Fraunhofer Fokus ESPRI powerpoint...

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

INFORMATION SOURCES

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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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A connected digital layer!

Silos

A CONNECTED DIGITAL LAYER?

economic There are

arguments

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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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IN FACT … IT’S MUCH BIGGER ALREADY

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“You are in Berlin at the I4T workshop.”

WHY LINKED DATA / LINKED STREAMS?

Challenges

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LINKING: A GENERAL PARADIGM

− Thematic − Technological − Scientific − Organizational

− Networks − Data − Applications − People

− Spezialized − Secure − Efficient

BASIC Graphs will be the

representation

“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

links with static data

…and it

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Cloud, Fog & Inter-Cloud

© Fraunhofer FOKUS

CENTRALIZED VS. AUTONOMOUS

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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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MULTI-DISCIPLINARY PROBLEM


EVERYTHING CHANGES

Streams

Graphs

Provenance

Statistics

A few solutions

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REST

KEEP IT SMALL AND SIMPLE!

LOD

Application

Application := Data + Services

SSN CoAP CoRE

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STREAMS: LINKED DATA EVERYWHERE

INTERNET OF THINGS

MIDDLEWARE

DATABASES

CLOUD

LIN

KED

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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)

SS SEMANTIC SENSOR NETWORKS ONTOLOGY

[Journal of Web Semantics 2012]

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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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SPITFIRE VOCABULARY

http://www.spitfire-project.eu [IEEE ComMag 2011]

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SIZE MATTERS!

• OS + 6LowPAN + CoAP + Semantic description < 48kB? • Processing power?

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STORAGE REQUIREMENTS

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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 = HTTP FOR SENSORS

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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







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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







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CQELS SPARQL REST

Linked Data

COAP

Sensors Virtual

Sensors

Linked Streams

SCALABLE INFRASTRUCTURE

Application

Middleware

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LINKED STREAM MIDDLEWARE

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LSM: LIVE TRAIN INFO


GRAPHOFTHINGS.ORG

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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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SSC DEVELOPMENT TOOLS

http://superstreamcollider.org/

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STREAMS: YET ANOTHER FORM OF LINKED DATA

INTERNET OF THINGS

MIDDLEWARE

DATABASES

CLOUD

LIN

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DAT

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Efficient data management



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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/

http://www.openiot.eu/

https://github.com/OpenIotOrg/openiot

http://www.ict-citypulse.eu/

http://vital-project.eu/

http://gambas-project.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

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