LINKING EVERYTHING HEADLINE SUBHEADLINE. Manfred Hauswirth TU Berlin, Open Distributed Systems & Fraunhofer FOKUS. Matthias Heyde / Fraunhofer FOKUS

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1 LINKING EVERYTHING HEADLINE SUBHEADLINE Matthias Heyde / Fraunhofer FOKUS Manfred Hauswirth TU Berlin, Open Distributed Systems & Fraunhofer FOKUS

2 A NETWORK OF EVERYTHING Interconnected Universal All encompassing assists humans, organisations and systems and things with problem solving enabling innovation and increased productivity 2

3 LINKING ON ALL LAYERS Internet of Everything Web of things, data and services e.g. interconnected processes, products and data Quelle: de.colourbox.com Logistik Cyber Physical Systems Intelligent, networked systems e.g. intelligent, predictive maintenance of machinery Networked embedded systems e.g. sensor networks for condition monitoring Embedded systems e.g. sensor systems IZM Source: Agenda CPS, Acatech Studie Fraunhofer

4 The Internet of Everything People People to People (P2P) People to Machine (P2M) Home Process Mobile Things Data Source: Cisco Whitepaper 2013 Business Machine to Machine (M2M) Connecting the Unconnected 4

5 INFORMATION SOURCES

6 UNTAPPED VALUE Source: Information Marketplaces - The New Economics of Cities, The Climate Group, Arup, Accenture, Horizon,

7 A connected digital layer! A CONNECTED DIGITAL LAYER? Silos 7

8 There are economic arguments

9 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 Cisco Whitepaper,

10 Your biggest competitor: Do nothing Adopting a sit and wait attitude towards digitization has made even established market players disappear. (Quelle, Kodak) Bosch Whitepaper,

11 THAT S WHY WE NEED LINKED DATA 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. 11

12 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 machinecomprehensible way give an exploitable meaning to the data 12

13 WHY GRAPHS AND ONTOLOGIES? Cities:Dublin Wikipedia.org Geo:hasCapital 84421km2 Geo:area Geo:hasLargestCity Geo:IslandOfIreland Geo:locatedOn EU:RepublicOfIreland Gov.ie Gov:hasTaoiseach Person:EndaKenny EU:RepublicOfIreland Gov:hasDepartment IE:DepartmentOfFinance 13

14 LINQUA FRANCA: LINKED OPEN DATA Media BestBuy Overstock.com Facebook US government UK government User-generated Government Publications BBC New York Times Geo Cross-domain Life sciences LinkedGeoData Over 200 open data sets with more than 25 billion facts, interlinked by 400 million typed links, doubling every 10 month Linking Open Data cloud diagram, by Richard Cyganiak and Anja Jentzsch. 14

15 IN FACT IT S MUCH BIGGER ALREADY 15

16 WHY LINKED DATA / LINKED STREAMS? You are in Berlin at the I4T workshop. 16

17 Challenges

18 LINKING: A GENERAL PARADIGM Networks Data Applications People Thematic Technological Scientific Organizational Spezialized Secure Efficient 18

19 Graphs will be the BASIC representation

20 LOTS OF DATA 90% of the data in the world today has been created in the last two years alone IBM The bringing together of a vast amount of data from public and private sources [ ] is what Big Data is all about IDC 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 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

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22 and it links with static data

23 CENTRALIZED VS. AUTONOMOUS Cloud, Fog & Inter-Cloud 23 Fraunhofer FOKUS

24 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 24

25 POST-PRIVACY 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? 25

26 MULTI-DISCIPLINARY PROBLEM 26 Fraunhofer FOKUS

27 EVERYTHING CHANGES Streams Graphs Provenance Statistics 27 Fraunhofer FOKUS

28 A few solutions

29 KEEP IT SMALL AND SIMPLE! Application := Data + Services Application LOD REST SSN CoAP CoRE 29

30 STREAMS: LINKED DATA EVERYWHERE Reasoning with streams LINKED DATA CLOUD DATABASES MIDDLEWARE INTERNET OF THINGS Web protocols down to the sensor level Scalable infrastructure framework Scalable data management SPARQL-like access to streams and sensors Semantic descriptions of sensors, streams, events, observations, etc. 30

31 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) 31

32 SS SEMANTIC SENSOR NETWORKS ONTOLOGY [Journal of Web Semantics 2012]

33 SSN APPLICATION: SPITFIRE DUL: DOLCE+DnS Ultralite EventF: Event-Model F SSN: SSN-XG CC: Contextualised-Cognitive Concepts on sensor network topology and devices Sensor Datasets Event LOD Cloud Datasets Concepts on sensor role, events, sensor project 33

34 SPITFIRE VOCABULARY [IEEE ComMag 2011] 34

35 SIZE MATTERS! OS + 6LowPAN + CoAP + Semantic description < 48kB? Processing power? 35

36 STORAGE REQUIREMENTS 36

37 RESTFUL SENSOR INTERFACES COAP = HTTP for sensors Standardisation Physical: 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, Core proposal + > 17 extensions 37

38 COAP = HTTP FOR SENSORS 38

39 COAP EXAMPLE Accessing sensors from we browser using HTTP-CoAP proxying SPITFIRE Smart Service Proxy (SSP) 39

40 YET ANOTHER FORM OF LINKED DATA Reasoning with streams LINKED DATA CLOUD DATABASES MIDDLEWARE INTERNET OF THINGS Web protocols down to the sensor level Scalable infrastructure framework Scalable data management SPARQL-like access to streams and sensors Semantic descriptions of sensors, streams, events, observations, etc. 40

41 KISS EXTENDED Application CQELS REST SPARQL GSN Virtual Sensors Linked Streams COAP Sensors Linked Data 41

42 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] 42

43 LINKED STREAM MODEL Sensor metadata ssn:observes :atemperature ssn:observedby ssn:ispropertyof :dublinairport ssm:observedpropery ssn:featureofinterest :weatherstation ssn:observes :ahumidity ssn:ispropertyof ssm:observedpropery :latestweather :tempvalue ssn:hasvalue ssn:observationresult ssn:hasvalue :humidvalue ssm:value ssm:unit :readings ssm:value ssm:unit 18 ^xsd:float Celcius 60 ^xsd:float % Stream data snapshot at T21:32:52 43

44 BLACK BOX APPROACH Query Optimizer Query rewriter Executor Operator implementations Execution Orchestrator Overhead Data transformation SPARQL-like Access methods 44

45 SCALING IT: CQELS CLOUD = CQELS + EC2 + STORM [ISWC 2013] 45

46 YET ANOTHER FORM OF LINKED DATA Reasoning with streams LINKED DATA CLOUD DATABASES MIDDLEWARE INTERNET OF THINGS Web protocols down to the sensor level Scalable infrastructure framework Scalable data management SPARQL-like access to streams and sensors Semantic descriptions of sensors, streams, events, observations, etc. 46

47 SCALABLE INFRASTRUCTURE Application CQELS Middleware REST SPARQL Linked Streams Linked Data Virtual Sensors COAP Sensors 47

48 LINKED STREAM MIDDLEWARE 48

49 LSM: LIVE TRAIN INFO 49

50 GRAPHOFTHINGS.ORG 50 Fraunhofer FOKUS

51 SUPER STREAM COLLIDER The SSC is a platform provides a webbased 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 51

52 SSC DEVELOPMENT TOOLS 52

53 SSC DEVELOPMENT TOOLS 53

54 SSC DEVELOPMENT TOOLS 54

55 STREAMS: YET ANOTHER FORM OF LINKED DATA Reasoning with streams LINKED DATA CLOUD DATABASES MIDDLEWARE INTERNET OF THINGS Web protocols down to the sensor level Scalable infrastructure framework Efficient data management SPARQL-like access to streams and sensors Semantic descriptions of sensors, streams, events, observations, etc. 55

56 STREAM REASONING RDF Files (e.g. maps) Transform Synchronize Buffer Application CQELS oclingo Sensor Streams [RR 2013] 56

57 SMART CITY PROJECTS USING THIS STACK Open Source IoT Architectural Blueprint Real-Time IoT Stream Processing and Large-scale Data Analytics for Smart Cities Smart, secure and cost-effective integrated IoT deployments in smart cities Behaviour-driven Autonomous Services for smart transportation in smart cities 57

58 STANDARDIZATION Semantic Web & Linked Data 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 European Research Cluster for the Internet of Things Coordination on Service Openness and Interoperability AC4 Naming and Labeling Activity Cluster AC2 Machine to Machine onem2m Community Industrial Groups Linked Data and Semantic Annotation (SSN-XG) Information Exchange and Interoperability 58

59 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 59 Fraunhofer FOKUS