Http://cal044202.student.utwente.nl/~johan/butterfl/europe/uppsala/uppsalanight.jpg InvitedTalk Amit Sheth, Vipul Kashyap and Tarcisio Lima Large-Scale

http://cal044202.student.utwente.nl/~johan/butterfl/europe/uppsala/uppsalanight.jpg

InvitedInvitedTalkTalk

Amit Sheth, Vipul Kashyap Amit Sheth, Vipul Kashyap andand Tarcisio Lima Tarcisio Lima

Large-Scale Distributed Information Systems Lab.Large-Scale Distributed Information Systems Lab.

University of Georgia, Athens, GA USAUniversity of Georgia, Athens, GA USA

http://lsdis.cs.uga.eduhttp://lsdis.cs.uga.edu July 31 August 2

Uppsala Sweden

3th International Workshop on

Cooperative Information AgentsCIA'99

TraditionalEnvironment

ComputerAssisted

Environment

Challenging Application:Challenging Application:

learning and manipulating learning and manipulating

concepts on the Webconcepts on the Web

We should explore richer and much more complex new information requests

perspective Iperspective I

State-of-the-artState-of-the-art

How are we learning on the Web?How are we learning on the Web?

How are we learning on the Web?How are we learning on the Web?

perspective IIperspective IICurrent issues: searchCurrent issues: search

Browsing, classificationQuerying

keyword: ubiquitousattribute: on very limited set of metadatacontent: almost none

BLENDED BROWSING & QUERYING INTERFACEBLENDED BROWSING & QUERYING INTERFACE

SEMANTIC BROWSINGSEMANTIC BROWSING

ATTRIBUTE & KEYWORDQUERYING

ATTRIBUTE & KEYWORDQUERYING

uniform view of worldwide distributed assets of similar type

Targeted e-shopping/e-commerce

assets access

How are we taking advantage of the Web?How are we taking advantage of the Web?

How are we taking advantage of the Web?How are we taking advantage of the Web?

VideoAnywhereVideoAnywhere™™ Backend Architecture

VideoAnywhere is a trademark of UGARF, Inc.

Video Content Agent

METABASEMETABASEwith

user profiles

APIAPI UserAgent

ExtractorOntology

Table

ExtractorOntology

Table

EPGextractor agent

EPGextractor agent

EPG

VideoDomeextractor agent

VideoDomeextractor agent

FoxNewsextractor agent

FoxNewsextractor agent

VideoDome FoxNews

Black Boxextractor agent

Black Boxextractor agent

“bachelor’sparty”T

VW

eb

open sourcecontent open source and partner content original content

check/extract

check/extract

check/extract

check/extract

XML XMLXML XML

insertclean upthreadtranslate

tags

user query &browsingprofiling

daily topten assetspush

pullMETABASEMETABASEwith

user profiles

APIAPI

MermaidMermaidDDTSDDTS

Multibase, MRDSM, ADDS, Multibase, MRDSM, ADDS, IISS, Omnibase, ...IISS, Omnibase, ...

Generation IGeneration I

1980s1980s

Evolving targets and approaches in integratingEvolving targets and approaches in integratingdata and information data and information (a personal perspective)(a personal perspective)

DL-II projectsDL-II projectsADEPT,ADEPT,InfoQuiltInfoQuilt

Generation IIIGeneration III

1997...1997...

InfoSleuth, KMed, DL-I projectsInfoSleuth, KMed, DL-I projectsInfoscopes, HERMES, SIMS, Infoscopes, HERMES, SIMS,

Garlic,TSIMMIS,Harvest, RUFUS,... Garlic,TSIMMIS,Harvest, RUFUS,...

Generation IIGeneration II

1990s1990s

VisualHarnessVisualHarnessInfoHarnessInfoHarness

a society for ubiquitous exchange of (tradeable) information in all digital forms of representation;

information anywhere, anytime, any forms

perspective IIIperspective III

Looking ahead:Looking ahead:

Supporting information brokering (for decision making and learning) is much more than current e-commerce

involving products and services

A minimal definition on the Global Information Infrastructure

Information brokering is an architecture that

guides creation and management of

information systems and semantic-level

solutions to serve a variety of information

stakeholders (participants), including

providers, facilitators, consumers, and the

business involved in creating, enhancing

and using of information.

Semantic Information Brokering Semantic Information Brokering IntroductionIntroduction

How should we learn?How should we learn? (an example scenario)(an example scenario)

Graduate students in a College of Geography have a final

project in which a case of study is proposed. In the case, they

are supposed to help a City Council in making decisions over

the planning of a new landfill. This is a hands-on learning

exercise through the interaction

with a Digital EarthDigital Earth and the start

point would be to find the best

location for the landfill*.

Tacoma Landfill

* This scenario comes in support of one of the suggestions for

Digital Earth scenarios sampled by the “First Inter-Agency Digital

Earth Working Group, an effort on behalf of NASA’s inter-agency

Digital Earth Program.


bydefinition

bysemantics by

synonymy A first cut refinement leads us to the following information request:

FindFind a proper soil in sites not subject to flooding or high a proper soil in sites not subject to flooding or high

groundwater levelsgroundwater levels for a new landfill nearnear thethe industrial zone industrial zone.

Liquefaction phenomenon cannot occurLiquefaction phenomenon cannot occur.

Find a landfill sitelandfill site for a new landfill near the source of the wastessource of the wastes.

The earthquakes’ impacts must be evaluatedThe earthquakes’ impacts must be evaluated..

A high level information request would be:


Adding on-the-fly user constraints while processing the information request:

Retrieve satellite images in 12-meter resolution or higher,Retrieve satellite images in 12-meter resolution or higher,

looking for soils with permeability rate < 10 looking for soils with permeability rate < 10 (silty clay loam)

for a new landfill

whose distance from the city industrial park is less than 5km.whose distance from the city industrial park is less than 5km.

Using the images’ coordinates, forecast seismic activity up to Using the images’ coordinates, forecast seismic activity up to

moderate magnitude moderate magnitude (5 - 5.9, Richter scale) in the pointed areas. in the pointed areas.

domain specific metadata; correlation among multiple ontologies; return results in multiple media (in this case, images and a simulation)


Partial sample ontologies for semantic information brokering:

LANDUSE

COMERCIAL

INDUSTRIAL

RURAL

RESIDENTIAL

AGRICULTURAL

MILITARYRECREATIONAL

LAND(SITE)

CULTIVATEDAREA

GREENLANDAREA LAND

BANK

ZONING

LANDFILLSITE

WASTEDISPOSAL

RECYCLING

HAZARDOUS

LANDFILLRESOURCE REC.

SOLID SEWAGE

shredding

magneticseparation

screening

washing

NATURALDISASTER

EARTHQUAKE

causes

LANDSLIDE

VOLCANO

STORMFLOOD

FIRE

AVALANCHE

TSUNAMI

causes

causes

causes


A sample result (depending on information providers) could be:

OrbView-4’s stereo imaging capacity providing 3-D terrain images

Hyperspectral data will be valuable for identifying material types

images source: http://www.orbimage.com

5km

industrial zoneindustrial zone

identified landfill siteidentified landfill site

The students now have the information requested for

helping the City Council in the planning of the new landfill

Semantic Information Brokering Semantic Information Brokering IntroductionIntroduction

INFORMATION HETEROGENEITY

Semantic Heterogeneity

Structural, Representational/Schematic Heterogeneity

Syntactic, Format Heterogeneity

SYSTEM HETEROGENEITY

Information System HeterogeneityDigital Media Repository Management Systems

Database Management Systems, (heterogeneity

of DBMSs, data models, system capabilities such

as concurrency control and recovery)

Platform HeterogeneityOperating System (heterogeneity of file system, naming, file types, operation, transaction support, IPC)

Hardware/System (heterogeneity of instruction set, data representation/coding)

Semantic InteroperabilitySemantic Interoperability

Structural InteroperabilityStructural Interoperability

Syntactic InteroperabilitySyntactic Interoperability

System InteroperabilitySystem Interoperability

A taxonomy for heterogeneity and interoperabilityA taxonomy for heterogeneity and interoperability

Impacts on Information OverloadImpacts on Information Overload

Heterogeneity:

differences in structure;

differences in query languages;

semi-structured or unstructured data;

media heterogeneity;

terminology (and language) heterogeneity;

contextual heterogeneity

Heterogeneity...Heterogeneity... … … is a Babel Tower!!is a Babel Tower!!

SEMANTIC INTEROPERABILITYSEMANTIC INTEROPERABILITY

metadata

ontologies

contexts

SEMANTIC HETEROGENEITYSEMANTIC HETEROGENEITY

Impacts on Information OverloadImpacts on Information Overload

Globalization: challenge of the scale

information resource discovery;

modeling of information content;

querying of information content

information focusing

information correlation

Much more than returning documents with potentially relevant information.

Handling the information overloadHandling the information overload

S E M A N T I S E M A N T I C S C S

S T R U C T U R ES T R U C T U R E

S Y N T A XS Y N T A X

S Y S T E MS Y S T E M

C O N S U M E R SC O N S U M E R S

F A C I L I T A T O R SF A C I L I T A T O R S

P R O V I D E R SP R O V I D E R SD

A T

AD

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D A

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I N O

L O

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T E

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L O

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Stakeholders and beneficiaries in information brokering









A T

AD

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Stakeholders and beneficiaries in information brokering

key player on the GII, who help to match key player on the GII, who help to match

the consumersthe consumers REQUESTS REQUESTS with the with the

INFORMATIONINFORMATION exported by the providers exported by the providers



key player on the GII, key player on the GII,

which which USEUSE information informationkey player on the GII, key player on the GII,

which which EXPORTEXPORT information information

P R O V I D E R SP R O V I D E R S


Information facilitatorsInformation facilitators enable brokeringbrokering between the information consumer and provider that may be defined as:

arbitration between consumers and providers for resolving the

information impedance, the differing worldviews of the

consumers and providers;

dynamic re-interpretation of information requests for

determination of relevant information services and products;

dynamic creation and composition of information products

after suitable assembly or correlation of information

components available from the various providers, or other

value addition activities.

INFORMATION/DATAINFORMATION/DATAOVERLOADOVERLOAD

INFORMATION PROVIDERS

InformationSystem

DataRepository

InformationSystem

INFORMATION CONSUMERS

User Query

User Query

User Query

INFORMATION BROKERINGINFORMATION BROKERING

InformationSystem

DataRepository

InformationSystem

InformationRequest

InformationRequest

InformationRequest

Corporations

Universities

People

Government

Programs

Newswires

Universities

Corporations

Research Labs

Handling the information overload

information brokering stakeholder perspective









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Levels of information brokeringLevels of information brokering

data brokering standards-based interoperabilitydata brokering standards-based interoperability

metadata-based brokeringmetadata-based brokering

semantic information brokeringsemantic information brokering


The role of facilitators on the GII:

design and construction of multimedia views using metadata

descriptions from standardized terminologies available to the

consumers;

maintenance and storage of associations between the digital

data exported by the providers and the metadata descriptions

designed by it;

bridge between providers and consumers

use of domain specific ontologiesdomain specific ontologies to characterize

vocabularies and terminological relationships in the

interoperation, reducing information overload


Services the facilitators can offer:

provide a collection of standard terminologies captured as

domain specific ontologies to the consumers, used by the

providers to construct metadata descriptions;

support the interoperation across multiple domain specific

ontologies;

support the transformation of information requests across

different ontologies, minimizing the loss of information.

key objective of the approach: enable semantic interoperability,

reducing the problem of knowing the structure and semantics

of data in the huge number of information sources.

Appropriateness of multi-agent systemsAppropriateness of multi-agent systems

Extensions to agent functionalities to support semantic information brokering:

capture, view and interrogate the semantics of the underlying

(multimedia) data;

request information without regards to the underlying data

type, structure, format or media in which the information may

be represented or stored;

support interoperation across multiple standard terminologies

or domain specific ontologies.

Demonstration Scenarios Demonstration Scenarios VOLCANOES ACTIVITYVOLCANOES ACTIVITY

Some volcanoes are more active than others, and a few

are in a state of permanent eruption, at least for the

geological present. Volcanoes may become quiescent

(dormant) for months or years. The danger to life posed by

active volcanoes is not limited to eruption of molten rock or

showers of ash and cinders.

Mudflows that melt ice and

snow on the volcano's flanks

are equally hazardous*.

* Encarta® 98 Desk Encyclopedia © & 1996-97 Microsoft Corporation.All rights reserved. Pu'u'O'o, Hawaii

Demonstration Scenarios Demonstration Scenarios VOLCANOES ACTIVITYVOLCANOES ACTIVITY

A sample information request:

Find information on volcanoesvolcanoes and also find how

these volcanoes affect/cause landslidesaffect/cause landslides and tsunamistsunamis.

Some of the ontologies involved in processing this information request are:

• Ontology for GIS Datasets;

• Ontology for Natural Disasters;

• Ontology for Volcanoes;

• Ontology for Landslides;

• Ontology for Tsunamis.

TRY HERE THIS AND OTHER CONCEPT DEMOS

Agents for media independent correlationsAgents for media independent correlations

UCSB’s Institute for CrustalStudies Earthquake Collection

Seismicity(SQL):

USGS GlobalLand Info System

Land(SQL)

NASA’s EOS EarthImages and Data

Image Features(IP routines)

ProviderAgent

ProviderAgent

ProviderAgent

CorrelationAgent

MetadataAgent

ConsumerAgent

Satelliteimage:

Seismicforecast:

Landfill site:

Objects displaying

USER INTERFACE

metadata extraction

metadataexportation

informationrequests

GUI construction

user constraints

metadata constraints

FINAL ANSWER (objects satisfying constraints)

control strategy

metadata extraction

metadata extraction

Information content

Agents for inter-ontology interoperationAgents for inter-ontology interoperation

ReformulationAgent

IntegrationAgent

OntologyAgent

ConsumerAgent

OntologyAgent

Ontology Aaerial-videography

Ontology Bland use classification

InformationRequest

ReformulatedRequest

InformationRequest

Ontology AB

INTEGRATION

Integrating aerial-videography with the

process of land use classification

is identified as an exciting example of

SEMANTIC INFORMATION BROKERINGSEMANTIC INFORMATION BROKERING,

leading to the gathering of ground-truth

satellite image interpretation and

post-classification land-cover mappings

In our starting example scenario:

Summary Summary

TextTextStructured DatabasesStructured Databases DataData Syntax,Syntax,

SystemSystem Federated DBFederated DB

Semi-structuredSemi-structured MetadataMetadata Structural,Structural,SchematicSchematic

Mediator,Mediator,Federated ISFederated IS

Visual,Visual,Scientific/Eng.Scientific/Eng. KnowledgeKnowledge SemanticSemantic

Knowledge Mgmt.,Knowledge Mgmt.,InformationInformationBrokering,Brokering,

Cooperative ISCooperative IS

ConclusionsConclusions

[email protected] - http://lsdis.cs.uga.edu

From Procedures, Objects, Components to Agentswe have a nice abstraction of computation.Now let’s apply them to address semantic-level issues

Information Brokering gives a framework for enabling

complex decision making and learning involving

heterogeneous digital media on the GII.

Documents

Http://cal044202.student.utwente.nl/~johan/butterfl/europe/uppsala/uppsalanight.jpg InvitedTalk Amit Sheth, Vipul Kashyap and Tarcisio Lima Large-Scale