ssg4env egu2010

Speaker:

SemsorGrid4Env: Semantic Sensor Grids for

Rapid Application Development forEnvironmental Management

FP7-223913

European Geosciences Union 2010

From Sensors to Interoperable Sensor Networks

Vienna, 6th May 2010

Jean-Paul Calbimonte,Universidad Politécnica de Madrid semsorgrid4env.eu

Table of Contents

• The Consortium• Project Challenges and Main Outcomes• Project Plan & milestones• Highlights

• Architecture• Data management• Registries• Semantic Integration• Application Tier

EGU 2010 - Vienna, 6 May 2010 2

The Team

1. Universidad Politécnica de Madrid, (UPM,

Spain)

2. University of Manchester (UNIMAN, UK)

3. National and Kapodistrian University of

Athens (NKUA, Greece)

4. University of Southampton (SOTON, UK)

5. Deimos Space SLU (DMS, Spain)

6. EMU Ltd. (EMU, UK)

7. TechIdeas (TI, Spain)

3

3

3

1

3EGU 2010 - Vienna, 6 May 2010

Project Challenges

Integrated information space •Discovery new sensor networks•Integrate with existing ones

• Integrate possibly other data sources (e.g., historical databases)

020406080

100

1ertrim.

3ertrim.

Este

Oeste

Norte

sens or networks

legacy data sources

semantic data integration and querying

thin applications (mashups )

regis tries

middleware

Rapid development • flexible and user-centric decision

support systems • Use data from multiple

autonomous independently deployed sensor networks and other applications.


Main Outcomes (I)

System Level (WP1)• An architecture for the design and implementation of open large-

scale Semantic Sensor Grids.• A reference SemsorGrid4Env implementation instantiating the

architecture

Component-level (WP2-WP5):• New techniques and tools for semantic-based data management

over the heterogeneous data streams that stem from autonomously deployed sensor networks. (WP2)

• Scalable and fault-tolerant resource discovery mechanisms for sensor registries. (WP3)

• The semantic infrastructure (including ontologies) needed to facilitate the integration of data coming from heterogeneous and distributed sensor networks, legacy databases and applications. (WP4)

• Higher-level application programming interfaces that ease the rapid generation of thin applications (e.g., mashups) of data from sensor networks and historical databases. (WP5)

Two environmental management applications (WP6-WP7)


Main Outcomes (II)

Fire Risk Monitoring and Warning in Spain

(technology-driven)

Coastal and Estuarine Flood Warning in Southern UK.

(established early adopter community)

MIDDLEWARE

THIN APPLICATIONS

Sensor network 1 Sensor network 2

EARTH OBSERVATION PRODUCTS

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T im e sin c e sta r t o f ye a r (h r )

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M o de l p re d ic tion s M e a su rem en t s R es idu a ls

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N a tio n a l S u rge M o d e l

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F ig . X L iv e - tim e d a ta a ss im ila tio n flo o d m o d e llin g : M u ltip le d a ta a cce ss

S o le n t R e -de p lo ya b le G S M -b a sed se n so r n e two rk

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7

Why SSG4Env?

• Flood and fire have significant environmental and economic impact in Europe

• Significant potential from emerging technologies to assist users by:• Improved monitoring by deployed & emerging sensor networks• New capabilties in data integration including live data streams• Rapid development of flexible and user-centric decision support

systems • Semantic Web supporting discovery and integration

• SSG4Env combines expertise and technology in all of these areas to provide solutions which are simple, live and dynamic

EGU 2010 - Vienna, 6 May 2010

8

Workpackage Structure and Deliverables

D1.1: Setup of software development technologies

D1.2: Deployment of technological infrastructure

D1.3: SemsorGrid4Env Architecture

D2.1: Data Requirements, Data Management and Analysis Issues and Query-Based Functionalities

D3.1: Data models and languages for registries in SemsorGrid4Env

D3.2: Distributed data structures and algorithms for a Semantic Sensor Grid registry

D4.1: Design of the SemsorGrid4Env ontology-based data integration model

D5.1: Specification of high-level application programming interfaces

D6.1: Requirements specification

D6.2: Deployment of the sensor network

D7.1: Requirements specification

D7.2: Deployment of the FloodNet sensor network in the Solent

D8.1: Quality and Risk Contingency Plan

D8.2: Gender Action Plan

D9.1: SemsorGrid4Env Website

D9.2: Plan for Dissemination Activities

D9.3:SWOT Analysis


Main Project Phases

Phase 1Months 1-6(Specification)

1. Induction, know-how, and use case gathering. 2. WP 1-5 will collaborate to assimilate the current architectural and technological

features and challenges of all the areas involved; and identify appropriate approaches for their integration.

3. WP 1-5 work with WP 6-7 to develop use case specifications that will form the drivers and the evaluation framework for the rest of the project

Phase 2Months 7-22(Development 1)

1. A specification of the architecture incorporating all services from WP 2-5.2. A first specification of the use case applications3. A first prototype of the individual systems and of the integrated middleware

Phase 3Months 23-34(Development 2)

1. A refined specification of the architecture incorporating all services from WP 2-5.

2. A second specification of the use case applications3. A second prototype of the individual systems and of the integrated middleware

Phase 4Months 33-36(Final Evaluation)

1. Evaluation of individual systems and integrated middleware.2. Evaluation of use cases.


WP1: SSG4Env General Architecture

10

Properties:• Any service may directly call

any other service.• Pre-existing services may be

called by any service.• Independent development of

services.• Based on WS-* standards: WS-

RF, WS-DAI, WS-N.

Key features:• Data tier services wrap

concrete data resources.• Semantic middleware adds

value to services in application and data tiers.


• Principal outcomes:• SNEE query processor / SNEEql query language• Documented requirements from use cases, to the level

of queries and data analyses.• Support QoS-aware evaluation within in network query

optimizer.• Developed out-of-network query compiler and evaluator

from to support integration queries.

WP2: Data & Stream Query Processor

SELECT RSTREAM t.id, w.speed, w.dirnFROM wind[NOW] w, tree[NOW] tWHERE t.smoke > 0AND sqrt((t.locx - w.locx)^2 + (t.locy - w.locy)^2) <= 40

EGU 2010 - Vienna, 6 May 2010 11

• Defined the data model stRDF and the query language stSPARQL, based on the paradigm of constraint databases.

• Represent thematic and spatial metadata that change over time. Coupled with the RDFS/OWL ontologies of WP4.

• Developed a formal semantics and algebra for stSPARQL on which we base our implementation.

• Development of Strabon: a centralized implementation of a subset of stSPARQL.

WP3: Semantic Registry

EGU 2010 - Vienna, 6 May 2010 12

• Designed, implemented and deployed a Semantic Integration Service • Extend existing ontology-based data integration models

to take into account sensor networks streaming data, semantic heterogeneity and quality of service

• Specified a suite of sensor network ontologies that will be used for describing sensors and related data for the SemSorGrid4Env software architecture

WP4: Semantic Infrastructure

EGU 2010 - Vienna, 6 May 2010 13

Query reconciliation

q qrQuery

canonisation

Qc

Distributed Query

Processing

Data decanonisation

Data reconciliationd dr

Dc

Clie

nt

O-O mapping S2O mappings

SPARQLSTR (Og) SPARQLSTR (O1 O2 On) SNEEql (S1 S2 Sn)

SNEEql’ (S1 S2 Sn)

[tuplel1 l2 l3][tripleO1 O2 On][tripleOg]

Semantic Integrator

WP2: Semantic IntegratorOntology-based data access

EGU 2010 - Vienna, 6 May 2010 14

SSG4Env Application Tier (WP 5, 6 & 7)

• High level API provides functionality for domain developers (API) and domain users (web apps)

• Supports applications in WP6 and WP7

• Resource-centric including Linked Data

• Embraces and investigates interplay of SOA and ROA

15EGU 2010 - Vienna, 6 May 2010

Summary (I): Application highlights

• Application requirement specifications• Sensor deployment in the UK Solent area• Early mashup developments for flood warning

• In order to engage more quickly potential users and other stakeholders.

16EGU 2010 - Vienna, 6 May 2010

Summary (II): Technical highlights

• Integration platform (WP1)• Architecture, validated with the application use cases (WP1)• Selection of outlier detection algorithms (WP2)• Out-of-network event stream query processor (WP2)• TinyOS2 code generator for the in-network SNEE (WP2)• Spatio-temporal extension of SPARQL (stSPARQL) (WP3)• Ontology-based streaming data access (WP4)• Selection of ontologies to be reused (WP4)• API combining RESTful and Linked Open Data approaches

(WP5)• A proposal for the identification, naming and generation of

Linked Stream Data (WP5)

17EGU 2010 - Vienna, 6 May 2010

Speaker:

SemsorGrid4Env: Semantic Sensor Grids for

Rapid Application Development forEnvironmental Management

FP7-223913

European Geosciences Union 2010

From Sensors to Interoperable Sensor Networks

Vienna, 6th May 2010

Jean-Paul Calbimonte


• Mashups provide rapid development of web interfaces to support custom requirements.

• Mashups require combined access to:• Sensed data from multiple sensors.• Stored data from multiple sources.• Ontologies for linking independent sources.

• The aim of the architecture is to deliver appropriate abstraction and integration services for the mashups.

Top Level Requirements


The Consortium Classified

Seven partners• Four universities• 2 SME• 1 large company

Four major sectors• Education• IT• Aerospace Engineering• Environment

Technological core competencies• Sensor Networks (UNIMAN,

SOTON-ECS, NKUA)• Semantics (UPM, UNIMAN,

SOTON-ECS)• Grid (UNIMAN, TI, SOTON-

ECS, UPM)• P2P (NKUA)• Rapid Application

Development (SOTON-ECS)

Use Cases• Flood warning (EMU,

SOTON-GEODATA)• Fire warning (DMS)

21EGU 2010 - Vienna, 6 May 2010

conceptmap-def WindSpeedMeasurement

uri-as

concat('ssg4env:WindSpeedMeasurement_',

windsamples.sensorid,windsamples.ts)

described-by

attributemap-def hasSpeed

operation "constant"

has-column windsamples.speed

dbrelationmap-def isProducedBy toConcept Sensor

joins-via

condition "equals"

has-column sensors.sensorid

has-column windsamples.sensorid

conceptmap-def Sensor

uri-as

concat('ssg4env:Sensor_',sensors.sensorid)

described-by

attributemap-def hasName

operation "constant"

has-column sensors.sensorname

Measurement

WindSpeedMeasurement

Sensor

isProducedBy

hasName xsd:string

hasSpeed xsd:float

S:WindSamples

- ts- speed- direction- sensorid

T:Sensors

- sensorid- sensorname

S2O: Mapping streams to ontologies


PREFIX fire: http://www.semsorgrid4env.eu#

PREFIX rdf: http://www.w3.org/1999/02/22-rdf-syntax-ns#

SELECT ?speed ?name

FROM STREAM <http://www.ssg4env.eu/Readings.srdf>

[RANGE 10 MINUTE STEP 1 MINUTE]

WHERE {

?WindSpeed a fire:WindSpeedMeasurement;

fire:hasSpeed ?speed;

fire:isProducedBy ?sensor;

fire:hasTimestamp ?time.

?sensor a fire:Sensor;

fire:hasName ?name.

}

SELECT concat(‘ssg4env.eu#WindSpeedMeasurement' , windsensor.id, windsensor.ts ) as a1 , concat( ‘ssg4env.eu#Sensor' , sensors.sensorid ) as a2 FROM sensors, windsensor[ FROM NOW - 10 TO NOW MIN] WHERE ( sensors.sensorid = windsensor.id )

Semantic Integrator

Work in progress: removing redundant queries, basic optimisations, more complex scenarios

Transforming SPARQLSTR to SNEEql

ssg4env egu2010

Documents

integration of data

data requirements

data models

data sources

mashups of data

data tiers

data analyses

semanticbased data management