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City Pulse: 609035 1 st Annual Review – Brussels, 19 th November 2014 1 CityPulse: Real-Time IoT Stream Processing and Large-scale Data Analytics for Smart.

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Presentation on theme: "City Pulse: 609035 1 st Annual Review – Brussels, 19 th November 2014 1 CityPulse: Real-Time IoT Stream Processing and Large-scale Data Analytics for Smart."— Presentation transcript:

1 City Pulse: st Annual Review – Brussels, 19 th November CityPulse: Real-Time IoT Stream Processing and Large-scale Data Analytics for Smart City Applications CityPulse- Small or medium-scale focused research project (STREP) project 1 st Annual Review – 19 th November 2014

2 Outline of this session An introduction to Linked Data concepts and how and why they should be used in the context of Smart Cities and IoT related studies. Using practical examples we will explore what data sets are already available in cities and how they can be used to answer questions in the context of Smart Cities. We will also explore how signal processing tools and results can be described as structured data. We will demonstrate how Smart City related sensory structured data can be validated with an SSN Validator. Finally, we will demonstrate tools and best practice for researchers who wish to publish their own data sets on the Semantic Web in a Linked Data fashion.

3 Virtualisation of Smart City and IoT data Data in smart cities is collected by sensor devices and also crowd sensing sources. The data is time and location dependent. It can be noisy and the quality can vary. It is continuous - streaming data Overall CityPulse information models focus on developing a framework in the scope of the CityPulse project for real-time IoT stream annotation that employs a knowledge-based approach to represent data streams and to support mashups.

4 CityPulse Arhitecture WP3- Large Scale Data Analysis Component The Information Flow of WP3

5 CityPulse Information Models Workflow Describing a stream annotation work flow using the Stream Annotation Ontology (SAO), Complex Event Service Ontology, and Quality Ontology. Less complexity vs Expressibility Existing vocabulares: W3C SSN, Prov, OWL-S

6 Stream Annotation Ontology (SAO) The SAO allows representation of aggregated stream data and temporal characteristics. It is based on the SSN Ontology and Timeline Ontology.

7 Quality Ontology (QO) The Quality Ontology is used to represent the quality of information for data streams in smart cities

8 Complex Event Service Ontology (CES) Overview of complex event service ontology

9 What can we do with these ontologies now? Describing sensory data, quality, and events on multiple timelines, linking together IoT objects...and well, that's about all we need! The CityPulse Ontologies subsumes all these ontologies to deal with smart city-related information

10 Exemplification The following is an exemplification of an annotated stream based on SAO Ontology, where it describes a traffic observation of a sensor feature called, "Average Speed", for a pair of sensors that are provided by the City of Aarhus via Open Data Aarhus Platform.

11 Progress and Results - Activity 3.3 A visual representation of geographical coordinates on Google Map for a pair of road traffic sensors provided by city of Aarhus, Denmark.

12 Traffic data annotation (1) - Sensor prefix ssn:. prefix tl:. prefix sao:. prefix ct:. :cityofaarhus a foaf:Organisation, prov: Agent. a ces:PrimitiveEventService, ssn:Sensor ; ssn:observes,,,, ; prov: wasAttributedTo :cityofaarhus.

13 Traffic data annotation – Sensor feature of interest (2) ct:hasFirstNode [ a ct:Node ; ct:hasCity "Hinnerup"^^xsd:string ; ct:hasLatitude " "^^xsd:double ; ct:hasLongtitude " "^^xsd:double ; ct:hasStreet "Ãrhusvej"^^xsd:string ] ; ics:hasSecondNode [ a ct:Node ; ct:hasCity "Hinnerup"^^xsd:string ; ct:hasLatitude " "^^xsd:double ; ct:hasLongtitude " "^^xsd:double ; ct:hasStreet "Ãrhusvej"^^xsd:string ].

14 Traffic data annotation – Sensor Property (3) :Property-001 a ct:Measured_Time ; ssn:isPropertyOf :FoI-001. :Property-002 a ct:Estimated_Time; ssn:isPropertyOf :FoI-001. :Property-003 a ct:Congestion_Level; ssn:isPropertyOf :FoI-001. :Property-004 a ct:Vehicle_Count; ssn:isPropertyOf :FoI-001. :Property-005 a ct:Average_Speed; ssn:isPropertyOf :FoI-001.

15 Traffic data annotation – Sensor Observation (4) a sao:Point, ssn:Observation ; sao:hasUnitOfMeasurement ; sao:value "141.0"^^xsd:double ; sao:time [ a tl:Instant ; tl:at " T06:00:00"^^xsd:dateTime ; tl:duration "PT5M"^^xsd:duration ] ; ssn:observedBy ; ssn:observedProperty. prov:wasAsscoatedWith :cityofaarhus.

16 Representation of Aggregated Sensory Data Source: S. Kolozali, M. Bermudez-Edo, D. Pushmann, P. Barnaghi, “A Knowledge-based Approach for Real-Time IoT Data Stream Annotation and Processing“, in Proc. of IEEE International Conference on Internet of Things (iThings 2014), Sep. tl:. :government a foaf:Organisation, prov: Agent. :sefki a foaf:Person, prov:Agent ; foaf:givenName "Sefki" ; foaf:mbox prov:actedonBehalfOf :ccsrSurrey ;. :sensorRec1 a sao:StreamData, ssn:SensorObservation ; prov: wasAttributedTo :government. :sensorRec2 a sao:StreamData, ssn:SensorObservation ; prov: wasAttributedTo :government. :traffic-sensor-recording-619 a sao:StreamEvent ; prov:used [ a sensorRec1; sensorRec2] ; sao:time [a tl:Interval; tl:at " T08:25:00"^^xsd:dateTime; tl:duration "PT15H30M"^^xsd:duration; ] ; prov:wasAsscoatedWith :sefki ;. :freshness-traffic-619 a qoi:Freshness ; qoi:value " T08:25:00"^^xsd:dateTime. :sax_AverageSpeedSample a SymbolicAggregateApproximation; rdfs:label "The sax representation of the traffic sensor recording obtained from Aarhus City."; sao:value "bbbbacdd"; sao:alphabetsize "4"^^xsd:int ; sao:segmentsize "8"^^xsd:int ; prov:wasGeneratedBy traffic-sensor-recording-619; qoi:hasQoI freshness-traffic-619. R eal time average speed data obtained from a pair of sensor points is mapped into SAX word, ”bbbbacdd”, with the segment size of “8” and alphabet size of “4” for 176 samples. An excerpt from an RDF data annotated for a set of sensor recordings based on Stream Annotation Ontology.

17 Reference Datasets (1) 17

18 Reference Datasets (2) ~120GB Vehicle traffic - Aarhus Pollution Measurement - Aarhus Weather Data - Aarhus Social media (Twitter) - Aarhus Webcasted events – Surrey Cultural Events - Aarhus

19 Hands on Session with SAOPY

20 Hands on session with SSN validation service To check RDF descriptions To enable user to validate an ontology or Linked Data Undefined classes and properties Poorly formed namespaces Problematic prefixes Literal syntax validation and other optional heuristics Validation CityPulse The Validation of Sensory Linked Data Source: S. Kolozali, T. Elsaleh, P. Barnaghi, “A Validation Tool for the W3C SSN Ontology based Sensory Semantic Knowledge“, in Proc. of the 13th International Semantic Web Conference (ISWC), Oct

21 City Pulse: st Annual Review – Brussels, 19 th November 2014 Sefki Kolozali, Maria Bermudez-Edo, Daniel Puschmann, Frieder Ganz, Payam Barnaghi, "A Knowledge-based Approach for Real-Time IoT Data Stream Annotation and Processing", in Proc. of the 2014 IEEE International Conference on Internet of Things (iThings 2014), Taipei, Taiwan, September Kolozali S, Elsaleh T, Barnaghi P. (2014) “A Validation Tool for the W3C SSN Ontology based Sensory Semantic Knowledge”. The 13th International Semantic Web Conference Stefan Bischof, Athanasios Karapantelakis, Cosmin-Septimiu Nechifor, Amit Sheth, Alessandra Mileo and Payam Barnaghi, "Semantic Modeling of Smart City Data", Position Paper in W3C Workshop on the Web of Things: Enablers and services for an open Web of Devices, June 2014, Berlin, Germany. R. Tönjes, P. Barnaghi, M. Ali, A. Mileo, M. Hauswirth, F. Ganz, S. Ganea, B. Kjærgaard, D. Kuemper, S. Nechifor, D. Puiu, A. Sheth, V. Tsiatsis, L. Vestergaard, "Real Time IoT Stream Processing and Large-scale Data Analytics for Smart City Applications", poster session, European Conference on Networks and Communications References


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