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© Fraunhofer-Institut für Intelligente Analyse- und Informationssysteme
IAIS
PICASSO Big Data Expert Group
Sören Auer
© Fraunhofer · Seite 2
The three Big Data „V“ – Variety is often neglected
Quelle: Gesellschaft für Informatik
Sören Auer 2
© Fraunhofer · Seite 3
Semantic Web Layer Cake 2001
http://www.w3.org/2001/10/03-sww-1/slide7-0.html
• Monolithic based on XML
• Focus on heavyweight Semantic (Ontologies, Logic, Reasoning)
© Fraunhofer · Seite 4
The Semantic Web Layer Cake 2015 –
“A Little Semantics Goes a Long Way”
Unicode URIs
XML JSON CSV RDB HTML
RDF
RDF/XML JSON-LD CSV2RDF R2RML RDFa
RDF Data Shapes
RDF-Schema
Vocabularies
OntologienSKOS Thesauri
LogikSWRL Regeln
SPARQL
(Acc
ess
co
ntr
ol)
, Sig
natu
r,
En
cryp
tio
n (
HT
TP
S/C
ER
T/D
AN
E),
• Lingua Franca of Data integration with many technology interfaces (XML, HTML, JSON, CSV, RDB,…)
• Focus on lightweight vocabularies, rules,thesauri etc.
• Less “invasive”
© Fraunhofer – Seite 5
INTEGRATING BIG DATA &
LINKED DATA
© Fraunhofer · Seite 6
Blueprint of the Data Aggregator Platform
Follows typical Lambda Architecture
Integrated on top of existing Big Data distribution
+ Semantic Layer (Retaining Semantics using LD approach )
6
Batch Layer
Speed Layer
Data Storage
Real-time data &
Transactions …
Batch View
Real-time
View
mess
ag
e p
ass
ing
message passing
Applications & Showcases
Real-time dashboards
Domain-specific BDE apps
Big Data Analytics
In-stream Mining
BD
E P
latfo
rm&
Inte
lligen
ce
Input data
Stream
Spatial
SocialStatistical Temporal
TransactionalImagery
© Fraunhofer · Seite 7
Adding a Semantic Layer to Data Lakes
7
ManagementAccounting
Regulatory Reporting Risk TreasuryAccounting
Semantic Data Lake• central place for
model, schema and data historization
• Combination of Scale Out (cost reduction) and semantics (increased control & flexibility)
• grows incrementally (pay-as-you-go)
Inbound
Data Sources
Outbound and Consumption
Inbound Raw Data Store
Data Lake (order of magnitude cheaper scalable data store)
Knowledge Graph for Relationship Definition and Meta Data
Frontend to Access Relationship and KPI Definition / Documentation
Frontend to Access (ad hoc) ReportsOutbound Data Delivery to
Target Systems
[1] Wrobel, Voss, Köhler, Beyer, Auer: Big Data, Big Opportunities - Anwendungssituation und Forschungsbedarf. Informatik
[2] Debattista, Lange, Scerri, Auer: Linked 'Big' Data. IEEE/ACM Big Data Computing BDC 2015: 92-98
JSON-LD CSVW R2RMLXML2RDF
© Fraunhofer · Seite 8
INDUSTRIAL DATA SPACE
© Fraunhofer · Seite 9
Vocabulary-based Integration facilitates Data-driven
Businesses
Vocabulary
© Fraunhofer ·· Seite 10
Die Arbeiten zum Industrial Data Space sind
komplementär verzahnt mit der Plattform Industrie 4.0
Handel 4.0 Bank 4.0Versicherung
4.0
…Industrie 4.0Fokus auf die
produzierende Industrie Smart Services
Übertragung,Netzwerke
Echtzeitsysteme
Industrial Data SpaceFokus auf Daten
Daten
…
© Fraunhofer-Institut für Intelligente Analyse- und Informationssysteme
IAIS
The Industrial Data Space Initiative
Community of >30 large German and European Companies
Pre-competitive, publicly funded innovation project involving 11 Fraunhofer institutes for developing IDS reference architecture
Current signatories of the MoU to support the Industrial Data Space Association
© Fraunhofer · Seite 12
Bilder: ©FotoliaFrancesco De Paoli, Nmedia, hakandogu
Semantic Data Linking for Enterprise Data Value Chains
Data Lake Pure Internet
centralized, monopolisticfederated, secure, „trusted“,
standard-basedcompletely dezentral, open,
unsecure
Data management Central Repository Decentral Decentral
Data Ownership Central Decentral Decentral
Data Linking Single provider Federated, on demand Missing
Data Security Bilateral Certified system Bilateral
Market structure Central Provider Role system Unstructured
Transport infrastructure Internet Internet Internet
Industrial Data Space
© Fraunhofer · Seite 13
Bilder: © Fotolia 77260795 ∙ 73040142 58947296 ∙ 68898041
Basic principles of the Industrial Data Space
On DemandVernetzung
Linked Light Semantics
Securitywith
Industrial Data
Container
Certified Roles
On DemandInterlinking
© Fraunhofer · Seite 14Bildquellen: Istockphoto
Industrial Data Space:
On Demand Interlinking
Service A
Service C
Service E
Service B
Service D
Service G
Service F
Enterprise 4
Enterprise 1
Enterprise 6
Enterprise 2Enterprise 3
Enterprise 5
All Data stays with its Ownern and are controlled and secured. Only on request for a service data will be shared. No central platform.
© Fraunhofer · Seite 15 --- VERTRAULICH ---
Linked Light Semantics
A lighweight approach for Data Interlinking
Q: istockphoto.com
Classical Enterprise systems
Fixed Data schema
Globale Enforcement
Closed
ManuelTransformation
High cost
Linked Light Semantics
Reference vocabularies
Bridge between local Representations
Intelligent and structured interlinked
Automatic translation/mapping
Leight-weight
Internet / WWW
Web pages
Only Links
Completely open
Lack of standardization
No structure
© Fraunhofer · Seite 16 --- VERTRAULICH ---
Industrial Data Space
Upload / Download / Search
Internet
AppsVocabulary
Industrial Data Space
Broker
Clearing
RegistryIndex
Industrial Data Space
App Store
Internal IDS
Connector
Company A Internal IDS
Connector
Company B
External IDS
Connector
External IDS
Connector
Upload
Third Party
Cloud Provider
Download
Upload / Download
© Fraunhofer
IDS Architecture Overview
© Fraunhofer · Seite 17
Industry 4.0
Semantic Models as Bridge between Shop & Office Floor
© Fraunhofer · Seite 18
Semantic Administrative Shell &
Reference Architecture for Industry 4.0 (RAMI4.0)
Administrative Shell (Verwaltungsschale) provides a digital identity for arbitraryIndustry 4.0 components (e.g. sensors, actors/robots) exposing data coveringthe whole life-cycle
Reference Architecture for Industry 4.0 (RAMI4.0) provides a conceptualframework for implementingcomprehensive Industry 4.0 scenarios
We have implemented both conceptsalong with a number of IEC and ISO standards in a comprehensiveinformation model ready to beimplemented in productiveenvironments
© Fraunhofer · Seite 19
Summary
Challenges and Opportunities - Interoperability and Standardization
• Adding a semantic layer to Big Data technology
• Integrating Linked Data and Big Data technology
• Towards Enterprise Knowledge Graphs and Data Spaces
• Applications e.g. in Manufacturing, Cultural Heritage, Finance