ESRIN, Frascati, 6 th April 2005

1/xxKES-B Project Final Presentation – ESRIN, Frascati, 6th April 2005

ESRIN, Frascati, 6th April 2005

KES-B Project

Final Presentation

gtd SISTEMAS DE INFORMACIÓN


Agenda

PART 0. ESRIN Presentation

PART I. IntroductionI.1 BackgroundI.2. ObjectivesI.3. Added ValuesI.4. Project Organisation

PART II. Technical PresentationII.1. Implementation ApproachII.2. Operational ContextII.3. ArchitectureII.4. OntologyII.5. Subsystems

PART III. ConclusionsIII.1. ResultsIII.2. Way ForwardIII.3. Open Questions

PART IV. DemoIV.1. Physical

DeploymentIV.2. Search DemoIV.3. Production DemoIV.4. Open Questions 2


ESRIN Presentation

ESRIN Presentation


Introduction

PART 0. ESRIN PresentationPART 0. ESRIN Presentation







I.1 Background

Problems in the EO Data Exploitation Chain:

1. The gap between EO Data Archives and Information/Services UsersDue to: The complexity and expense

of the eminently manual process of mining information from EO data

Resulting in a bottleneck for the exploitation of the petabytes of available and new EO data.

2.The heterogeneity and incompatibility among formats and tools Affecting data, information and

knowledge. Results in a number of additional

difficulties for shorting the above introduced gap.


I.2 Objectives (1/2)

KES-B is a (TRP) focussed at demonstrating with a prototype system the feasibility of the application of innovative knowledge-based technologies to provide services for two needs :1) Support users in easily identifying and accessing the required

information or products by using their own vocabulary, domain knowledge and preferences.

2)Automate generation of EO products with easy, scheduled and controlled exploitation of EO resources (e.g.: data, algorithms, procedures, ...)These initial goals have been translated in the KES-B prototype as the

provision of the two main types of KES-B services:1) Search service (also referred to as Product Exploitation or

Information Retrieval service), which takes the form of the present web search portal

2) Production service (also referred to as Information Extraction), which takes the form of a workflow system that is able to integrate Image Information Mining (IIM) processing functions, and that publishes its services to the SSEKES-B Prototype Application domain scenario of test : o Water Quality: Oil –spill detection , HAB detection.o Transport Security: Ship detection, Winds extraction.


I.2 Objectives (2/2)

Transformation (via Knowledge)

Non-EO Data

Service ProvidersValue AddersDistributors

Data Providers

Users (KBytes)

Information/ServicesEO Data/Products

Archives (PBytes)

Support Infrastructure?

Easy / Automate?

SSE

KES-BProduction KES

IIM

I.RSearch KES

KES-B Platform

KES Knowledge


I.3 Initial Added Value

Thus, EO Data exploitation remain unexploited because of:a) Manual transformation of Data to Information.b) The user does not know about available information

KES-B contributes to solve these problems by:


I.4 Project Organisation


PART I. IntroductionI.1. BackgroundI.2. ObjectivesI.3. Added ValuesI.4. Project Organisation

PART II. Technical PresentationII.1. Implementation ApproachII.2. Operational ContextII.3. ArchitectureII.4. OntologyII.5. SubsystemsII.6. Physical Deployment


Technical Presentation





II.1. Implementation Approach


II.1. Implementation Approach: OWL Ontologies


Data Provider

EO Services Registry

SSEPortal

SSE

Data Repository

Service Provider 1

KES-B

Production services

Search services

WFD

Internet

WF developerExpert

SP2

KES-B

SP3

KES-B

SP4

WFD WFE

KES-BSearchPortal

KES-BFunctionProvisionPortal

ConsumerUser

FunctionsProvides Experts

II.2. Operational Context


II.3. Architecture: System Actors and Functions

KES-B Platform

MASS-SSE

KESB Production Expert User

KESB Search User

Processing Functions[ICD-IF-3]

ExternalDR

Provide/Receive operational data

[ICD-IF-2]

SearchEO resources

Invoke Product. Services

[ICD-IF-1]

KESB System Administrator

EditUser Profile

Administrate System- Production- Search- Knowledge

SearchProduction

System Administration

Knowledge Management

Production Workflows

Register Services


II.3. Architecture: System Components and Interfaces

ONTOLOGY

Web Portal Interface (WMS)

KES-B Platform

MASS Interface System (MIS)

MASS-SSE

Expert User

Production Mgnt.System

(PMS)

Provide Processing Functions

Put Feature

Data

KnowledgeMgnt.System

(KMS)

Features Mgnt.System

(FMS)

User Mgnt.System

(UMS)

Search on KB

ExternalDR

Provide/Receive operational data

Put Production metadata

in KB

Read/Write Feature

metadata

Get User Preferences

from KB

Edit User Profile

SearchEO resources

Invoke Product. Services

Invoke Search Services

Search on Feature Server

Administrator

EditUser Profile

Administrate System

(KESB-ICD-IF2)

(KESB-ICD-IF1)

(KESB-ICD-IF3) (*)

Search Mgnt.System

(SMS)


II.3. Architecture: Production Collaboration Sequence


II.3. Architecture: Search Collaboration Sequence


Features

Text

Non-EOData

Products

Production Procedures

Sub-Domains

Categories

Feature Extraction

EO Non-EO

Sensor and auxiliaryData

Algorithms/ Rules

Algorithms/ Rules

Domains

Applications

II.4. Ontology:Overview of Initial Concepts


Functional View : System Needs driving to Ontology needs

II.4. Ontology: Ontology functions

Ontology Needs

ISO 19115 DCISO 19115 (, GML)

KESKES, QSR (, FL)KES, EO, ES

System Needs

ProductionSearch

KnowledgeManagement

Ontology & KB

Application Domain Knowledge Schemas

Search Support Schemas

Spatial CataloguingSchemas

Production Resources Schemas

Operational Resources Schemas

Information Resource Schemas


II.4. Ontology: Ontology Models integratedOntology Construction Language

Ontology Model

Need Used by Deployed by

OWLXML Based

EO Earth Observation domain knowledge representation.

SMS to support search for EO ResourcesKB Server

ES Earth Science domain knowledge representation

SMS to support search for EO Resources. KB Server

KES Knowledge Enabled Services knowledge representation.

SMS to support search with user adaptation.PMS for Production Resources cataloguing.

KB Server

DC Dublin Core for universal item cataloguing.

SMS to support search conducted by using information resource tags.

KB Server

ISO-19115

Spatial Resources Metadata framework. PMS for Production Operational Resources Cataloguing.FMS for Feature Manager Operational Resources and Metadata Cataloguing.

KB Server

QSR Qualitative Spatial Reasoning (using FL).

SMS (through the SRE) for advanced spatial searches over vector feature data stored on the GIS Feature Server.

KB Server

FL Fuzzy Logic for Uncertainty Representation.

QSR ontology. KB Server

XML Based BPEL Workflow Definition PMS

UDDIWSDL SOAP

Web Service RegistrationWeb Service DefinitionWeb Service Use

MASS Interface

OWL RDF-SRDF

Knowledge Repr. (Ontology-based)Knowledge Repr. (Triples Networks with Taxonomy)Knowledge Repr. (Triples Data Networks)

KB Server

Non-XML Based

WordNet WordNet (WN) Natural Language Processing (NLP) Ontology.

Used by SMS.Free Text Query algorithm. SMS


II.4. Ontology: Deployment of Ontology Components


II.4. Ontology: KES ontology: kes_Resources taxonomy


II.4. Ontology: KES ontology: relations between resources

• Two kes_Resources can be related with a kes_Relation class. This enables the construction of a semantic networks of resources.

• Each relations bears weights, to each user and to each domain. These weights are modified when the user browse the knowledge base navigating through the semantic network.


II.4. Ontology: KES ontology: instanciating eoDomains

Ontology

Knowledge Base

kes_Domain

es_Domaineo_Domain

KES SystemOntology Model

EO & ES Application DomainOntology Models

Water Quality Domain Ocean Optics DomainKes_Domaininstances Ocean Circulation Domain

isA isA isA

«uses»

Kes_Conceptinstances

Maritim Security Domain

Oil Spill Domain Ships Domain Winds and Waves DomainHarmful Algae Bloom Domain

isAisA isA isA isA

kes_is_Akes_is_A kes_is_A

kes_is_A

Concept NConcept 1

«uses»

Concept NConcept 1…..

Oil_spill domain custom terms

Ships domain custom terms


II.4. Ontology: Spatial Reasoning Engine ModelSpatial Reasoning EngineOntology Model

KES Ontology Model

Fuzzy LogicOntology Model

-threshold

RuleSet

Relations

1*

11

SpatialRelations

«extends»

AttributeRelations

#not

Attributive

«extends»

#not#threshold

Equality

#not

Intersection

-fusionAlgorithm-nodesToWrite-outputAttributesCF-outputLayers

FusionModel

1*

1

1

-operation

AttributeGeneration

kes_Feature

kes_Feature_Attribute

1

*

1

1

-optionreporting-replacing

ReportModel-haloFactor-haloOffset-zEnable-zThreshold-areaLayer-areaPolygonID-areaPolygonLabel-inputLayers-inputAttributesCF-cfCombMethod-aoiCoords

SearchModel

1

2

FuzzySet

ClassMembershipFunction 1

1

11

#not

Distance

«extends»

Other_relations

«extends»

-minAngle-maxAngle

Orientation

1

1

Relative

«extends»

#threshold

Touches

Absolute

1

1

1

1

1

1

1

1

Equals Crosses-threshold

Disjoints

-threshold

Contains

«extends»

-threshold

Intersects

«extends»

-count-aligned-angleTolerance-distanceTolerance-threshold

Node

-conditionAttributeName-conditionOperationType-conditionValue

Condition

1

2

1

1 1*

SRE ontology: 3 main parts:• Search Model• Fusion Model• Report Model

Search Model are rulesets of relations between features. Relations can be geo-spatial and attributive.Fusion Model are definition of topological operations (e.g. union, envelop), and atribute operations.Both models combine represent in a a Data Fusion model working on a GIS feature level.


Enables to define Fuzzy Logic terms:

- Fuzzy Sets (e.g. distance)- Fuzzy Terms (e.g. near, close)

Supports the Qualitative Spatial Reasoning (QSR) Ontology, so users can define queries using fuzzy terms, (i.e. semantic terms)

II.4. Ontology: Fuzzy Logic Ontolody Model

Universe of Discourse

1

0

Physical distance (km)

Very short Short Somewhat Long Long


Knowledge Base Server Components Architecture Diagram

MySQL...

Kes_Resource

NonSpatial Spatial

JRE 1.4.2_04-b03

JRE 1.4.2_03-b02

JBOSS 3.2.3

Protege RMI Server

KnowledgeBaseAPI

KnowledgeBaseBean(EJB)

RMIConnector

Rmi Registry(JNDI Srv)

Rmi Registry(JNDI Srv)

JDBC

Get/Set Slot(Protégé Instances)

model

storage

RMI

protege

ProtégéProject Client


n clients

KBI Data(Remote)

Client

n clients

RMI

ProtégéProject

(Remote)Client

n clients


Get/Set Attribute(Ontology Class Java Objects)

The knowledge base server keeps all the system information in a knowledge enabled manner using Protégé.

The Protégé project is based on OWL and it is supported by a MySQL database backend. The OWL based elements are served by the Protégé RMI Server included in protégé distribution.

II.5. Sub Systems: KMS


Above picture shows the KBS architecture. It is based on three main components:

1. MySQL Database (COTS).

2. Protégé RMI Server (COTS).

3. KBI EJB Application (specifically developed KESB component).

The KBI EJB application implements the “Put data” and “Get data”.

Knowledge Base Server Components Architecture Diagram

II.5. Sub Systems: KMS


FMS

Feature server FS

PMS

SQL Server

KMS

FeatureManagement

InterfaceLibrary

SHP

DRSRegistry

DB

KB

ConfigFile

SDE Server

SDE consoletools (DLL)(SHP2SDE,SDE2SHP)

Invoke FetureData Import

Services(SOAP interface)

FeatureManagementAPI interface

SMS

KB ServerInterface

WFE (BPEL)Server

Data RepositoryServer

WebServiceInterface

applications

SpatialReasoning

Engine(SRE)

SDEdatabase

FeatureLayers

DB

SDE ClientLibrary

(Java Lib)

SDEConnection

JDBC

O.S.Calls

SDEConnection

JDBC

APICalls

APICalls

Read/writefeature metatada

The FMS represents the KES-B system responsible to handle (in an operational basis) the spatial feature data. Thus, it supports the feature data information production, and also the feature data information exploitation (retrieval).

Feature Server component

II.5. Sub Systems: FMS


In the context of the KES-B system architecture, the FMS represents the backend system to handle feature data, and providing services to the KES-B Production (PMS) and Search (SMS) systems :

1. First, the PMS imports the feature data generated as output of the image information mining (IIM) production procedure application workflows.

2. And second, the SMS contains advanced spatial reasoning engines to exploit (retrieve, search).

Feature Server component

II.5. Sub Systems: FMS


Service Provider

MASSSystem

Eclipse Platform

Collaxa BPELDesigner

Internet

KES-B Portal

FunctionProvision

WebInterface

KES-B ProductionManagement System

(PMS)

Createworkflows

BPELC

InvokeEO-Services

External DataRepositories

ExpertFunctionProvider

ExpertWorklfowDeveloper

KES-BMASS

InterfaceSystem(MIS)

KES-B WebMngmtSystem(WMS)

Get data /Put data

KES-BKnowledge

MngmtSystem(KMS)

KES-BFeatureMngmtSystem(FMS)

KBcontents

(metadata)

Featuremanagement

services

1. MIS (MASS Interface System), in order to publish KES-B services into MASS.

2. WMS (Web portal Mngt System), to publish a web graphical interface for function management (provision of function modules).

3. FMS (Feature Mngt System), in order to import feature data into KES-B GIS feature server database,

4. KMS (Knowledge Mngt System), in order to get and put metadata contents in the knowledge base.

II.5. Sub Systems: PMS


II.5. Sub Systems: PMS


A � Processing Machine Cluster (PMC), which is a variable set of Processing Machine (PM), each one including: o A Processing Machine WS Interface, to handle the requests coming from the Production Server. o A temporal Data Repository Server, to handle the operational products flow. o The actual processing engines (IDL, JAVA…) that are able to run the Function executable module provided by the expert. A set of � Processing Management Applications (PMA), for the production Expert and system administrator : o PMA1: the WFD tool (Collaxa BPEL Designer) o PMA2: the WFE console (Collaxa BPEL Server portal), to control the execution status of the WF. o PMA3: the Function Provision tool (a custom KES-B development). This tool provides a web-based interface for the expert to catalogue and submit Function packages, and a server side logic to generate the FuncionWS-Interface.

II.5. Sub Systems - PMS

PMS aims to provide the following main functionalities


The SMS is the core part of the KES-B platform that implements the Search capabilities. The SMS is conceived as the part of the KES-B system that enables the user to search, by query and browse actions, the relevant information available within the KES-B platform.

Searches provided by the SMS can be summarized in the following: 1.Queries and browsing,2.Free text query, ontology based query, spatial reasoning based query.

The contents object of search are any resource stored on platform data backend components:1.The Knowledge Base Server (KBS). 2.The Feature Server (FS)

II.5. Sub Systems - SMS


Main characteristic for this engine remains on being the basis of a decision-making support system, being it possible due to:

• Possibility of expressing complex query models, composed by a number of relations involving an arbitrary number of features. The relations can be spatial relations (proximity, intersection, overlapping, orientation,…), and also relations between attributes of the related features (temporal proximity, other attributes conditions, etc.). The defined complex query can be saved in the system (in the user account) for later use. • Fuzzy Logic based evaluation for almost all the relations (spatial predicates as well as attribute relation predicates) offered by the engine. It is interesting to know if two feature objects accomplish or not a relation between them, but moreover it is interesting to know the degree in that the relation is being accomplished (0,0 to 1,0 or percentage).• The Query Results are ordered in base of the approximation strength to the Query Model. This strength may be also known as Certainty Factor. This means that in a query, all the relation evaluations are combined in order to obtain a value (0.0 to 1.0 or percentage) that will indicate how good are the matches resulting from the query.

II.5. Sub Systems - SRE


II.5. Sub Systems: SRE (Spatial Reasoning Engine)

Internal Status VSCN GIS

Server

Pattern Matching Model

Fusion Model

Report Model

S.R. Engine

Application Logic

Spatial Reasoning Query Engine (WS)

Persistence Logic

Feature Server (Virtual Scene Interface)

SRE Models

SREngineWSInterface

SREngineWSDelegate

SREngineWSBean SRService

Spatial Reasoning Inference Engine

KnowledgeBaseConnector (EJB)

SearchFunction

FusionFunction

ReportFunction

Spatial Query Data Object

FTi*

R...

Condition

*

RGeometric

Fusion

Attribute Fusion

KB Data Access

Input Point

Invoke EJB APIgetObject()

Read/Write Feature Objects +SDE API

JESS / FuzzyJESS

Spatial Data Access


Persistence Logic

Persistence

Business Logic

Presentation

TextualSearchEngine(EJB)

OntologyBrowserEngine(EJB)

KnowledgeBaseConnector

(EJB)

RMI

User AdaptationEngine(EJB)

Protege RMI Server(Application)

RMI

RMI

User Adaptation API (JAR)

KnowledgeBaseAPI(JAR)

User ManagementController

User Creation

Form(JSP)

User EditionForm(JSP)

User Registry

(JSP)

User profile data

model

User ManagementAPI (JAR)

Domain Selection

Form(JSP) Added value in user oriented systems resides

in the capabilities that system has towards user adaptation.

The user interacts with the system constantly. The system ‘learns’ from those actions and updates user preferences.

After this learning process, the system is able to present information adapted to user preferences.KES-B user knowledge must be feed from following subjects:

User browsing: the navigation sequence exploitation produces a frequently navigation map. Portal map can be adapted to user preferences in this way to present most visited sections and short cuts to pages of interest.

II.5. Sub Systems - UMS


The KES-B system has to publish its KES capabilities as a webservice into the MASS/SSE Environment.

For this purpose, the MASS/SSE environment provides a MASS Toolbox (hereinafter MTB), as a component to be integrated in the service provider platform, enabling this publication and interfacing.

MIS is a KES_B component that contains all the connectors between MASS portal and KES-B system. The components of MIS described in the following chapters are:• Toolbox application,• Services containing the name of the KES-B web service to be

executed and the operations that the web service is capable to carry out,

• JSP files transform the Toolbox request into a KES-B service request,

• Definition files describing the service operations.

II.5. Sub Systems - MIS


II.5. Sub Systems - MIS


Generic portlet

Web Presentation component

PresetationController

Presentation Model

Presentation View

1 *

1

*

Main presentation conrtroller

«uses»

«uses»«uses»

HTTPServletPortal MapPage

1*

Descriptor1*

«uses»

1

*

Layout

1

1

HeaderFooter

1

11The Web Management System is the KESB Web Portal.

It represents a dynamic web content delivery.

II.5. Sub Systems - WMS



Conclusions








III.1. Conclusions: Structure


III.1. RL1 – Results on Technology


III.1. RL2: Results on System Design


III.1. Results on Ontology Architecture


III.1. Results on System Architecture


III.1. RL3: Results on System Functions


III.1. Results on Search Functions


III.1. Results on Production Functions


III.1. Results on Knowledge Management Functions


III.2. Ways Forward: in the short-term


III.2. Ways Forward: in the mid-long term (perspectives)

Curiosity

Skepticism

Commitment to Action

Know

ledge/E

xperi

ence

Increase in attendance at trainings and more evidence of

coverage at conferences

Confidence in ability to implement

Adoption

Enthusiasm

Advocacy

Positive experiences of the power of OWL

People are now asking “How” questions as opposed to

“Why” and “What”.

KES-B Initial Days

KES-B results take us here



WebServices

SemanticWeb

GridComputing

SemanticGrid

Semantic Web

Services

GridServices

Semantic for Grid


III.3. Questions and Answers

Thank You



IV. Demo








Public access to Internet

for MASS/SSE Conectivity

KES-B Platform

Private Network

J2SDK 1.4.2

b28

MASS

Interface

AnyExec

FTP

muis-c193.204.228.57

muis-dev193.204.228.62

J2SDK 1.4.2

TOMCAT/JetSpeed

+ JBOSS

- SREngine

- Text Search Engine

- UA Engine

- OB Engine

- FM Importer

- Knowledge Base Conn.

Collaxa Server

SDE Server

Microsoft SQL Server

RMI Server

MySQL Server

WordNet

Aspell

Dispatcher

FTP Web Service

AnyExec WeService

J2RE 1.4.2

Internet Explorer

WF Designer

Protege Client

Clientxxx.xxx.xxx.xxx

Public Network (Internet)

IV.1 Demo: Physical Deployment

Minium platform for full functionality:2 Host machines.At least 1 with Windows Server


HWPC: Xeon 3.4 GHz; RAM: 3.5 GB

O.S. Windows 2003 Server FS

Java Runtime O.S.J2SDK 1.4.2

Collaxa BPEL ServerPort : 9700

Application ServerJboss 3.2.3

KESB_WS_DR_2_Gis_DataImport_Service_1.0

KESB_WS_DR_2_Importer_1.0

KESB_WS_WF_1a_HAB_1.0

KESB_WS_WF_2a_Oilspill_1.0

KESB_WS_WF_3a_ShipDetection_1.0

KESB_WS_WF_3b_ShipDetection_1.0

KESB_WS_WF_4a_WindWaves_1.0

KESB_WS_WF_4b_WindWaves_1.0

Application ServerJboss 3.2.3

User Adaptation Engine EJB

Spatial Reasoning Engine WS – Port 7000

Text Search Engine EJB

Ontology Browser Engine EJB

PFI Wind Extract WS – Port 5014

PFI Oil Spill WS – Port 5006

PFI Import Service WS – Port 7001

PFI Crop Export WS – Port 5015

PFI Ship Detection WS – Port 5005

Knowledge Base Interface EJB

Web Server Publisher

Portal Servlet ContainerTOMCAT + JWSDP 1.4

JetSpeedPort : 80

Portal JSP

Protégé RMI Server

FTP WSPort : 5002

Processing ServiceInterface WSPort : 5001

GLUE Server

PMI

Dispatcher WSPort : 5003

GLUE Server

Apache Ant 1.6.1

Host 1: muis-dev Server 1/2IV.1 Demo: Physical Deployment: Host1

MySQLServer

Port : 3306

WordNet

Aspell

MS-SQLServer

Port : 1433

ESRI ArcSDEServer 8.3Port : 5151

Oil SpillShipHab

Crop Export

IDL VM


HWPC: Xeon 3.0 GHz; RAM: 1.0 GB

O.S. Windows 2003 Server FS

Internet Information Server (IIS) (FTP only)

Java Runtime O.S. J2SDK 1.4.2 b28

Servlet ContainerTOMCAT + JWSDP 1.4

MASS Interface(TOOLBOX)

Port : 80

Processing Machine Interface

GLUE Server

Processing ServiceInterface WSPort : 5001

FTP WSPort : 5002

Apache Ant 1.6.1

Oil SpillShipHab

Crop Export

IDL VM

Host2: muis-c Server

IV.1. Demo: Physical Deployment: Host2


IV.2.Demo Cases

1. Search Demonstration Cases1. Free text search of EO resources 2. Advanced search of EO resources3. Features Types source4. Spatial Query: Suspicious ship

2. Production Demonstration Cases1. Provide Ship Detection Function2. Ship Detection Service Workflow (Without GIS Importer)3. Ship detection MASS Publication Service


Purpose:

To demo the free textual search over the KB contents.The system explores the KB contents ontology, applies the ontology information structure, synonyms expansion using Wordnet, misspelling expansion using Aspell.

Inputs:On the portal, Introduce in Free Text query : ‘production

proceddure’.Execution:

Input text, and press Search button. Outputs:

n classes found (x)m instances found (x)

Conclusions:kes_Procedure has been found, and it is possible to prove

the behaviour of textual queries.

IV.2. Search Case 1: Freetext query for EO resource


Purpose: To demo that user can find out which production procedures process a certain type of product to generate a specific feature. Through the advanced search functionality, the user creates a query to exploit certain relations between certain classes. Inputs:

String logic All words: productionTarget Filters Domains: Oil Spill Domain Classes: KES Procedure

Execution:Introduce properly values in advanced search and push

Search button.Outputs:

n classes found (x)m instances found (x)

Conclusions:It is possible to refine textual searches.

IV.2. Search Case 2: Advanced Query for EO resource


Purpose: To demo that class navigation feature provided by the search HMI runs properly.To find out which instrument has generated the product from which a harmful alga bloom feature has been extracted. Inputs:Classes > KES Feature > ES Feature > Harmful Alga Bloom Feature > HAB Detection > MER_FR__1P > Medium Resolution Imaging Spectrometer (MERIS) > ENVISAT SatelliteExecution:

Execute previous inputs through browser.Outputs: ENVISAT Satellite description.Conclusions:It is possible to browse through ontology classes looking for desired capabilities.

IV.2. Search Case 3: Browse EO resources


Purpose: To demo that it is possible to build spatial querys. This demo detects ships which are probably guilty of causing or originating oil spills according to given model (model itself just pretends to be illustrative).

Execution:Go to Spatial search.Load “Query 6” from KESB Portal.Select an interest area.Press Search button.

Outputs:A set of results.

Conclusions:It is possible to establish spatial searches between data produced by Production Subsystem.

IV.2. Search Case 4: Spatial Query (suspicious ship)


Purpose: To show that it is possible to build spatial querys. This query detects ships which are though to be guilty of causing or originating oil spills according to given model

Inputs (Scenario):




SHIP

WIND

OIL SPILLabsolute near

temporally near

temporally near

similar direction

similar direction

Reasoning of the Query Model:

If: ships are near oil spills, in time and space,And ships and oil-spill bear in similar direction,And wind are blowing in similar direction than oil-spill in that time,Then, Ship is considered suspicious.

(model itself just pretends to be illustrative).


Execution:Go to Spatial search.Load “Query 6” from KESB Portal.Select an interest area.Press Search button.

Outputs:A set of results.

Conclusions:Spatial Search engine is able to find instances of feature objects that meet the query rules constraints, with a varying degree of certainty (result strenght), based on a fuzzy logic based spatial pattern matching similarity measure.



Purpose: To demonstrate how the user uploads the Ship Detect expert function to the KESB-PMS System. It will be uploaded the IDL module, which provides the Ship Detection algorithm. Inputs: see figure -->

IV.3. Production Case 1: Provide Ship Detection Function

Execution:Introduce all parameters correctly and push send button.

Outputs:Ship Detection will be deployed like a Web Service.Conclusions:It is possible to add functionalities to the system through Web Portal, in which expert user will can add theirs own algorithms.


Purpose: The Ship Detection service detects ships shape on the sea surface, from ERS-SAR-PRI or ENVISAT-ASAR-WSM satellite images and for each detected ship extracts its features. In this way, this Work Flow will be in charge of proving that this functionality runs properly. Inputs:Set of files :

- DAT_01.001- LEA_01.001

- VDF_DAT.001 Execution:

Open a browser with Oracle Bpel Server.Call Work Flow with following parameters :

IV.3. Production Case 2: Ship Detection WF Service

Outputs:A set of shape files with corresponding ships data extracting of satellite image.Conclusions:It is possible to create Work Flows combining different algorithms introduced into KESB system by expert user,

<executionRequest xmlns="http://acm.org/samples">

<FTP>false</FTP>

<inputDir>anonymous:[email protected]/data/input/</inputDir>

<outputDir>anonymous:[email protected]/data/output/</outputDir>

<inputParams>FR;%PARAMETERS%;\kesb\feature\extraction\data\

output\;UTV_Ship_p_;\kesb\feature\extraction\data\idl\input\DAT_01.001;\kesb\feature\

extraction\data\idl\input\LEA_01.001;\kesb\feature\extraction\data\idl\input\

VDF_DAT.001</inputParams>

</executionRequest>


Demo Case 2. Ship Detection Service Workflow 2/2Outputs:A set of shape files with corresponding ships data extracting of satellite image.Conclusions:It is possible to create Work Flows combining different algorithms introduced into KESB system by expert user,

IV.3. Production Case


Purpose: To demonstrate the KES-B capabilities of acting as a service provider and publish a ship detection service available to MASS clients. Therefore involves, publication of KES-B internal service and execution of the service through MASS portal.

Inputs:MASS registration files

TOOLBOX registration files JSP Connector

Execution:Loggin to http://services.eoportal.org and log in using user gtdprovider and password 318aktn7.

Outputs:A Ship Detection service published into MASS/SSE portal.

Conclusions:It is possible to declare public functionality through MASS/SSE portal.

IV.3. Production Case 3: Shipt Detection Service in MASS


IV.4. Questions and Answers

Thank You

ESRIN, Frascati, 6 th April 2005

Documents

Transcript of ESRIN, Frascati, 6 th April 2005