prepared for CERN seminar, June 2000
Heterogeneous
Information Management
June 2000
Gio Wiederhold
Stanford University
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1
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
applications
A2
A1
A4
A3
A5
A6
integrators
a.
I2
I1
mediators
network
b.
M1
c.
d.
wrappers
D1
W2
W1
D2
D4
W3
D5
M2
e.
D6
D3
datasources
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Transforming Data to Information
Application
Layer
Mediation
Layer
Foundation
Layer
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users at workstations
value-added services
data and simulation resources
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Data and Knowledge
Data Loop
Knowledge Loop
Storage
Education
Selection
Abstraction
Integration
Recording
Summarization
Experience
Decision-making
State changes
Action
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Information is
created at the
confluence of
data -- the state
&
knowledge -the ability to
select and
project the
state into
the future
6
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
Data
overload
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.
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[Herbert Simon]9
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
User interface
Human-computer
Interaction
Applicationspecific code
Service
interface
MEDIATION
Resource access
interface
Domainspecific
code
Sourcespecific
code
Real-world
interface
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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
articulation
Summarize
Transform
Heterogenous
Selection
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resources
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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
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Future
• Generated from
models
• Domain Expert
maintains models
• Specification
determines functions
• Resource changes
trigger regeneration
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Coverage of Current DARPA I3 Efforts
)
|
(
]
Good progress / active research / related work / poor coverage
(web,schema
searching)
for relevance
to customer
Maintenance
Caching /
for multiple domains
History
:-(
:-|
Security
Mediators
:-(
:-[
Facilitation
Integration
over sources
:-)
:-[
:-(
for cooperation
Wrapping (syntactical heterogeneity)
:-[
:-)
:-)
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:-(
Databases / Web / Text / Simulation
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:-|
(rule technology?)
(auto linking)
:-(
Abstraction
:-[
:-)
Discovery
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 4 4
• Representation and Access Conventions 4
• 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 Software
Databases 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
Domain Ontology
• context is implicit in use
• explicit context is needed
for external use
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No committee is needed
to forge compromises *
within a domain
 Compromises hide valuable details
24
SKC Objective
Provide for Maintainable Ontologies
• devolve maintenance onto many
domain-specific experts / authorities
SKC
• provide an algebra to compute
composed ontologies that are
limited to their articulation terms
• enable interpretation within the source
contexts
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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
Intersection
Union
Difference
create a subset ontology
keep sharable entries
create a joint ontology
merge entries
create a distinct ontology
remove shared entries
The Articulation Ontology (AO) consists of
rules that link domain ontologies
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matching
27
Sample Operation: INTERSECTION
Terms useful
for purchasing
Result contains
shared terms
Source Domain 1:
Owned and maintained
by Store
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Source Domain 2:
Owned and maintained
by Factory
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INTERSECTION support
Articulation ontology
Terms useful
for purchasing
Matching
rules that use
terms from the
2 source domains
Store
Ontology
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Factory
Ontology
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Sample Intersections
size = size
Articulation
color =table(colcode)
ontology
style = style
matching rules :
Anatomy
{. . . }
Shoe Factory
• Material inventory {...}
• Employees { . . . }
• Machinery { . . . }
• Processes { . . . }
• Shoes { . . . }
Shoe Store
• Shoes { . . . }
• Customers { . . . }
• Employees { . . . }
foot = foot
Employees
Nail (toe, foot)
...
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Department
Store
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Hardware
Employees
Nail (fastener)
...
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Other Basic Operations
UNION: merging
entire ontologies
DIFFERENCE: material
fully under local control
Articulation
ontology
typically prior
intersections
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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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Knowledge Composition
Legend:
Articulation
knowledge
U : union
for
U
U
(A B) U
(B C) U
(C E)
Articulation knowledge
for (C E)
U
U
Knowledge
resource
E
Articulation
knowledge
for (A B)
U
Knowledge
resource
A
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U
(B
C)
Knowledge
resource
C
Knowledge
resource
B
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(C
U
: intersection
Composed knowledge for
applications using A,B,C,E
D)
Knowledge
resource
D
33
U
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
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– 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 matcher
for
Articulationcreating
Expert
Transport
ontology
Vehicle
ontology
Suggestions
for 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. Okay
2. False
3. 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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Using the match repository
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Navigating the match repository
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Primitive Operations
Model
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
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and
Instance
Constructors
• create object
• create set
Connectors
• match object
• match set
Editors
• insert value
• edit value
• move value
• delete value
Converters
• object - value
• object indirection
• reference indirection
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Future: exploiting the result
Avoid n2 problem of interpreter
mapping as stated by Swartout
as an issue in HPKB year 1
Result has links
to source
Processing & query evaluation
is best performed within Source
Domains & by their engines
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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
automously
maintainable
Empowerment
* based on experience with software
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SKC Summary
.
• Algebra enables Interoperation by
dealing explicitly with differences by knowledge
identifying maintenance domains
keeping sources autonomous
• Assumes domain has a common ontology
composing domain ontologies requires the algebra to manage the
linkages where articulation occurs
processes are best executed within the domains
• Knowledge about articulation is disjoint
allows integration specialists to work independently
supports 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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Long Range Science Vision
Databases
access
storage
algebras
Systems
Engineering
analysis
documentation
costing
Artificial
Intelligence
knowledge mgmt
domain expertise
uncertainty
Integration Methods
GIS
Spatial is special.
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Integration
Science
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Background Material:
• Technology Sources
• Maintenance
• Projects
• Information about the Future
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Interfaces
Human  Computer
{x-widgets, HTML}
Application  Mediator
{OQL, KQML, ...}
Mediator  Data sources
{SQL, TQL, XML, … }
Data real world
{sensors, clerks, … }
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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
Web Search Tools
Multimedia Interfaces
Agents
Database Models Security Filters Domain Ontologies
Object Bases
Temporal Algebras
Uncertainty algebras
Customer Models Constraint Management Circumscription
Communication Standards
Active Databases
DB Views
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Case-based Reasoning
Internet Billing Knobots Simulation Access
Wrappers
Public Databases
GIS
GIS
Caching
Text & Speech Processing
Distributed Storage Systems
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High Perf.Comm.
52
Fat versus thin mediators
• too thin: insufficient added value
• Too fat: hard to
compose
service
scope
• Too narrow: few costumers
• too broad:
hard to maintain,
needs a committee
domain scope
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Just right
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Maintenance is good for you
?
13
12
11
100%
10
9
90
8
80
7
70
6
60
lifetime
5
50
4
40
3
30
2
1
20
10
relative annual
maintenance cost
depreciation = 1 / lifetime
years
0
automobile
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hardware
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software
54
Client-Server Architecture
Client system s
X
Fast build of clients
by resource reuse
data and simulation resources
Changes (x) are difficult,
can affect many clients
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Systems with
Mediators
Gio Wiederhold. 1995
Applications . . . .
Mediators . . . . . .
Data Resources . . .
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Growth through
Reuse
Gio Wiederhold. 1995
New Application
Prior & Revised
Mediators
Extended Data
Resources
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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
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7 2
58
A mediator is not just static
software: Knowledge ages
Application
Interface
Changes of
user needs
Software & People
Models, programs,
rules, caches, . . .
Owner / Creator
Maintainer
Lessor - Seller
Advertisor
Resource
changes
Resource Interfaces
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Domain
changes
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Roles
Computer Scientists
• Provide tools
–
–
–
–
adapatation
integration
matching
composing
• Assess Standards
• Assure scalability
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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 –
Sources
wrapper, supplier or vendor for supplier
c. Source selection –
expert specialist in mediator
d. Source quality assessment –
customer input to mediator
Services
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 –
Customers
client services with customer input
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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.
Mediator & Wrapper
Generation.
Warehousing.
Security Mediators.
Megaprogramming.
Simulation Access.
Changes, Consistency,
and Configurations.
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MIDAS
WHIPS
TSIMMIS
TIHI
SimQL
CHAIMS
C3
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The TSIMMIS Project
Ramana 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,
heterogeneous sources
a uniform interface to
– mediator toolkit to rapidly construct mediators, based on a
mediator specification,
a set of wrappers
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to integrate data from
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Investors Need to Fuse Information
from Multiple
Sources
.
Network
Ticker Tape
WWW
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Personal
database
• group together information about
the same real-world entity
• remove redundancies
• resolve conflicts
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An Integration Architecture
Client
Application
portfolios for each company
Mediator
stock market prices
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business reports
Wrapper
Wrapper
Ticker
Tape
Dialog
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Additional Challenge: Sources Without a
Well-Structured Schema
Examples
• semistructured
– irregular
– deeply nested
• incomplete
schema knowledge
– autonomous
– dynamic
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• World Wide Web
• SGML documents
• genome, chemical
structures
• bibliographic
information
• files
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Wrappers & Mediators from
High-Level Specifications
Client
DeclarativeMediator
Specification
Mediator
Mediator Specification
Interpreter
Wrapper
Source
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Wrapper
Source
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Wrapper Specification
Interpreter
Declarative
Source
Specifications
68
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)
yes
Enabled through secure signatures
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E-Commerce in the Digital Library
Steven Ketchpel & DL Economics Group
Payment
Delivery
CyberCash
DigiCash
First Virtual
SET
Cryptolope
DigiBox
HTTP
E-mail
Major
Integration
Problem
Shopping Models: Pay-per-view, Subscription,
Session, Shareware, Auctions, Site License,
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
State
Information
Event Handlers
Abstract API
allows application to
interact with many
different services
in a consistent way
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2
1
Order
Complete
3
Start Transfer $
4
Payment
Complete
Event Handlers
Event Handlers
Customer
Bill
Merchant
Event Handlers
Payment/Delivery/
Other Services
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Proxy event handlers
translate from
native applications
to shopping model
defined protocols
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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
–
–
–
–
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legacy system
flat BibTeX files
relational DB
wwWeb files
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Mediator Specification Interpreter
Architecture
Query
Result
Query Rewriter
logical datamerge
program
Mediator
Specification
Cost-Based Optimizer
plan
Datamerge Engine
Queries to
Wrappers
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Results
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Environmental Restoration at
INEL Undoing 50 years of messes
….
MSL [Stanford]
OQL [ODMG]
MQL [ISX]
OEM
QEM
OEM
QEM
other
mediators
wrapper
OEM
QEM
QEM
OEM
mediator
QEM
OEM
OEM
QEM
CORBA
OEM
QEM
wrapper
QEM
wrapper
wrapper
Many projects
many sources
ERIS
LOCKHEED MARTIN
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IEDMS
ISX - Stanford Univ.
Idaho National
Engineering Laboratory
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Mediation to Implement Feedback in Training
David Maluf, Priya Panchapagesan, Ted Linden
Another task of mediators, prior to integration
MIFT
Abstraction
Abstraction to match levels of granularity
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Mediation Feedback:
Playback or Graph
User
Interface
Commanders
Trainees
Observers
Training
Developers Analysts
UI in
Java
Application
Layer
Standards
in KQML
Objectives
Mediation
Layers
Tasks
Stanford
Mediators with
rules in CLIPS
I.D.A
Wrapped
Simulation
Resources
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Wrappers
in C/C++
Janus
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SimNet
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MIFT
.
Result
.
Analyses:
• Force ratio
• Losses
• Area gain
Exercise
Simulator
Type
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Control Valve Sizing, Future
From Andrew Arnold: Civ. Eng. Qualification Exam
• Interpretation
– Programmatic
• Analysis
– Integrated
• Evaluation
– Integrated
• Transformation
– Automated
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F-22 IWSDB Phase 6
User Interfaces
Application
PRIDE
Provisioner
Engineer
IWSDB
client
GUI
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Integration Services
Change
Notification
Query Reformulation
Match
maker
Wrappers Databases
Sybase
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Index
WAIS
server
Domain
Model
Domain
Matching
PD
DS
Suppliers
S
Q
L
79
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
Information Systems should also deal
with the Future
past
SQL
now
SimQL
future
time
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
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