3. Big Data in Marketing
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4. Big Data in Intelligence
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5. BigDataEurope aims to help
maximizing the societal value of Big
Data
Health, demographic change and wellbeing;
Food security, sustainable agriculture and forestry,
marine and maritime and inland water research, and the
Bioeconomy;
Secure, clean and efficient energy;
Smart, green and integrated transport;
Climate action, environment, resource efficiency and
raw materials;
Europe in a changing world - inclusive, innovative and
reflective societies;
Secure societies - protecting freedom and security of
Europe and its citizens.
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6. The three Big Data „V“ –
Variety is often neglected
Quelle: Gesellschaft für Informatik
10. BigDataEurope 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
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11. Orthogonal Dimensions of Big Data
Ecosystems
Generic Big Data Enabling Technologies
Data Value Chain
Data Generation
& Acquisition
Data Analysis &
Processing
Data Storage &
Curation
Data
Visualization &
Usage
Data-driven
Services
SocietalChallenges
DomainSpecificDataAssets&Technology
Healthcare
Food Security
Energy
Intelligent Transport
Climate & Environment
Inclusive & Reflective Societies
Secure Societies
12. Envisioned societal stakeholder engagement cycle implemented
through Big Data interest groups for each of the H2020 societal
challenges
13. Domains, Focus Areas & Data Assets
Societal
Domain
Preliminary Big Data Focus
area
Selected Key Data assets
Life Sciences
& Health
Heterogeneous data Linking &
integration
Biomedical Semantic Indexing &
QA
ACD Labs / ChemSpider, ChEBI, ChEMBL, Con-ceptWiki,
DrugBank, EN-ZYME, Gene Ontology, GO Annotation, Swis-
sProt, UniProt, Wik-iPathways, PubMed, MeSH, Disease Ontology
(DO), Joint Chemical Dic-tionary (Jochem), Bio-ASQ datasets
Food &
Agriculture
Large-scale distributed data
integration
INFOODS, AQUASTAT Green Learning Network (GLN),
Agricultural Bibliography Network (ABN), AGRIS, AquaMaps,
Fishbase
Energy
Real-time monitoring, stream
processing, data analytics, and
decision support
European Energy Exchange Data, smart meter measurement
data, gas/fuels/energy market/price data, consumption statistics,
equipment condition monitoring data)
Transport
Streaming sensor network & geo-
spatial data integration
GTFS data, OSM/ LinkedGeoData, MobilityMaps, Transport
sensor data, ROSATTE Road safety attributes, European Road
Data Infrastructure - EuroRoadS
Climate
Real-time monitoring, stream
processing, and data analytics.
European Grid Infrastructure (EGI), Databases hosting
atmospheric data. Several software frameworks for simulation,
calibration and reconstruction.
Social
Sciences
Statistical and research data
linking & integration
Federated social sciences data catalogs, statistical data from
public data portals and statistical offices (e.g. EuroStats,
UNESCO, WorldBank)
Security
Real-time monitoring, stream
processing, and data analytics.
Earth Observation data (e.g. Very High Resolution Satellite
Imagery acquired from commercial providers and governmental
systems) and collateral data for supporting CFSP/CSDP missions
14. Health Example: OpenPhacts – Big
Data & Drug Discovery
Precompetitive Data Platform for Pharma
Industry
Integrate Multiple Research Biomedical Data
Resources Into A Single Open & Free Access
Point
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16. Blueprint of the Data Aggregator
Platform
Follows typical Lambda Architecture
Possibly integrated on top of
existing Big Data distribution
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
Transactiona
l
Imagery
17. Data Aggregator Platform
Challenges
Ingest semantic (RDF) and non-semantic
(CSV, JSON, XML, …) data
o Integrate various mapping techniques (R2RML,
CSV on the Web, JSON-LD)
preserve semantics, provenance and
metadata in Big Data processing chains
o Preserve URI/IRIs
o Preserve triples
Exploit semantics for aggregations
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Quelle 4. Bild: http://images.zeit.de/mobilitaet/2014-05/auto-autonom/auto-autonom-540x304.jpg
Prescriptive analytics not only anticipates what will happen and when it will happen, but also why it will happen. Further, prescriptive analytics suggests decision options on how to take advantage of a future opportunity or mitigate a future risk and shows the implication of each decision option. Prescriptive analytics can continually take in new data to re-predict and re-prescribe, thus automatically improving prediction accuracy and prescribing better decision options. Prescriptive analytics ingests hybrid data, a combination of structured (numbers, categories) and unstructured data (videos, images, sounds, texts), and business rules to predict what lies ahead and to prescribe how to take advantage of this predicted future without compromising other priorities.[8]