Ontology-based Stream/Sensor Data

Modeling

Presented by:Ashraf Heydari

Supervisor:Dr. Kahani

2

Outline•Introduction & Motivation•Approach

▫Ontology Model ▫URI Definition▫SPARQL Extensions ▫Example

•Conclusions•References

3

Introduction & Motivation

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Sensor Networks• Increasing availability of cheap, robust, deployable

sensors as ubiquitous information sources• Dynamic and reactive, but noisy, and unstructured

data streams

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Different Kinds of Sensors

Camera Sensors

Satellite Sensors

GPS Sensors

Sensor Dataset

Weather Sensors

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The Sensor Web• Universal, web-based access to sensor

data

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Streaming Data• Continuously appended data• Potentially infinite• Time-stamped tuples• Continuous queries• Changes of values over time• Latest used in queries

(t9, a1, a2, ... , an)(t8, a1, a2, ... , an)(t7, a1, a2, ... , an)......(t1, a1, a2, ... , an)......

Streaming Data

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A Set of Challenges in Sensor Data Management•Provisioning

▫Complexity of acquisition: distributed sources, data volumes

▫Pre-processing incoming data▫Tools for data ingestion needed

•Spatial/temporal•Analysis, modeling

▫Discovery: identify sources, metadata▫Data quality: faulty data, loss, estimates▫Analysis models ▫Republish analytic results▫Workflows for data stream processing

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A Set of Challenges in Sensor Data Management•Interoperability

▫Data aggregation/integration•Uncertainty, data quality

▫Noise, failures, measurement errors, confidence, trust

• Distributed processing ▫High volume, time critical▫Fault-tolerance▫Load management ▫Stream processing features▫Continuous queries▫Live & historical data

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A Set of Challenges in Sensor Data Management•Interoperability

▫Data aggregation/integration•Uncertainty, data quality

▫Noise, failures, measurement errors, confidence, trust

• Distributed processing ▫High volume, time critical▫Fault-tolerance▫Load management ▫Stream processing features▫Continuous queries▫Live & historical data

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A Semantic Perspective on These Challenges•Sensor data model representation and management

▫For data publication, integration and discovery▫Bridging between sensor data and ontological

representations for data integration▫Ontologies: Observations and measurements, time

series, etc.▫Event models

•Sensor data querying and (pre-)processing▫Data heterogeneity▫Data quality▫New inference capabilities required to deal with

sensor information•User interaction with sensor data

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Semantic Sensor Web/ Linked Stream-Sensor Data (LSD)

•A representation of sensor/stream data following the standards of Linked Data▫Adding semantics allows the search and

exploration of sensor data without any prior knowledge of the data source

▫Using the principles of Linked Data facilitates the integration of stream data to the increasing number of Linked Data collections

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Semantic Sensor Web/ Linked Stream-Sensor Data (LSD)

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Some Examples• Meteorological data in

Spain: automatic weather stations▫http://aemet.linkeddata.

es/

• Live sensors in Slovenia▫http://sensors.ijs.si/

• Channel Coastal Observatory in Southern UK▫http://webgis1.geodata.s

oton.ac.uk/flood.html

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Approach

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How to Deal with Linked Stream/Sensor Data•An ontology model•URI definition•SPARQL extensions

▫To handle time and tuple windows

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SSN Ontologies. HistorySeveral efforts since approx. 2005In 2009, a W3C incubator group was

started, which has just finishedOntology: http://purl.oclc.org/NET/ssnx/ssnA good number of internal and external

references to SSN OntologySSN Ontology paper submitted to Journal of

Web Semantics

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Overview of The SSN Ontology Modules

Skeleton

Device

Deployment

PlatformSite

System

Process

ConstraintBlockMeasuringCapability

OperatingRestriction

Data

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Overview of The SSN Ontologies

Skeleton

Device

Deployment

PlatformSite

System

System

onPlatform only

hasSubsystem only, some SurvivalRange

hasSurvivalRange only

OperatingRangehasOperatingRange only

hasDeployment only

DeploymentRelatedProcess

Deployment

deploymentProcesPart only

deployedSystem only

Platform

deployedOnPlatform only

attachedSystem only

Device

Sensor

SensingDevice

Sensing

implements some

observes only

hasMeasurementCapability only

inDeployment only

SensorInput

detects only

isProxyFor onlyObservationValu

e

SensorOutput

hasValue some

isProducedBy some

Process

Process

hasInput only

hasOutput only, some

Input

Output

Observation

observedBy only

featureOfInterest only

observationResult only

Property

observedProperty onlyhasProperty only, some

isPropertyOf some

sensingMethodUsed only

includesEvent some

FeatureOfInterest

ConstraintBlock

Condition

inCondition only

MeasuringCapability

MeasurementCapability

forProperty only

OperatingRestriction

inCondition only

Data

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SSN Ontology. Sensor and Environmental Properties

CommunicationMeasuringCapability

MeasurementCapability

MeasurementProperty

hasMeasurementProperty only

Accuracy

DetectionLimit

Drift

Frequency

MeasurementRange

Precision

Resolution

ResponseTime

Selectivity

Sensitivity

Latency

Skeleton

EnergyRestrictionOperatingRestriction

OperatingRange

OperatingProperty

hasOperatingProperty only

EnvironmentalOperatingProperty

MaintenanceSchedule

SurvivalRange

SurvivalProperty

hasSurvivalProperty only

EnvironmentalSurvivalProperty

SystemLifetime

BatteryLifetime

OperatingPowerRange

Property

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A Usage Example

SWEET

Service

Coastal Defences

Ordnance Survey

Additional

Regions

Role

DOLCE UltraLite

Schema

FOAF

Upper

External

SSG4Env infrastructure

Flood domain

SSN

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How to Deal with Linked Stream/Sensor Data•An ontology model•URI definition•SPARQL extensions

▫To handle time and tuple windows

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URI Definition•No clear practices yet•We have to identify…

▫Sensors▫Features of interest▫Properties▫Observations

•Debate between being observation or sensor-centric▫Observation-centric seems to be the winner

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How to Deal with Linked Stream/Sensor Data•An ontology model•URI definition•SPARQL extensions

▫To handle time and tuple windows

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SPARQLStream

Example:“provide me with the wind speed observations over the last

minute in the Solent Region ”

...

...( <si-1,pi-1, oi-1>, ti-1 ),( <si, pi, oi>, ti ),( <si+1,pi+1, oi+1>, ti+1 ),......

cd:Observation

xsd:double

cd:observationResult......( <ssg4e:Obs1,rdf:type, cd:Observation>, ti ),( <ssg4e:Obs1,cd:observationResult,”34.5”>, ti ),( <ssg4e:Obs2,rdf:type, cd:Observation>, ti+1 ),( <ssg4e:Obs2,cd:observationResult,”20.3”>, ti+1 ),......

STREAM <http://www.semsorgrid4env.eu/ccometeo.srdf>

RDF-Stream

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SPARQLStreamExample:“provide me with the wind speed observations over the last

minute in the Solent Region ”

cd:Observation

xsd:double

cd:observationResult

PREFIX cd: <http://www.semsorgrid4env.eu/ontologies/CoastalDefences.owl#>PREFIX sb: <http://www.w3.org/2009/SSN-XG/Ontologies/SensorBasis.owl#> PREFIX rdf: <http://www.w3.org/1999/02/22-rdf-syntax-ns#> SELECT ?windspeed ?windts FROM STREAM <http://www.semsorgrid4env.eu/ccometeo.srdf> [ NOW – 1 MINUTE TO NOW – 0 MINUTES ] WHERE { ?WindObs a cd:Observation; cd:observationResult ?windspeed; cd:observationResultTime ?windts; cd:observedProperty ?windProperty; cd:featureOfInterest ?windFeature. ?windFeature a cd:Feature; cd:locatedInRegion cd:SolentCCO. ?windProperty a cd:WindSpeed. }

cd:Feature

cd:featureOfInterest

cd:Property

cd:observedProperty

cd:locatedInRegion

cd:Region

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Queries to Sensor/Stream DataSNEEqlRSTREAM SELECT id, speed, direction FROM wind[NOW];

Streaming SPARQLPREFIX fire: <http://www.semsorgrid4env.eu/ontologies/fireDetection#>SELECT ?sensor ?speed ?directionFROM STREAM <http://…/SensorReadings.rdf> WINDOW RANGE 1 MS SLIDE 1 MSWHERE { ?sensor a fire:WindSensor; fire:hasMeasurements ?WindSpeed, ?WindDirection. ?WindSpeed a fire:WindSpeedMeasurement; fire:hasSpeedValue ?speed; fire:hasTimestampValue ?wsTime. ?WindDirection a fire:WindDirectionMeasurement; fire:hasDirectionValue ?direction; fire:hasTimestampValue ?dirTime. FILTER (?wsTime == ?dirTime)}

C-SPARQLREGISTER QUERY WindSpeedAndDirection ASPREFIX fire: <http://www.semsorgrid4env.eu/ontologies/fireDetection#>SELECT ?sensor ?speed ?directionFROM STREAM <http://…/SensorReadings.rdf> [RANGE 1 MSEC SLIDE 1 MSEC]WHERE { …

SPARQL-STR v1SELECT ?waveheightFROM STREAM <www.ssg4env.eu/SensorReadings.srdf> [FROM NOW -10 MINUTES TO NOW STEP 1 MINUTE]WHERE { ?WaveObs a sea:WaveHeightObservation; sea:hasValue ?waveheight; }

Query translation

Query ProcessingCl

ient Stream-to-Ontology

mappings

SPARQLStream

[tuples]

Sensor Network

Data translation[triples]

SNEEql

conceptmap-def WaveHeightMeasurement virtualStream <http://ssg4env.eu/Readings.srdf> uri-as concat('ssg4env:WaveSM_', wavesamples.sensorid,wavesamples.ts) attributemap-def hasValue operation constant has-column wavesamples.measured dbrelationmap-def isProducedBy toConcept Sensor joins-via condition equals has-column sensors.sensorid has-column wavesamples.sensorid

conceptmap-def Sensor uri-as concat('ssg4env:Sensor_',sensors.sensorid) attributemap-def hasSensorid operation constant has-column sensors.sensorid

S2O Mappings

SELECT measured FROM wavesamples [NOW -10 MIN]

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SPARQL-STR v229

Query translation

Query EvaluatorC

lient Stream-to-Ontology

Mappings (R2RML)

SPARQLStream

[tuples]

Stream Engine (S3)

Ontology-based Streaming Data Access Service

Relational DB (S2)

Sensor Network (S1)

RDF Store (Sm)Data

translation[triples]

SNEEql, GSN API

GSN

SwissEx30

•Global Sensor Networks, deployment for SwissEx.

•Distributed environment: GSN Davos, GSN Zurich, etc.▫In each site, a number of sensors available▫Each one with different schema

•Metadata stored in wiki▫Federated metadata management

Getting things done31

•Transformed wiki metadata to SSN instances in RDF

•Generated R2RML mappings for all sensors•Implementation of Ontology-based querying

over GSN•Fronting GSN with SPARQL-Stream queries•Numbers:

▫28 Deployments▫Aprox. 50 sensors in each deployment▫More than 1500 sensors▫Live updates. Low frequency▫Access to all metadata/not all data

Sensor Metadata32

station

location

model

sensors

properties

Sensor Data: Observations33

• GSN (Global Sensor Networks) is a database software middleware designed to facilitate the deployment and programming of sensor networks. 

• The software takes data (either directly from a sensor or from a CSV file), enters it into a database and provides a web-based query interface.

• It is completely generalised and able to handle sensors of all types.

SPARQL-STR + GSN34

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Conclusions

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Conclusions• Sensor data is yet another good source of data with

some special properties

• Everything that we do with our relational datasets or other data sources can be done with sensor data

• Adding semantics allows the search and exploration of sensor data without any prior knowledge of the data source

• Using the principles of Linked Data facilitates the integration of stream data to the increasing number of Linked Data collections

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References

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References• Semantic Sensor Network XG Final Report, W3C Incubator Group Report 28 June 2011,

http://www.w3.org/2005/Incubator/ssn/XGR-ssn-20110628/• K. Janowicz and M. Compton 

The Stimulus-Sensor-Observation Ontology Design Pattern and its Integration into the Semantic Sensor Network Ontology. In The 3rd International workshop on Semantic Sensor Networks 2010 (SSN10) in conjunction with the 9th International Semantic Web Conference (ISWC 2010), 2010.

• P. Barnaghi, S. Meissner and M. Presser Sense and sensability: Semantic data modelling for sensor networks. In Proceedings of the ICT Mobile Summit 2009, pp. 1-9, 2009.

• M. Compton, C. Henson, H. Neuhaus, L. Lefort and A. Sheth A Survey of the Semantic Specification of Sensors. In Proceedings of the 2nd International Workshop on Semantic Sensor Networks (SSN09) at ISWC 2009, pp. 17-32, 2009.

• M. Compton, H. Neuhaus, K. Taylor and K. Tran Reasoning about Sensors and Compositions. In Proceedings of the 2nd International Workshop on Semantic Sensor Networks (SSN09) at ISWC 2009, pp. 33-48, 2009.

• P. Barnaghi and M. Presser Publishing Linked Sensor Data. In The 3rd International workshop on Semantic Sensor Networks 2010 (SSN10) in conjunction with the 9th International Semantic Web Conference (ISWC 2010), 2010.

• A. Gray, J. Sadler, O. Kit, K. Kyzirakos, M. Karpathiotakis, J. Calbimonte, K. Page, R. Garc´ıa-Castro, A. Frazer, I. Galpin, A. Fernandes, N. Paton, M. Koubarakis, D. De Roure, K. Martinez, A. G´omez-P´erez. A Semantic Sensor Web for Environmental Decision Support Applications. In Sensors 11, no. 9, 2011.

• R. Garcà a Castro, C. Hill and O. Corcho Sensor network ontology suite v2. Deliverable D4.3v2, SemSorGrid4Env SemSorGrid4Env: Semantic Sensor Grids for Rapid Application Development for Environmental Management, 2011.

• H. Neuhaus , M. Compton The Semantic Sensor Network Ontology: A Generic Language to Describe Sensor Assets. In AGILE Workshop Challenges in Geospatial Data Harmonisation, 2009.

• D.F.Barbieri, D.Braga, S.Ceri, E.Della Valle, M.Grossniklaus Querying RDF Streams with C-SPARQL . In SIGMOD Record, 2010.

• D.F.Barbieri, D.Braga, S.Ceri, E.Della Valle, M.Grossniklaus C-SPARQL: SPARQL for continuous querying. In: WWW '09, 2009.

• A. Salehi, M. Riahi, S. Michel, and K. Aberer. GSN, Middleware for Streaming World (Best Demo Award). NCCR-MICS, NCCR-MICS/CL4, 2009. 

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