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gtd SISTEMAS DE INFORMACIÓN. KES-B Project Final Presentation. ESRIN, Frascati, 6 th April 2005. Agenda. PART 0. ESRIN Presentation. PART I. Introduction I.1 Background I.2. Objectives I.3. Added Values I.4. Project Organisation PART II. Technical Presentation - PowerPoint PPT Presentation
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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
2/xxKES-B Project Final Presentation – ESRIN, Frascati, 6th April 2005
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
3/xxKES-B Project Final Presentation – ESRIN, Frascati, 6th April 2005
ESRIN Presentation
ESRIN Presentation
4/xxKES-B Project Final Presentation – ESRIN, Frascati, 6th April 2005
Introduction
PART 0. ESRIN PresentationPART 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
5/xxKES-B Project Final Presentation – ESRIN, Frascati, 6th April 2005
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.
6/xxKES-B Project Final Presentation – ESRIN, Frascati, 6th April 2005
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.
7/xxKES-B Project Final Presentation – ESRIN, Frascati, 6th April 2005
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
8/xxKES-B Project Final Presentation – ESRIN, Frascati, 6th April 2005
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:
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I.4 Project Organisation
10/xxKES-B Project Final Presentation – ESRIN, Frascati, 6th April 2005
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
PART 0. ESRIN Presentation
Technical Presentation
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
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II.1. Implementation Approach
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II.1. Implementation Approach: OWL Ontologies
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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
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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
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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)
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II.3. Architecture: Production Collaboration Sequence
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II.3. Architecture: Search Collaboration Sequence
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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
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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
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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
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II.4. Ontology: Deployment of Ontology Components
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II.4. Ontology: KES ontology: kes_Resources taxonomy
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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.
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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
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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.
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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
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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
Get/Set Slot(Protégé Instances)
n clients
KBI Data(Remote)
Client
n clients
RMI
ProtégéProject
(Remote)Client
n clients
Get/Set Slot(Protégé Instances)
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
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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
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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
30/xxKES-B Project Final Presentation – ESRIN, Frascati, 6th April 2005
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
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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
32/xxKES-B Project Final Presentation – ESRIN, Frascati, 6th April 2005
II.5. Sub Systems: PMS
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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
34/xxKES-B Project Final Presentation – ESRIN, Frascati, 6th April 2005
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
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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
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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
37/xxKES-B Project Final Presentation – ESRIN, Frascati, 6th April 2005
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
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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
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II.5. Sub Systems - MIS
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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
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PART 0. ESRIN Presentation
Conclusions
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
42/xxKES-B Project Final Presentation – ESRIN, Frascati, 6th April 2005
III.1. Conclusions: Structure
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III.1. RL1 – Results on Technology
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III.1. RL2: Results on System Design
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III.1. Results on Ontology Architecture
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III.1. Results on System Architecture
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III.1. RL3: Results on System Functions
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III.1. Results on Search Functions
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III.1. Results on Production Functions
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III.1. Results on Knowledge Management Functions
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III.2. Ways Forward: in the short-term
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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
53/xxKES-B Project Final Presentation – ESRIN, Frascati, 6th April 2005
III.2. Ways Forward: in the mid-long term (perspectives)
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III.2. Ways Forward: in the mid-long term (perspectives)
WebServices
SemanticWeb
GridComputing
SemanticGrid
Semantic Web
Services
GridServices
Semantic for Grid
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III.3. Questions and Answers
Thank You
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PART 0. ESRIN Presentation
IV. Demo
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
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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
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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
59/xxKES-B Project Final Presentation – ESRIN, Frascati, 6th April 2005
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
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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
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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
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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
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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
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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)
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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):
IV.2. Search Case 4: Spatial Query (suspicious ship)
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IV.2. Search Case 4: Spatial Query (suspicious ship)
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).
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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.
IV.2. Search Case 4: Spatial Query (suspicious ship)
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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.
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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:anonymous@kes-b.gtd.es/data/input/</inputDir>
<outputDir>anonymous:anonymous@kes-b.gtd.es/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>
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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
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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
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IV.4. Questions and Answers
Thank You
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