publishing consuming linked sensor data meetup cuenca

Date: 01/12/2011

Publishing and Consuming Linked Sensor

Data

Jean-Paul Calbimonte

Ontology Engineering Group. Facultad de Informática, Universidad Politécnica de Madrid.

jp.calbimonte@upm.es

1st Latin American Linked Data MeetupCuenca, Ecuador

2

Linked Sensor Data 101

Linked Sensor Data

Motivation

Ingredients

Generate

Consume

Motivation

From Sensor Networks…

… to the Sensor Web/ Internet of Things…

… to Semantic Sensor Web and …

Linked Sensor Data

3

Sensors

4

http://www.flickr.com/photos/wouterh/2409251427/

• Cheaper• Ubiquitous• Robust• Routing

• Noisy• Processing• Memory• Energy(Limited)

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

Streaming Data

Background – Querying Relational Data Streams

5

Streaming DataSTREAMAurora/BorealisCougarTinyDBSNEE

e1

e2e1

e2 e3

e2e1 e3

e1

e4

t t+1 t+2 t+3 t+4 t+5

WINDOW [tnow-2 TO tnow] SLIDE 1

Transform infinite sequence of tuples to bounded bag

...

CQLSNEEqlTinyQL

Query engines

Query languages

SELECT attribute FROM stream [NOW -10 MIN]

Sensor Networks

Source: Antonis Deligiannakis

An example: SmartCities

7 SmartSantander Project

Environmental sensors

Parking sensors

Who are the end users of Sensor Networks?

Source: Dave de Roure

The climate change expert, or a simple citizen

Not only environmental, but many others…

9

Weather Sensors

Camera SensorsSatellite Sensors

GPS Sensors

Sensor Dataset

Source: H Patni, C Henson, A Sheth

10

The Sensor Web

Universal, web-based access to sensor data

Source: Adapted from Alan Smeaton’s invited talk at ESWC2009

Make sensors more accessible?

11Source: SemsorGrid4Env consortium

Should we care as computer scientists?

“Grand Challenge” CS issues:• Heterogeneity• Scale• Scalability• Autonomic behaviour• Persistence, evolution• Deployment challenges• Mobility

Source: Dave de Roure

Anything left for Semantic Web research?

Data from the Web

13Sensors, Mappings and Queries

Emergency planner

Flood risk alert: South East

England

forecastswave data Environmental

defenses

I have to make sense out of all this

data

Semantic Sensor Web / Linked Sensor Data (LSD)

A representation of sensor data following the standards of Linked Data

But what is Linked Data?

What is Linked Data?

15

An extension of the current Web…

data are given well defined and explicitly represented meaning

So that it can be shared and used By humans and machines

And clear principles on how to publish data

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The four principles (Tim Berners Lee, 2006)

http://www.ted.com/talks/tim_berners_lee_on_the_next_web.html

Use URIs as names of thingsUse HTTP URIsProvide useful information when URI is dereferencedLink to other URIs

Linked Open Data

17

http://richard.cyganiak.de/2007/10/lod/

2011

Semantic Sensor Web / Linked Sensor Data (LSD)

• Early references…• Sheth A, Henson C, and Sahoo S, Semantic Sensor Web, IEEE

Internet Computing, 2008.• Sequeda J, Corcho O. Linked Stream Data: A Position Paper.

Proceedings of the 2nd International Workshop on Semantic Sensor Networks, 2009.

• Le-Phuoc D, Parreira JX, Hauswirth M. Challenges in Linked Stream Data Processing: A Position Paper. Proceedings of the 3rd International Workshop on Semantic Sensor Networks, 2010.

A representation of sensor data following the standards of Linked Data

Let’s check 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.soton.ac.uk/flood.html

• And some more from DERI Galway, Knoesis, CSIRO, etc.

19

AEMET Linked Data

20

Observations

Sensors

JSI Sensors

21

Coastal Channel Observatory and other sources

22

• Work with Flood environmental sensor data.• SemSorGrid4Env project www.semsorgrid4env.eu.

Wave Height

Tidal Observations

Wind Speed

Motivation

23Enabling Ontology-based Access to Streaming Data Sources

Emergency planner

Flood risk alert: South East

England

...

...

...

Real-time data

Meteorological forecasts

Flood defences data

Other sources

• Detect conditions likely to cause a flood• Present data model in terms of the user domain: e.g. Flood risk

assessment

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

minute in the Solent region, if it is higher than the average of the last 2 to 3 hours”

Wave,Wind,Tide

On

tolo

gy

SPARQL

RDDF RDF

RDF

RDF

Ingredients for Linked Sensor Data

Core ontological modelAdditional domain ontologiesGuidelines for generation of identifiersSensor Web programming interfacesQuery processing engines

http://www.flickr.com/photos/santos/2252824606/

Sensor Metadata

25Sensors, Mappings and Queries

station

location

model

sensors

properties

Sensor Metadata

• What properties are measured

• Which sensors available

• Where are they located

• How are they configured

• Who is responsible

26Sensors, Mappings and Queries

Sensor Data: Observations

27Sensors, Mappings and Queries

Heterogeneity

Integration

Since aprox. 2005: Several proposalsProject specificReuse?Alignment?Best practices?

2009-2011: W3C SSN-XG incubator groupSSN Ontology: http://purl.oclc.org/NET/ssnx/ssn

Sensor Network Ontologies

Skeleton

Device

Deployment

PlatformSite

System

Process

ConstraintBlockMeasuringCapability

OperatingRestriction

Data

SSN ontology modules

Skeleton

Device

Deployment

PlatformSite

System

System

onPlatform only

hasSubsystem only, someSurvivalRang

e

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

Overview of the SSN ontologies

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

SSN Ontology: Measurement Capabilities

Core ontological model

A model to bind them all

• W3C SSN Ontology

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ssn:FeatureOfInterest

ssn:Observation

ssn:isProducedByssn:SensorOutput

ssn:Sensor

ssn:featureOfInterest

ssn:ObservationValue

ssn:Property

ssn:observedByssn:observationResult ssn:hasValue

ssn:hasProperty

ssn:observedProperty

ssn:observes

xsd:datatype

quantityValue

Example

swissex:Sensor1 rdf:type ssn:Sensor; ssn:onPlatform swissex:Station1; ssn:observes [rdf:type sweetSpeed:WindSpeed].

swissex:Sensor2 rdf:type ssn:Sensor; ssn:onPlatform swissex:Station1; ssn:observes [rdf:type sweetTemp:Temperature].

swissex:Station1 :hasGeometry [ rdf:type wgs84:Point;

wgs84:lat "46.8037166"; wgs84:long "9.7780305"].

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station

senso

r1

senso

r2

Example

swissex:WindSpeedObservation1 rdf:type ssn:Observation; ssn:featureOfInterest [rdf:type sweetAtmoWind:Wind]; ssn:observedProperty [rdf:type sweetSpeed:WindSpeed]; ssn:observationResult [rdf:type ssn:SensorOutput; ssn:hasValue [qudt:numericValue "6.245"^^xsd:double]]; ssn:observationResultTime [time:inXSDDatatime "2011-10-26T21:32:52"]; ssn:observedBy swissex:Sensor1 ;

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WindSpeed : 6.245

At: 2011-10-26T21:32:52

Usage: SSN & Domain Ontologies

SWEET

Service

Coastal Defences

Ordnance Survey

Additional Regions

Role

DOLCE UltraLite

Schema

FOAF

Upper

External

SSG4Env infrastructure

Flood domain

35

SSN

AEMET Ontology Network

• 83 classes• 102 object properties• 80 datatype properties• 19 instances

Additional domain ontologies

Examples: AWS, qu, Sweet

• http://www.w3.org/2005/Incubator/ssn/ssnx/meteo/aws

• http://www.w3.org/2005/Incubator/ssn/ssnx/qu/qu

• http://sweet.jpl.nasa.gov/

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Observed Properties

Features of Interest

Types of Sensors

Units of Measurement

Time

Ingredients for Linked Sensor Data

Core ontological modelAdditional domain ontologiesGuidelines for generation of identifiersSensor Web programming interfacesQuery processing engines

http://www.flickr.com/photos/santos/2252824606/

Good practices in URI Definition

Sorry, no clear practices yet…

Good practices in URI Definition

• URIs for:• Observations• Sensors• Features of interest• Properties• Time periods

• Debate: observation or sensor-centric?• Observation-centric seems to be the winner• Sensor-centric, check [Sequeda and Corcho, 2009]

• Example:

http://aemet.linkeddata.es/resource/Observation/at_1316382600000_of_08130_on_VV10m

when sensor property

Ingredients for Linked Sensor Data

Core ontological modelAdditional domain ontologiesGuidelines for generation of identifiersSensor Web programming interfacesQuery processing engines

http://www.flickr.com/photos/santos/2252824606/

Sensor High-level API

Source: K. Page & Southampton’s team at SemsorGrid4Env

Sensor High-level API

Source: K. Page & Southampton’s team at SemsorGrid4Env

Ingredients for Linked Sensor Data

Core ontological modelAdditional domain ontologiesGuidelines for generation of identifiersSensor Web programming interfacesQuery processing engines

http://www.flickr.com/photos/santos/2252824606/

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Swiss-Experiment

• FP7 Network of Excellence

GeoResearcher

Environmental and GeoScience researchSwiss Alps

...

...

...

Real-time data

Snow,Wind,Radiation.Lots of stuff

• How much snow is lost to evaporation?

• Snow redistribution by wind• Wind erosion of sand• ...

I want data to create my

models and compare

Where is the Data?

46

GSN

GSN server instance

wan7

timed: datetime PKsp_wind: float

..sensor1sensor2sensor3…

Virtual

senso

rs

timed sp_wind

1 3.4

2 5.6

3 11.2

4 1.2

5 3.1

.. …

Queries

SELECT sp_wind FROM wan7 WHERE sp_wind >10

SELECT sp_wind FROM wan7 [NOW -5 HOUR] WHERE sp_wind >10I want SPARQL!

Where is the Data?

47

GSN

GSN server instance

wan7

timed: datetime PKsp_wind: float

..sensor1sensor2sensor3…

Virtual

senso

rs

ssn:Observation

Mappings

Creating Mappings

48

wan7

timed: datetime PKsp_wind: float

ssn:ObservationValue

qudt:numericValue

xsd:decimal

http://swissex.ch/data#Wan7/WindSpeed/ObsValue{timed}

sp_wind

ssn:SensorOutput

ssn:Observation

ssn:hasValue

ssn:observationResulthttp://swissex.ch/data#

Wan7/WindSpeed/Observation{timed}   

http://swissex.ch/data#Wan7/ WindSpeed/ ObsOutput{timed}   

ssn:Property

ssn:observedProperty

sweetSpeed:WindSpeed

R2RML

• RDB2RDF W3C Group, R2RML Mapping language:• http://www.w3.org/2001/sw/rdb2rdf/r2rml/

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:Wan4WindSpeed a rr:TriplesMapClass; rr:tableName "wan7"; rr:subjectMap [ rr:template "http://swissex.ch/ns#WindSpeed/Wan7/{timed}"; rr:class ssn:ObservationValue; rr:graph ssg:swissexsnow.srdf ]; rr:predicateObjectMap [ rr:predicateMap [ rr:predicate ssn:hasQuantityValue ]; rr:objectMap[ rr:column "sp_wind" ] ]; .

<http://swissex.ch/ns#/WindSpeed/Wan7/2011-05-20:20:00 > a ssn:ObservationValue<http://swissex.ch/ns#/WindSpeed/Wan7/2011-05-20:20:00 > ssn:hasQuantityValue " 4.5"

Queries to Sensor Data

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 { … 50

SNEEqlRSTREAM SELECT id, speed, direction FROM wind [NOW];

Streaming SPARQLPREFIX fire: <http://www.semsorgrid4env.eu/ontologies/fireDetection#>SELECT ?WindSpeedFROM STREAM <http://…/SensorReadings.rdf> WINDOW RANGE 1 MS SLIDE 1 MSWHERE { ?sensor fire:hasMeasurements ?WindSpeed FILTER (?WindSpeed<30)}

SPARQL-Stream

Query translation

SELECT ?waveheightFROM STREAM <www.ssg4env.eu/SensorReadings.srdf> [NOW – 5 HOUR TO NOW]WHERE { ?WaveObs a ssn:ObservationValue; qudt:numericalValue ?waveheight; FILTER (?waveheight>10) }

SELECT sp_wind FROM wan7 [NOW -5 HOUR] WHERE sp_wind >10

Data Access

• GSN Web Services• GSN URL API

• Compose the query as a URL:

52

http://montblanc.slf.ch :22001/ multidata ?vs [0]= wan7 &field [0]= sp_wind &from =15/05/2011+05:00:00& to =15/05/2011+10:00:00&c_vs [0]= wan7 & c_field [0]= sp_wind & c_min [0]=10

SELECT sp_wind FROM wan7 [NOW -5 HOUR] WHERE sp_wind >10 ?

Algebra expressions

53

timed,sp_wind

π

ω

σ sp_wind>10

5 Hour

wan7

http://montblanc.slf.ch :22001/ multidata ?vs [0]= wan7 &field [0]= sp_wind &from =15/05/2011+05:00:00& to =15/05/2011+10:00:00&c_vs [0]= wan7 & c_field [0]= sp_wind & c_min [0]=10

SELECT sp_wind FROM wan7 [NOW -5 HOUR] WHERE sp_wind >10

Using the Mappings

54

SELECT ?waveheightFROM STREAM <www.ssg4env.eu/SensorReadings.srdf> [NOW – 5 HOUR TO NOW]WHERE { ?WaveObs a ssn:ObservationValue; qudt:numericalValue ?waveheight; FILTER (?waveheight>10) }

wan7

timed: datetime PKsp_wind: float

xsd:datatype

ssn:ObservationValue

qudt:numericalValue

sp_wind

http://swissex.ch/data#Wan7/WindSpeed/ObsValue{timed}

timed,sp_wind

π

ω

σsp_wind>10

5 Hour

wan7

Algebra construction

55Sensors, Mappings and Queries

timed,sp_wind

π

ω

σ sp_wind>10

5 Hour

wan7

windsensor1

windsensor2

Static optimization

56Sensors, Mappings and Queries

timed,sp_wind

π

ω

σ sp_wind>10

5 Hour

wan7

timed,windvalue

π

ω

σ windvalue>10

5 Hour

windsensor1

timed,windvalue

π

ω

σ windvalue>10

5 Hour

windsensor2

57

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

Query translation

Query ProcessingC

lient

Mappings

SPARQLStream

[tuples]

Sensor Network

Data translation[triples]

GSN API

R2RML Mappings

http://montblanc.slf.ch :22001/ multidata ?vs [0]= wan7 &field [0]= sp_wind

Query processing engines

Lessons Learned

• High-level• 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• Practical lessons learned

• Manage separately data and metadata of the sensors• Data should always be separated between realtime-

data and historical-data• Use the time format xsd:dateTime and the time zone• Graphical representation of data for weeks or months

is not trivial anyway

Conclusions

Ingredients for Linked Sensor DataCore ontology

Domain ontologiesGuidelines for identifiersAPIs

Query processing engines

Work in progress & examples

Challenges: generate & consume LSD

Thanks!

Questions, please.

jp.calbimonte@upm.es

60

Acknowledgments: all those identified in slides, especially those working in LSD at OEG: Oscar Corcho, Raúl García-Castro, Freddy Priyatna + the SemsorGrid4Env team (Alasdair Gray, Kevin Page, etc.), the AEMET team at OEG-UPM (Ghislain Atemezing, Daniel Garijo, José Mora, María Poveda, Daniel Vila, Boris Villazón) + Pablo Rozas (AEMET)