Post on 31-Dec-2015
description
04/19/23 Gio - CERN 1
Heterogeneous Information Management
June 2000
Gio WiederholdStanford University
prepared for CERN seminar, June 2000
04/19/23 Gio - CERN 2
Abstract
Information is created by applying knowledge (enoded as programs or rules) to collected data and message received.
Data and computation resources are provided by a variety of suppliers, public and private.
The autonomy of the suppliers causes heterogeneity and inconsistencies. The number of potential suppliers and their autonomy also creates information overload
To cope with these issues novel intermediate services are needed, opening up new opportunities. Many traditional relationships among consumers and vendors will change.
We will present the concepts and status of such services. Collaboration, security, and payment schemes are some of the
considerations.
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Outline
• Background for Mediated Systems• Motivation and Functions needed• Architecture• Current Status
• Resolving Semantic Heterogeneity• Research Directions• Background
– Maintenance– Research Projects– Integration of Simulation Information
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Evolution of mediation
W2 W1
D2
D6D4
W3
I1
D1D5
I2
M1 M2
A1A4 A5A2
A6
a.
b.
A3
c.
d. e.
datasources
wrappers
mediators
network
integrators
applications
D3
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Transforming Data to Information
Application Layer
Mediation Layer
Foundation Layer
data and simulation resources
value-added services
users at workstations
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Data and Knowledge
Information is created at theconfluence ofdata -- the state & knowledge -- the ability to select and project the state into the future
Knowledge LoopKnowledge LoopData LoopData Loop
EducationEducation
RecordingRecording
ActionAction
StorageStorage
SelectionSelection
IntegrationIntegration
SummarizationSummarization
Decision-makingDecision-making
State changesState changes
AbstractionAbstraction
ExperienceExperience
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Definition*
A mediator is a software module that exploits encoded knowledge about certain sets or subsets of data to create information for a higher layer of applications.
It should be small and simple, so that it can be maintained by one expert or, at most, a small and coherent group of experts.
* Wiederhold: IEEE Computer March 1992
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Information overload Data starvation
• More databases– public & corporate
• Faster communication– digital– packeting: TCP-IP, ATM
• World-wide connectivity– Internet & Intranets– world-wide web
• Disintermediation– ubiquitous publishing
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Change in Supply vs Demand
What information consumes is rather obvious, it consumes the attention of its recipients.
Hence a wealth of information creates a poverty of attention, and a need to allocate that attention efficiently among the overabundance of information sources that might consume it.
[Herbert Simon]
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Function of Mediation
Apply Domain-specific Specialist Knowledge to add value
• to locate data sources• to convert for consistency• to integrate from diverse sources• to describe data for processing• to abstract for insight / models• to extrapolate to new situations• to summarize for presentation
INFORMATION
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Interfaces
Service Service interfaceinterface
Resource accessResource access interfaceinterface
User interfaceUser interface
Real-worldReal-world interfaceinterface
Human-computerHuman-computer InteractionInteraction
Application-Application- specific codespecific code
Domain-Domain- specific specific codecode
Source-Source- specificspecific codecode
MEDIATIONMEDIATION
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Making data relevant
• Data reduction• Data abstraction
– Level changing– Summarization– Exception search– Level change to integrate with
other data sources
• Follow Customer Model: hierarchical, divide-and-conquer, a common paradigm
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Functions inside Mediation
Selection
Summarize
Transform
Inte- -gration
Hetero-
genous
resources
articulation
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Status of Mediation Technology
Today• Handcrafted• Expert consults with
programmer • Programmer codes the
knowledge needed• Resource changes
require advise, program update
Future• Generated from
models• Domain Expert
maintains models• Specification
determines functions • Resource changes
trigger regeneration
15Gio - CERN04/19/23
Coverage of Current DARPA I3 Efforts
Databases / Web / Text / Simulation
Facilitation(auto linking)
Maintenance(rule technology?)
Discovery(web,schemasearching)
Wrapping (syntactical heterogeneity)
Integrationover sources
Abstractionfor relevanceto customer
Mediators for multiple domains
Caching /History
:-[
:-[
:-[
:-(
:-(
:-)
:-(:-(
Securityfor cooperation:-(
:-|
:-|
:-)
Good progressGood progress / / active researchactive research / / related workrelated work / / poor coveragepoor coverage
:-)
:-[
:-)
(( ]] || ))
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Mediator Design Principle
Transform Data into Information
Match
Costumer Model
Hierarchical
to
Resource Model
General network
(and maintain models)
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Heterogeneity among Domains
If interoperation involves distinct
domains mismatch ensues• Autonomy conflicts with consistency,
– Local Needs have Priority,– Outside uses are a Byproduct
Heterogeneity must be addressed• Platform and Operating Systems • Representation and Access Conventions • Naming and Ontology
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Unsolved problem in Interoperation
Common assumption in assembling and integrating distributed information resources
• The language used by the resources is the same• Sublanguages used by the resources are subsets of a
globally consistent language
This assumption is provably false.
Working towards the goal of global consistency is
1. naïve -- the goal cannot be achieved
2. inefficient -- languages are efficient in local contexts
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Ontology: components .
We represent the contents and structure of a languages by its ontology:
• a set of well-defined terms, which delimit the domain of discourse
• relationships among those terms, chosen from a limited set
a formalizable subset of expert knowledge
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SKC’s grounded definition .
• Ontology: a set of terms and their relationships• Term: a reference to real-world and abstract objects• Relationship: a named and typed set of links between objects• Reference: a label that names objects• Real-world object: an entity instance with a physical manifestation• Abstract object: a concept which refers to other objects
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Where are Ontologies found?
Ontologies allow communication among partners in enterprises (rarely in machine-readable form)
Relationships determine meaning - parent, school, company
Variable and Class names in SoftwareDatabases use ontologies during design
in their E-R diagrams (implicitly) and to represent the leaf nodes in their schemas.
Knowledge-bases use term ontologies (often explicitely), add class definition (to hold instances), constraints, and operations among the terms.
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Establishing Ontologies
Top-down: – Commonly acceptable UPPER layers
Domain-specific– Analysis and Sharing tools– Model and Object-type based
Bottom-up– Wordlist creation from task-specific collections– Database models, schemas, and contents
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Large Ontologies: good or bad?
Have all the Knowledge together+ simple for customers of KBs– hard for owners of KBs, must synchronize with many others– in the limit -- everybody must be globally consistent
Large KB will cover multiple / all domains created by a committee -- slow
maintained by a committee -- costly
Differences in level of abstraction -- efficiency homeowner: nail carpenter: sinker, brad, boxnail, . . .
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Domain ontology assumption .
• a domain will contain known objects• the object configuration is consistent• within a domain all terms are consistent &• relationships among objects are consistent
• context is implicit in use• explicit context is needed for external use
No committee is needed to forge compromises * within a domain Compromises hide valuable details
Domain Ontology
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SKC Objective
Provide for Maintainable Ontologies
• devolve maintenance onto many domain-specific experts / authorities
• provide an algebra to compute composed ontologies that are limited to their articulation terms
• enable interpretation within the source contexts
SKC
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Conservative assumption !
When dealing with multiple ontologies one can never be sure that identically or similarly spelled words mean the same thing,
I.e, refer to exactly the same set of real-world objects under all current and future conditions
• Common, optimistic assumption: Meaning is identical– Gets worse when terms are stemmed
• SKC, conservative or pessimistic assumption: Meaning never matches, unless there is a match rule– number of matching rules is reduced by focusing on the
articulation
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An Ontology Algebra
A knowledge-based algebra for ontologies
The Articulation Ontology (AO) consists of matching rules that link domain ontologies
Intersection create a subset ontology keep sharable entries
Union create a joint ontology merge entries
Difference create a distinct ontology remove shared entries
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Sample Operation: INTERSECTION
Source Domain 1:Owned and maintained by Store
Result contains shared terms
Source Domain 2:Owned and maintainedby Factory
Terms usefulfor purchasing
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INTERSECTION support
Store Ontology
Articulation ontology
Matching rules that use terms from the 2 source domains
Factory Ontology
Terms usefulfor purchasing
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Sample Intersections
Shoe Store• Shoes { . . . }• Customers { . . . }• Employees { . . . }
size = size color =table(colcode)
style = style
Ana-tomy {. . . }
• Material inventory {...}• Employees { . . . }• Machinery { . . . }• Processes { . . . }• Shoes { . . . }
Shoe Factory
Hard-ware
Articulation ontologymatching rules :
foot = foot Employees Employees Nail (toe, foot) Nail (fastener). . . . . .
Department Store
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Other Basic Operations
typically priorintersections
UNION: mergingentire ontologies
DIFFERENCE: materialfully under local control
Arti-culation ontology
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Features of an algebra
Operations can be composed
Operations can be rearranged
Alternate arrangements can be evaluated
Optimization is enabled
The record of past operations can be
kept and reused
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Articulationknowledgefor U
U
U
(A B)U
(B C)U
(C E)
Knowledge Composition
Knowledge resource
B
Knowledge resource
A
Knowledge resource
C
Knowledge resource
D
U
(C D)
U
(B C)
Articulation knowledgefor
Composed knowledge forapplications using A,B,C,E
Knowledge resource
E
U
(C E)
Legend:
U : union
U
: intersection
Articulationknowledgefor (A B)
U
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Sample Processing in HPKB
• What is the most recent year an OPEC member nation was on the UN security council?
– Related to DARPA HPKB Challenge Problem
– SKC resolves 3 Sources
• CIA Factbook ‘96 (nation)
• OPEC (members, dates)
• UN (SC members, years)
– SKC obtains the Correct Answer
• 1996 (Indonesia)
– Other groups obtained more,
but factually wrong answers
– Problems resolved by SKC
* Factbook has out of date OPEC & UN SC lists
– Indonesia not listed
– Gabon (left OPEC 1994)
* different country names
– Gambia => The Gambia
* historical country names
– Yugoslavia
• UN lists future security council members
– Gabon 1999
• intent of original question
– Temporal variants
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Tools to create articulations
Graph matcherforArticulation- creatingExpert
Vehicle ontology
Transport ontology
Suggestionsfor articulations
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continue from initial point
Also suggest similar terms for further articulation:
• by spelling similarity,• by graph position• by term match repository
Expert response:1. Okay2. False3. Irrelevant to this articulation
All results are recorded
Okay’s are converted into articulation rules
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Candidate Match Repository
Term linkages automatically extracted from 1912 Webster’s dictionary *
* free, other sources .have been processed.
Based on processing headwords definitions using algebra primitives
Notice presence of 2 domains: chemistry, transport
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Primitive Operations
Unary• Summarize -- structure up• Glossarize - list terms• Filter - reduce instances• Extract - circumscription
Binary • Match - data corrobaration• Difference - distance
measure• Intersect - schem discovery• Blend - schema extension
Constructors• create object• create setConnectors• match object• match setEditors• insert value• edit value• move value• delete valueConverters• object - value• object indirection• reference indirection
Model and Instance
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Future: exploiting the result
Processing & query evaluation is best performed within Source
Domains & by their engines
Result has linksto source
Avoid n2 problem of interpretermapping as stated by Swartout as an issue in HPKB year 1
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SKC Synopsis
• Research: Reliable query answers from heterogeneous, imperfect data sources
• Sources:– General: CIA World Factbook ‘96, UN www, OPEC www
Webster’s Dictionary, Thesaurus, Oxford English Dictionary
– Topical: OPEC, BattleSpace Sensors, Logistics Servers
• Client: DARPA High Performance Knowledge Base
(HPKB) project
• Theory: Rule-based algebra– Translation & Composition primitives
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Innovation in SKC
• No need to harmonize full ontologies• Focus on what is critical for interoperation• Rules specific for articulation• Potentially many sets of articulation rules
• Maintenance is distributed– to n sources– to m articulation agents
is m < n2 , depending on architecture density a research question
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Domain Specialization
• Knowledge Acquisition (20% effort) &• Knowledge Maintenance (80% effort *)
to be performed by• Domain specialists• Professional organizations• Field teams of modest size
Empowermentautomouslymaintainable
* based on experience with software
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SKC Summary .
• Algebra enables Interoperation bydealing explicitly with differences by knowledgeidentifying maintenance domainskeeping sources autonomous
• Assumes domain has a common ontologycomposing domain ontologies requires the algebra to manage the
linkages where articulation occursprocesses are best executed within the domains
• Knowledge about articulation is disjoint allows integration specialists to work independentlysupports multiple intersections and views
• Maintenance is structured and partitioned
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Current SKC Directions
• Experience with real world (imperfect) data confirms validity of our approach
– Expert sources are better maintained than general sources– Rules applied to multiple sources provide more reliable and
accurate query results– Component architecture enables scalable, maintainable
knowledge base development
• Porting the concepts to the DARPA Markup Language (DAML) setting
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Mediation Research Topics
• Mediator management and maintenance• Representation of knowledge and customer models• Balancing dynamic and warehouse solutions• Formalization of semantic heterogneities
– many levels and types – roles for wrappers vs. mediators vs. applications– scalability by partitioning -- make it simple!– Domain Ontologies --- tools, validation, . . .
• Effect of object paradigm and method-based access• Service and business models • New types of information systems
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IntegrationScience
IntegrationScience
ArtificialIntelligence
knowledge mgmtdomain expertise
uncertainty
ArtificialIntelligence
knowledge mgmtdomain expertise
uncertainty
Systems Engineering
analysisdocumentation
costing
Systems Engineering
analysisdocumentation
costing
Databasesaccessstoragealgebras
Databasesaccessstoragealgebras
Long Range Science Vision
Integration Methods
GISSpatial is special.
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Background Material:
• Technology Sources• Maintenance• Projects• Information about the Future
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Interfaces
Application Application Mediator Mediator{OQL, KQML, ...}{OQL, KQML, ...}
Mediator Mediator Data sources Data sources{SQL, TQL, XML, … }{SQL, TQL, XML, … }
Data Data real worldreal world{sensors, clerks, … }{sensors, clerks, … }
Human Human Computer Computer{x-widgets, HTML}{x-widgets, HTML}
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Support for KB-Algebra
• Ontolingua [Gruber, Fikes @ Stanford KSL]: Repository for Domain Terminologies
Used for mechanical design, bibliographies, catalogs
• LOOM [MacGregor@ USC ISI]: Classification-based Expert System
Helps in structuring and processing ontologies
• PROTÉGÉ [Musen@ Stanford MIS] Reuse
• Penguin [Barsalou, Keller@ Stanford MIS, CIFE]: Object manipulation based on Relational Algebra
Used for genetics laboratory, building design
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Getting there:Available Technology/Science
CachingCachingCachingCaching
Uncertainty algebrasUncertainty algebrasUncertainty algebrasUncertainty algebras
GISGISGISGIS
Temporal AlgebrasTemporal AlgebrasTemporal AlgebrasTemporal Algebras
Active DatabasesActive DatabasesActive DatabasesActive Databases
AgentsAgentsAgentsAgentsWeb Search ToolsWeb Search ToolsWeb Search ToolsWeb Search Tools
Security FiltersSecurity FiltersSecurity FiltersSecurity Filters
Object BasesObject BasesObject BasesObject Bases
KnobotsKnobotsKnobotsKnobots
WrappersWrappersWrappersWrappersDB ViewsDB ViewsDB ViewsDB Views
High Perf.Comm.High Perf.Comm.High Perf.Comm.High Perf.Comm.
Simulation AccessSimulation AccessSimulation AccessSimulation Access
Database ModelsDatabase ModelsDatabase ModelsDatabase Models
Internet BillingInternet BillingInternet BillingInternet Billing
Customer ModelsCustomer ModelsCustomer ModelsCustomer Models Constraint ManagementConstraint ManagementConstraint ManagementConstraint Management
Case-based ReasoningCase-based ReasoningCase-based ReasoningCase-based Reasoning
Distributed Storage SystemsDistributed Storage SystemsDistributed Storage SystemsDistributed Storage Systems
Multimedia InterfacesMultimedia InterfacesMultimedia InterfacesMultimedia Interfaces
CircumscriptionCircumscriptionCircumscriptionCircumscription
Communication StandardsCommunication StandardsCommunication StandardsCommunication Standards
Domain OntologiesDomain OntologiesDomain OntologiesDomain Ontologies
Text & Speech ProcessingText & Speech ProcessingText & Speech ProcessingText & Speech Processing
Public DatabasesPublic DatabasesPublic DatabasesPublic Databases
GISGISGISGIS
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Fat versus thin mediators
• too broad:
hard to maintain, needs a committee
• too thin: insufficient added value
• Too fat: hard to
compose
• Too narrow: few costumers
domain scope
service scope
Just right
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Maintenance is good for you
rela
tive
an
nu
al
mai
nte
nan
ce c
ost
dep
reci
atio
n =
1 /
lif
etim
e
automobile hardware software automobile hardware software
100%100%
4040
00
2020
7070
3030
1010
8080
9090
6060
5050
life
tim
eli
feti
me
yearsyears 10 10
44
22
77
33
11
88
99
66
55
1313
1111
1212??
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Client-Server Architecture
Client system
data and simulation resources
Fast build of clients by resource reuse
s X
Changes (x) are difficult,can affect many clients
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Systems with Mediators
Applications . . . .
Mediators . . . . . .
Data Resources . . .
Gio Wiederhold. 1995
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Growth through Reuse
New Application
Prior & Revised Mediators
Extended Data Resources
Gio Wiederhold. 1995
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Linear O(n) Cost of Growth-- now
O(n2)
• Data changes only affect some mediators; only in their domain
• Mediators can
1. supply old information to n-1 prior applications
2. provide better information to the new application
3. be partially or completely reused
• New applications, using the new data, can be developed and inserted dynamically
27
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A mediator is not just static software: Knowledge ages
ApplicationInterface
Resource Interfaces
Owner / Creator Maintainer Lessor - Seller Advertisor
Changes ofuser needs
Domainchanges
Resource changes
Models, programs,rules, caches, . . .
Software & People
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Roles
Computer Scientists• Provide tools
– adapatation– integration– matching– composing
• Assess Standards• Assure scalability
Domain Experts• Learn to use the tools• Select resources• Assess their value • Rank their quality • Resolve semantics• Get client feedback• Give provide feedback
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Assigning maintenance responsibility
a. Source data quality –supplier database, files, or web pages
b. Interface to the source – wrapper, supplier or vendor for supplier
c. Source selection – expert specialist in mediator
d. Source quality assessment – customer input to mediator
e. Semantic interoperation – specialist group providing input to the mediator
f. Consistency and metadata information – mediator service operation or warehouse
g. Informal, pragmatic integration – client services with customer input
h. User presentation formats – client services with customer input
Services
Sources
Customers
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Sample projects
• Tsimmis at Stanford• E-Commerce in Digital Libraries• INEEL: information integration for environmental
restoration• MIFT: feedback for training• Civil Engineering and Architecture• F-22• SimQL• Security
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Projects at Stanford DB group
Data Mining.Data Mining.
Mediator & Wrapper Mediator & Wrapper
Generation.Generation.
Warehousing.Warehousing.
Security Mediators.Security Mediators.
Megaprogramming.Megaprogramming.
Simulation Access.Simulation Access.
Changes, Consistency,Changes, Consistency,
and Configurations.and Configurations.
Data Mining.Data Mining.
Mediator & Wrapper Mediator & Wrapper
Generation.Generation.
Warehousing.Warehousing.
Security Mediators.Security Mediators.
Megaprogramming.Megaprogramming.
Simulation Access.Simulation Access.
Changes, Consistency,Changes, Consistency,
and Configurations.and Configurations.
TSIMMISTSIMMIS
CHAIMS CHAIMS SimQLSimQL
TIHITIHI
C3C3
MIDASMIDAS
WHIPSWHIPS
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The TSIMMIS ProjectRamana Yerneni, Yannis Papakonstantinou, ...
• Objective: Support mediation technology– integrated access to distributed, autonomous,
heterogeneous data sources, using object fusion– wrapper toolkit to rapidly create wrappers, based on
source specification, a uniform interface to heterogeneous sources
– mediator toolkit to rapidly construct mediators, based on a mediator specification, to integrate data from a set of wrappers
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Investors Need to Fuse Informationfrom Multiple Sources .
• group together information about
the same real-world entity• remove redundancies • resolve conflicts
WWW
Ticker Tape Personaldatabase
NetworkNetwork
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An Integration Architecture
ClientApplication
business reports
portfolios for each company
stock market prices
WrapperWrapper
TickerTape Dialog
Mediator
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Additional Challenge: Sources Without a Well-Structured Schema
• semistructured– irregular– deeply nested
• incomplete schema knowledge– autonomous– dynamic
• World Wide Web• SGML documents• genome, chemical
structures• bibliographic
information• files
Examples
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Wrappers & Mediators from High-Level Specifications
Wrapper
Client
Mediator SpecificationInterpreter
DeclarativeMediatorSpecification
Source Source
DeclarativeSource
Specifications
Mediator
Wrapper
Wrapper SpecificationInterpreter
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E-money
Services must be paid for• Incentive for creation and improvement• price proportional to value added, often small
• profit f (cost, market, price, overhead )
• price low per item, so overhead must be low
Simple payment (no credit accounts, checks)
Enabled through secure signatures
yes
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E-Commerce in the Digital Library
DeliveryCryptolope
DigiBoxHTTPE-mail
Shopping Models: Pay-per-view, Subscription, Session, Shareware, Auctions, Site License,
MajorIntegration
Problem
Steven Ketchpel & DL Economics Group
PaymentCyberCashDigiCash
First VirtualSET
Gift Certificate, Layaway, Pre-paid vouchers, … .
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Shopping model: merchant-independent logic controlling flow of business model
Example shopping models:
Order, Pay, (Deliver 52 times)(1 month; Order, Deliver) Pay
Bill
Start Transfer $
OrderComplete
Payment Complete
Event Handlers
2 1
34
Even
t Han
dlers
Even
t Han
dlers
Proxy event handlers translate from
native applications to shopping model defined protocols
Abstract API allows application to
interact with many different services in a consistent way
Abstract API allows application to
interact with many different services in a consistent way
Payment/Delivery/Other Services
Customer Merchant
Event Handlers
State Information
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TSIMMIS Status
• Mediator Specification Interpreter running on Ultrix, AIX, OSF.
• 9000 lines of C/C++ code• 4000 C++ lines of Server/Client Support Libraries • Integration of three disparate bibliographic sources
– legacy system– flat BibTeX files– relational DB– wwWeb files
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Mediator Specification Interpreter Architecture
Query Rewriter
Cost-Based Optimizer
Datamerge Engine
MediatorSpecification
Query
logical datamerge program
plan
Result
Queries toWrappers Results
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Environmental Restoration at INEL Undoing 50 years of messes
…. MQL [ISX]
MSL [Stanford]OQL [ODMG]
QEM
mediator
QEMQEM
QEM
QEM
QEM
CORBA
othermediators
OEMOEM
OEM
OEM
OEMOEM
OEM
QEM
QEM
Idaho NationalEngineering Laboratory04/19/23
LOCKHEED MARTINISX - Stanford Univ.
Many projectsMany projectsmany sourcesmany sources
wrapper
wrapper
ERIS
wrapper
IEDMS
wrapper
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Mediation to Implement Feedback in TrainingDavid Maluf, Priya Panchapagesan, Ted Linden
Abstraction to match levels of granularity
Abstraction
Another task of mediators, prior to integrationAnother task of mediators, prior to integration
MIFT
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Mediation Feedback:Playback or Graph
Janus SimNet
TraineesTraineesObservers
Commanders Training Developers Analysts
Wrapped Simulation Resources
Mediation Layers
Application Layer
Mediators with rules in CLIPS
Standardsin KQML
Wrappersin C/C++
UI in Java
User Interface
I.D.A
Stanford
Objectives
Tasks
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MIFT . Result .
Analyses:Analyses:• Force ratioForce ratio• LossesLosses• Area gainArea gain
ExerciseExercise
SimulatorSimulatorTypeType
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Control Valve Sizing, Future
• Interpretation– Programmatic
• Analysis– Integrated
• Evaluation– Integrated
• Transformation– Automated
From Andrew Arnold: Civ. Eng. Qualification Exam
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F-22 IWSDB Phase 6
Integration ServicesUser Interfaces
SSQQLL
PD DS
WrappersDatabases
Domain Model
Matchmaker Domain
Matching
ChangeNotification
Query Re-formulation
Provi-sioner
Engi- neer
Appli-cationPRIDE
IWSDBclient
GUI
WAISserver
Index
Suppliers
Sy-base
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Simulation services
1. Continously executing: weather prediction– SimQL result reports best match samples
2. Execution specific to query: what-if assessment, spreadsheets– may require HPC power for adequate response
3. Complement base data: materials data, assembly – performs inter- or extra-polations to match query parameters
4. Combinations of 2. and 3.: top layer simulation using stored partial lower level results: weapon performance in setting
5. Human-in-the-loop (mediated by an agent program): SAFs
Note• A simulation service program can be written in any language• A simulation service must be compliant to the interface
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SimQL: Simulation Access Service
Decision-making requires dealing with the future, as well the past
• Databases deal well with the past
• Sensors can provide current status
• Spreadsheets, simulations deal with the likely futures
Information systems should be able to combine all three
timetimepast SQL now SimQL futurepast SQL now SimQL future
Information Systems should also deal with the Future