More to follow! o SC6: 2 so far, next in the next weeks o Recordings & Presentations available online! o Keep track on BDE Website (Events) 16-déc.-16www.big-data-europe.eu
  • 27. WEB: www.big-data-europe.eu EMAIL: info@big-data- europe.eu BIG DATA INTEGRATOR www.github.com/big-data-europe PROJECT COORDINATION (Fraunhofer IAIS) Prof. Sören Auer, auer © cs.uni-bonn · de > Dr. Simon Scerri, scerri © cs.uni-bonn · de EIS Department/Group, Questions & Contacts www.big-data-europe.eu 16-déc.-16 #BigDataEurope leads the Fraunhofer Big Data Alliance