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Empowering Communities with
Data Technologies
Fraunhofer IAIS - coordinatorSC Workshops
BIG DATA EUROPE
The Motivation – Big Data
Every day, we create 2.5 quintillion bytes of data — so much that 90% of the data in the
world today has been created in the last two years alone.
This data comes from everywhere: sensors used to gather climate information, posts to
social media sites, digital pictures and videos, purchase transaction records, and cell phone
GPS signals to name a few.
This data is big data. Source:
Big Data
23-sept.-15www.big-data-europe.eu
BIG
DATA
Big Data: Dimensions
www.big-data-europe.eu
Volume
Velocity
Variety
100010101010101010101010
010100101010101010010100
101010010100101010010100
101001010100101010100101
010001010101010010101010
101010101001010101010101
001010101110001010101010
101010101001010010101010
101001010010101001010010
101001010010100101010010
101010010101000101010101
00101010101
1000101010
1010101010
1010010100
1010101010
1001010010
…….
………….
……
………..…….
.
……………
1 0
1000101010
1010101010
1010010100
1010101010
100101oo11
Veracity!
Big Data Dimensions
Health
Climate
Energy
> Transport
Food
Societies
Security
Big Data in Europe: Challenges,
Opportunities
www.big-data-europe.eu
Loremi
psumd
olors
KSDJ
OPSC
KKSD
KAB
LKASJL
LAWWD
S
wpweppe
pwpisio
we
101010
011010
100101
010
Data
Sources
#1: Compile, Harmonise, Publish
101010
011010
100101
010
101010
011010
100101
010
#2: Interlink, Centralise Access, Explore
101010100101010101001
011010001010101010010
101010100001011010001
010101010010101010100
100101010100101010101
001011010001010
Town
Squar
e
#3: Analyse, Discover, Visualise
#4: Mashup, Cross-domain Exploitation
Journalists Authorities
Traffic
Sensors
Taxi Map SchedulesRoad Network
Big Data in Europe:
Obstacles
23-sept.-15www.big-data-europe.eu
#1 Big Data “Variety“ problem
 Multiple Data Sources
 Required: Integration, Harmonisation
#2 Opening-up Data concerns
 Loss of control, lack of tracking
 Reservations about large corporations
#3 Limited Skills, Training,
Technology
 Lack of Data Scientists
 Lack of Generic Architectures, components
Data Value Chain Evolution
23-sept.-15www.big-data-europe.eu
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
Cloud
Stage 1
Stage 2
Stage 3
Food SocietiesClimate Energy
BigDataEurop
e – The
Project
23-sept.-15www.big-data-europe.eu
Rationale
 Show societal value of Big Data
 Lower barrrier for using big data technologies
o Required effort and resources
o Limited data science skills
 Help establishing cross-
lingual/organizational/domain Data Value
Chains
23-sept.-15
BigDataEurope: Consortium
Stakeholder Engagement Cycle
Data Aggregator Platform:
Blueprint
Batch Layer
Speed Layer
Data Storage
Real-time data &
Transactions …
Batch View
Real-time
View
messagepassing
message passing
Applications & Showcases
Real-time dashboards
Domain-specific BDE apps
Big Data Analytics
In-stream Mining
BDEPlatform&
Intelligence
Input data
Stream
Spatial
Social
Statistical
Temporal
Transaction
al
Imagery
+ Semantic Layer (Retaining Semantics using LD
Lambda Architecture
Big Data & Transport
 Challenges
o We want to hear what you have to say!
o Input for BD Aggregator Platform & Components
 Opportunities
o We want to broaden your horizons!
o We will provide free, useful, cutting-edge BD
tools.
23-sept.-15www.big-data-europe.eu

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