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Page 1: A Semantically Enabled Service Architecture for  Mashups  over Streaming  and Stored Data

A Semantically Enabled Service Architecture for Mashups over Streaming and Stored Data

Alasdair J G GrayUniversity of Manchester

Extended Semantic Web Conference 2011

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Semantic service architecture for mashups – A. J. G. Gray 2

Overview of the Talk• Motivation: Estuarine Flooding• Semantic Sensor Web – SemSorWeb

– Requirements– Architecture

• Semantic property documents• Demo application: sample mashup

June 2011

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3Semantic service architecture for mashups – A. J. G. Gray

Motivation: Estuarine FloodingThe Solent• Strait separating the Isle of Wight from English

mainland• Busy shipping channel and ports• Complex tidal and wave patterns

– Two high tides

June 2011

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Semantic service architecture for mashups – A. J. G. Gray 4

The Solent

June 2011

Images: http://www.wikipedia.com/

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Estuarine Flooding• Financial implications

– Damage– Loss of business

• Personal factors– Emotional impact

• Flood prediction– Locations– Severity

• Requires correlating– Sea-state data– Weather forecasts– Details of sea defences

• Response Planning– Evacuation routes– Personnel deployment– …

• Requires more data– Traffic reports– Shipping– …

June 2011Image: http://www.metro.co.uk/

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Flood defences data (database)

Meteorological forecasts

Flood Detection and Response“Detect overtopping events in the Solent region”

sea-level > sea-defence• Sea-level: sensors• Defence heights: databases

“Provide contextual information”• Web feeds• Other sources: maps, models

June 2011

Real-time sensor data

Wave,Wind,Tide

Other sources:Maps, models, …

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1. Accurate characterisation of conditions that define an event

2. Correlation of data of differing modalities3. Integrating data from heterogeneous data

models4. Discovery of relevant data sources5. Presentation and control of information

June 2011

Sensor Web Requirements“Provide flood risk details of overtopping events in the Solent region with high wind speed observations”

“Provide flood risk details of overtopping events in the Solent region with high wind speed observations”

“Provide flood risk details of overtopping events in the Solent region with high wind speed observations”

“Provide flood risk details of overtopping events in the Solent region with high wind speed observations”

“Provide flood risk details of overtopping events in the Solent region with high wind speed observations”

“Provide flood risk details of overtopping events in the Solent region with high wind speed observations”

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ApplicationServices

Semantic Registry

Semantic Integrator

Data Source

Connectivity Bridge

Applications

Concrete Resource

SemSorWeb ArchitectureInterfaces• Service Metadata• Registration• Discovery• Integration• Query• Data Access• Subscription• Notification

June 2011

Semantic Property

Document

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Reconcile Terminology

SSN

SWEET

Service

Coastal Defences

Ordnance Survey

Additional Regions

Role

DOLCE UltraLite

Schema

FOAF

Upper

External

SSG4Env infrastructure

Flood domain

June 2011

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Sea-State Sensor Data: Channel Coastal Observatory

43 sensors deployed around UK coast• Measuring

– Tides (7)– Waves (24)– Weather conditions (12)

• On and off shore• Bespoke hardware• Fixed functionality• Fixed data rate• Central data centre

June 2011

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Publishing CCO Sensor Data• Service Description

– Streaming data service• Dataset Description

– Spatiotemporal data coverage

– Thematic data coverage

• Tide height• Dataset schema

description– envdata_sndownpier_tide

(ts:int, Observed:float, Tz:float, Hs:float, HMax:float, Tp:float)

<service:WebService rdf:about="#cco-ws”> <rdfs:label>Channel coastal observatory streaming data service</rdfs:label> <service:hasInterface rdf:resource="service:ssg4ePullStream"/> <service:hasDataset rdf:resource="#envdata_SandownPier_Tide"/> <service:hasDataset rdf:resource="#envdata_SandownPier_Met"/></service:WebService><sweet:Dataset rdf:about="#envdata_SandownPier_Tide"> <rdfs:label>envdata_SandownPier_Tide</rdfs:label> <service:coversRegion rdf:resource="&AdditionalRegions;SandownPierLocation"/> <time:hasTemporalExtent rdf:datatype="&registry;TemporalInterval”>[2005, NOW]</time:hasTemporalExtent>; <service:includesFeatureType rdf:resource="&CoastalDefences;Sea"/> <service:includesPropertyType rdf:resource="&CoastalDefences;TideHeight"/> <service:includesPropertyType rdf:resource="&CoastalDefences;WaveHeight"/> <service:hasSchema rdf:resource="#envdata_SandownPier_Tide_Schema"/></sweet:Dataset><schema:Stream rdf:about="#envdata_SandownPier_Tide_Schema"> <schema:extent-name>envdata_SandownPier_Tide</schema:extent-name> <schema:hasAttribute rdf:resource="#HMax"/> <schema:hasAttribute rdf:resource="#Tp"/></schema:Stream><schema:Attribute rdf:about="#HMax"> <schema:attribute-name>HMax</schema:attribute-name>

June 2011

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Flood Web Application“Smashing it all together”

Severe Weather Alert!Gale force winds, Boscombe Bay

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User Login: Locate Relevant Data

• User logs in selecting:• Role• Region• Task

• Values parameterise registry lookups

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Semantic service architecture for mashups – A. J. G. Gray 17

Initial Display

June 2011

Inform user of potential overtopping events.

Pose query to integrated data source.

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Integrating Sensed and Stored DataWeb

ApplicationApplication

Services Integrator DQP CCO-WS CCO-StoredGET http://…/geojson?interval=xxx&resource=yyy&query=zzz

SPARQLExecuteFactory(integrator, query)

GenericQueryFactory(snee, pull, query)EPR

EPR

URL

JSON

GET URL

SPARQLResultSet

GetStreamItem(int:<stream>, <pos>)

WebRowSet

SQLExecute(cco, query)

GetStreamItem(cco:<stream>, <pos>)

WebRowSet

GetStreamItem(snee:pull:<stream>, <pos>)

WebRowSetGetStreamItem(cco:<stream>, <pos>)

WebRowSet

GetStreamItem(snee:pull:<stream>, <pos>)

WebRowSet

GetStreamItem(cco:<stream>, <pos>)

WebRowSetJSON

GET URL

SPARQLResultSet

GetStreamItem(int:<stream>, <pos>)

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Sensor Data

June 2011

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Conclusions: Requirements revisited

1. Accurate characterisation of conditions that define an event– Declarative queries

2. Correlation of data of differing modalities– Query evaluation over streaming and stored

3. Integrating data from heterogeneous data models– Ontology-based access to streaming and stored data

4. Discovery of relevant data sources– Semantic registry

5. Presentation and control of information– Application services to support, e.g. smash-ups

June 2011

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Acknowledgements• For more information:

http://www.semsorgrid4env.eu/• Demo application:

http://www.semsorgrid4env.eu/services/dynamic-demo

June 2011