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Manfred Hauswirth TU Berlin, Open Distributed Systems & Fraunhofer FOKUS
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HEADLINE SUBHEADLINE
LINKING EVERYTHING
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A NETWORK OF EVERYTHING
enabling innovation and increased productivity
Interconnected Universal All encompassing
assists humans, organisations and systems and things with problem solving
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LINKING ON ALL LAYERS
Logistik Quelle: de.colourbox.com
Source: Agenda CPS, Acatech Studie 2012
IZM Embedded systems e.g. sensor systems
Networked embedded systems e.g. sensor networks for condition monitoring
Intelligent, networked systems e.g. intelligent, predictive maintenance of machinery
“Internet of Everything” Web of things, data and services e.g. interconnected processes, products and data
Cyber Physical Systems
© Fraunhofer
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Mobile
Business
Home
People to People (P2P)
Data
Machine to Machine (M2M)
People
Things
Process
People to Machine (P2M)
Connecting the Unconnected
The “Internet of Everything”
Source: Cisco Whitepaper 2013
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UNTAPPED VALUE
Source: Information Marketplaces - The New Economics of Cities, The Climate Group, Arup, Accenture, Horizon, 2011
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The Internet of Everything (IoE) creates $14.4 trillion in Value at Stake — the combination of increased revenues and lower costs that is created or will migrate among companies and industries from 2013 to 2022. Cisco Whitepaper, 2013
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Your biggest competitor: Do nothing Adopting a ‘sit and wait’ attitude towards digitization has made even established market players disappear. (Quelle, Kodak) Bosch Whitepaper, 2014
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− Many communities struggle with closed approaches − E.g., pervasive computing, embedded systems, IoT, ...
− Cyber-Physical Systems are inherently “open world”
− Prof. David Karger (MIT) in his ESWC 2013 keynote:
“Semantic Web technologies support and open world assumption where millions of unforeseeable schemas may have to be integrated.”
− Simple integration with existing LOD data sets
− Geo-spatial, governmental, media, ...
− Manageable integration effort with other graph data, e.g., Google Knowledge Graph, Facebook Graph, etc.
THAT’S WHY WE NEED LINKED DATA
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TWO KEY COMPONENTS
1. RDF – Resource Description Framework Graph based Data – nodes and arcs
− Identifies objects (URIs) − Interlink information (Relationships)
2. Vocabularies (Ontologies) − provide shared understanding of a domain − organize knowledge in a machine-
comprehensible way − give an exploitable meaning to the data
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Cities:Dublin
84421km2 Geo:IslandOfIreland
EU:RepublicOfIreland
Geo:locatedOn
Geo:area Geo:hasCapital
Geo:hasLargestCity
Wikipedia.org
Gov.ie
EU:RepublicOfIreland
Person:EndaKenny
Gov:hasTaoiseach Gov:hasDepartment
IE:DepartmentOfFinance
WHY GRAPHS AND ONTOLOGIES?
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LINQUA FRANCA: LINKED OPEN DATA
Linking Open Data cloud diagram, by Richard Cyganiak and Anja Jentzsch.
http://lod-cloud.net/
Media
Government
Geo
Publications
User-generated
Life sciences
Cross-domain
US government UK government
BBC New York Times
LinkedGeoData
BestBuy Overstock.com Facebook
Over 200 open data sets with more than 25 billion facts, interlinked by 400 million typed links, doubling every 10 month
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LINKING: A GENERAL PARADIGM
− Thematic − Technological − Scientific − Organizational
− Networks − Data − Applications − People
− Spezialized − Secure − Efficient
“90% of the data in the world today has been created in the last two years alone”
– IBM
Over the next few years we’ll see the adoption of scalable frameworks and platforms for handling streaming, or
near real-time, analysis and processing.” – O’Reilly
“The bringing together of a vast amount of data from public and private sources […] is what Big Data is all about” – IDC
Big Data represents a number of developments in technology that have been brewing for years and are coming to a boil. They include an explosion of
data and new kinds of data, like from the Web and sensor streams; [...].” – IDC
LOTS OF DATA
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SMALL DATA, SMART DATA AND BIG DATA
Conflicts and benefits
− Global view of data vs. decentralized data sources
− Global view of data vs. privacy − What is “the right question” ?
− Deep Learning
− Networked, i.e., linked data − Hierarchical, networked analytics
− Smart Data = Big Data + utility + semantics + data quality + security + privacy = useful, high-quality, authentic, correct data
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Privacy gone?
− “Necessary” Services: Google (Search, Gmail, Drive, Android), Apple (iOS, App Store), Facebook, WhatsApp, Amazon, LinkedIn, Dropbox
− Localization: GPS, WLAN − Sensors: Smart Home, Smart City, Smart Grid
− International Regulation? − Informed users? − Data market?
POST-PRIVACY
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STREAMS: LINKED DATA EVERYWHERE
INTERNET OF THINGS
MIDDLEWARE
DATABASES
CLOUD
LIN
KED
DAT
A
Semantic descriptions of sensors, streams, events, observations, etc.
Web protocols down to the sensor level
SPARQL-like access to streams and sensors
Scalable data management
Scalable infrastructure framework
Reasoning with streams
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W3C SSN XG
− Semantic Sensor Networks ontology to describe sensors and sensor data
− Semantic annotations for OGC’s SWE Sensor Model Language
− Motivations − No existing sensor ontology included all the basic concepts − Ease integration of (some) semantics in more spread languages and
standards (specifically SensorML)
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SSN APPLICATION: SPITFIRE
•DUL: DOLCE+DnS Ultralite •EventF: Event-Model F
•SSN: SSN-XG •CC: Contextualised-Cognitive
Concepts on sensor network topology and devices
Concepts on sensor role, events, sensor project
Event Datasets
Sensor Datasets
LOD Cloud
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COAP = HTTP for sensors
− Standardisation
− Physical: 802.15.4 − Network: IEEE 6LoWPAN, ROLL − Service layer:
− IETF CoRE (Constrained RESTful Environments): CoAP protocol + extensions
− Encoding (Extensible XML interchange - EXI, SensorML)
− Ontologies
− CoAP = Constrained Application Protocol − IETF draft, http://tools.ietf.org/id/coap − Core proposal + > 17 extensions
RESTFUL SENSOR INTERFACES
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COAP EXAMPLE
Accessing sensors from we browser using HTTP-CoAP proxying – SPITFIRE Smart Service Proxy (SSP)
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YET ANOTHER FORM OF LINKED DATA
INTERNET OF THINGS
MIDDLEWARE
DATABASES
CLOUD
LIN
KED
DAT
A
Semantic descriptions of sensors, streams, events, observations, etc.
Web protocols down to the sensor level
SPARQL-like access to streams and sensors
Scalable data management
Scalable infrastructure framework
Reasoning with streams
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KISS EXTENDED
Application
CQELS SPARQL REST
Linked Data
COAP
Sensors Virtual
Sensors
Linked Streams GSN
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CONTINUOUS QUERY EVALUATION OVER LINKED STREAMS
Scalable processing model for unified Linked Stream Data and Linked Open Data
Combines data pre-processing and an adaptive cost-based query optimization algorithm
[SSN 2009, SSN 2010, ISWC 2011, ISWC 2013]
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:dublinAirport
:aHumidity
:aTemperature
:weatherStation
:latestWeather
:readings
:humidValue :tempValue
“18”^xsd:float “Celcius” “60”^xsd:float “%”
ssn:featureOfInterest
ssn:observedBy
ssn:observes ssn:observes
ssn:isPropertyOf ssn:isPropertyOf
ssm:observedPropery ssm:observedPropery
ssm:value ssm:value ssm:unit ssm:unit
ssn:hasValue ssn:hasValue
ssn:observationResult
Sensor metadata
Stream data snapshot at 2011-07-08T21:32:52
LINKED STREAM MODEL
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Query rewriter
Orchestrator
Data transformation
SPARQ
L-like
Optimizer
Operator implementations
Access methods
Executor
Query
Execution
Overhead
BLACK BOX APPROACH
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SCALING IT: CQELS CLOUD = CQELS + EC2 + STORM
https://code.google.com/p/cqels/wiki/CQELSCloud [ISWC 2013]
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YET ANOTHER FORM OF LINKED DATA
INTERNET OF THINGS
MIDDLEWARE
DATABASES
CLOUD
LIN
KED
DAT
A
Semantic descriptions of sensors, streams, events, observations, etc.
Web protocols down to the sensor level
SPARQL-like access to streams and sensors
Scalable data management
Scalable infrastructure framework
Reasoning with streams
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CQELS SPARQL REST
Linked Data
COAP
Sensors Virtual
Sensors
Linked Streams
SCALABLE INFRASTRUCTURE
Application
Middleware
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SUPER STREAM COLLIDER
− The SSC is a platform provides a web-based interface and tools for building sophisticated mash-ups combining semantically annotated Linked Stream and Linked Data sources into easy to use resources for applications.
− Drag & drop editor − CQELS/SPARQL visual editor − WebSocket stream publisher
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STREAMS: YET ANOTHER FORM OF LINKED DATA
INTERNET OF THINGS
MIDDLEWARE
DATABASES
CLOUD
LIN
KED
DAT
A
Semantic descriptions of sensors, streams, events, observations, etc.
Web protocols down to the sensor level
SPARQL-like access to streams and sensors
Efficient data management
Scalable infrastructure framework
Reasoning with streams
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RDF Files (e.g. maps)
Sensor Streams
• Transform • Synchronize • Buffer
oClingo CQELS
Application
STREAM REASONING
[RR 2013]
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SMART CITY PROJECTS USING THIS STACK
Open Source IoT Architectural Blueprint http://www.openiot.eu/ https://github.com/OpenIotOrg/openiot
Real-Time IoT Stream Processing and Large-scale Data Analytics for Smart Cities http://www.ict-citypulse.eu/
Smart, secure and cost-effective integrated IoT deployments in smart cities http://vital-project.eu/
Behaviour-driven Autonomous Services for smart transportation in smart cities http://gambas-ict.eu/
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STANDARDIZATION
oneM2M Community Industrial Groups • Linked Data and Semantic Annotation (SSN-XG) • Information Exchange and Interoperability
World Wide Web Community • Semantic Sensor Networks (SSN-XG) • RDF Stream Processing (RSP-XG) • Enable user to configure, deploy, use IoT based services
Internet Connected
Objects
Machine to Machine
Semantic Web &
Linked Data
European Research Cluster for the Internet of Things • Coordination on Service Openness and Interoperability – AC4 • Naming and Labeling Activity Cluster – AC2
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CONCLUSIONS
We must be prepared to operate in a new environment 1. Everything will be networked and linked
2. Not everything will be connected directly
3. There will be no „one size fits all“ architecture
4. Graphs will be the dominating data model
5. Streams will replace static data
6. Semantics will be essential (open world)
7. Simple abstractions will be essential
8. The importance of Linked Data will grow
9. Data products will dominate
10. Geographic data will be at the center
© Fraunhofer FOKUS