Intelligent Information Systems
5. Mediated Sytems
Gio Wiederhold
EPFL,
April-June 2000, at 14:15 - 15:15, room INJ 218
Schedule
Presentations in English -- but I'll try to manage discussions in French and/or German.
Integrated approach, drawing from concepts in databases, artificial intelligence, software
engineering, and business principles.
1. 13/4 Historical background, enabling technology:ARPA, Internet, DB, OO, AI., IR
2. 27/4 Search engines and methods (recall, precision, overload, semantic problems).
3. 4/5 Digital libraries, information resources. Value of services, copyright.
4. 11/5 E-commerce. Client-servers. Portals. Payment mechanisms, dynamic pricing.
5. 19/5 Mediated systems. Functions, interfaces, and standards. Intelligence in
processing. Role of humans and automation, maintenance.
6. 26/5 Software composition. Distribution of functions. Parallelism. [ww D.Beringer]
7. 31/5 Application to Bioinformatics.
8. 15/6 Educational challenges. Expected changes in teaching and learning.
9. 22/6 Privacy protection and security. Security mediation.
10.29/6 Summary and projection for the future.
• Feedback and comments are appreciated.
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Architectures & Communication
History
Presentation
Information
Aggregation
Access,
Select
Data
Source
Printed
reports
Application
Computatio
I-O
code
Local
Storage
Function: ‘mainframe’
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terminl
Application
Computation
SQL for
A&S
Data
Base
smart
terminal
Work
station
Information
Minicomptr
Application
Computation
Select
FTP
File
Storage
CORBA
file server
client server
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Object
Struct.
Server
Storage
User
Workst.
Information
Aggregation
SQL, ...
for A&S
Distr.
Sources
mediated
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The world wide information
network and its participants
_
….
_
External:
….
sources
and / or
sinks
_
….
_
_ ….
….
_
data,
meta-data,
knowledge
_
….
_
_
…. _
_ ….
_
….
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Internal:
transformers
and memory.
4
Mediation moves services into the network
applications
A4
A3
A2
A1
A5
A6
integrators
a.
I2
I1
mediators
network
b.
M1
c.
d.
M2
e.
wrappers
D1
W2
W1
D2
D3
D4
W3
D5
D6
datasources
History
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Understand the Architecture for
Information Technology:
Component Classification
Customers
Customers
Customers
Customers
Customers
Services
Services
Services
Sources
Sources
Sources
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Transform Data to Information
Application
Layer
Mediation Layer
decision-makers at workstations
value-added services
Foundation
Layer
data and simulation resources
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Service layer
Between the minds that plan and the hands that work, there must be a mediator.
[Fritz Lang, intertitle in 'Metropolis' UFA 1926]
Decision making (DM)

Multiple
domains !
Customer
Service
MEDIATION
Resource
access
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Shared
software,
standards ?
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Example in Health Care
.
Health Care Planner
Will the Clinic loose Money?
Investment
domain
Patient
Care domain
Age Profile
Service Operations
Patient Volume Growth
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Loan Interest
Bond Sales
State Support
Specifications for the components
Customer
models
Customers
Customers
Customers
Customers
Customers
Services
Services
Services
Sources
Sources
Sources
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Catalogs
Content
&
Methods
Metadata
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Value-added intermediate services 1
Needs
Technologies extant and new
Describe customer Build interpretable workflow model with
model
meta-specifications for selection
Discover new
resources
Monitor and index public metadata,
describe resource capabilities, contents & methods
Select relevant
resources
Match available metadata and indices of resource
contents to leaf nodes in the customer model
Easy access to
resources
Wrap resources to make them compatible,
exploit wrapper templates, skip unavailable sources
Filter out
excessive data
Filters attached to the customer model;
balance relevant volume and precision
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Modeling: sources
• Models provide abstractions
• abstractions represent a point of view
• Models of databases are schemas and E-R models
• well established
• constraints - references, uniqueness
• scopes remain implicit
• Information systems have meta-data
• XML has DTD’s
• under discussion, still limited
Focus on resources
Meta data
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Value-added intermediate services 2
Needs
Identify articulations
*
Match level of detail
*
Integrate
information
Omit redundant
data, documents
Reduce customer
overload
Inform customer
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Technologies, extant and new
Matching of related concepts,
use articulation rules to match nodes
Automatic abstraction to match sources at
articulation points within the customer model
Attach data instances to articulation points,
combine elements , link to customer model
Match data for content, omit overlap,
report inconsistencies in overlapping sources
Summarize according to customer model,
rank information at each level
Present information according to model
hierarchy, consider bandwidth
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Integrate via Mediation
• User application
– Workstations
• Mediator Services
– Expert-owned nodes
– Composable
• Data sources
– Remote primary and
byproduct services
– Simulations
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Mediation for Quality
S= source
reliability
C= confidence
T=
Estimates:
C1= 5+_1
T1=100+_160
S1
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Customer Model
f(S,C,T)
BEST=
low cost
rapid response
Assessments:
reliable delivery
S1=.8 S2=.9 S3=8
trustworthiness
C2= 8+_1
T2=70+_30
C3= 10+_1
T3=50+_80
S2
S3
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Functional Layer
User interface
Human-computer
Interaction
Applicationspecific code
Service
interface
MEDIATION
Resource access
interface
Wrappers
Domainspecific
code
Sourcespecific
code
Real-world
interface
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Productivity
• Data is NOT information
– Information supports decision-making
– Best among a limited number of choices
• Selection of best sources
– Locate all, use the best, most relevant
• Integration of diverse sources
– Time synchrony, spatial synchrony, topic matching
– Abstraction, ranking, pruning
• Intermediate processing to overcome
– Disintermediation between source (author)
and destination (customer)
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Functions in Mediation of Data
• Delivery to client
• Summarization or determine
exceptions from expected
values or trends
• Omission of replicated or
known information
• Integration of data from
diverse domains
• Resolution of
scope mismatch
transistors
semiconductors
• Abstraction to
match levels of
granularity
• Conversion to
compatible
protocols and
representations
• Assessment of
quality of diverse
sources
• Acess to sources
via wrappers
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Mediator Design Principle
Transform Data into Information
Match
Customer Model
Hierarchical
to
Resource Model
General network
and maintain models
(large fraction of cost)
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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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Allocation Flexibility
User Interfaces
Application C
Provider of
Mediator M
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Application I
Provider
of mediator N
M
Copy- if high
intensity of
interaction with
1. Application (M2)
2. Resources (N1,2)
3. Processing (M1)
Mediators are
only code
Application B
M2
HPC
N
M1
DB
P
N
2
Databases
N
1
DB
Q
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DBS R
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Central Solutions do not Scale
What works
with 7 modules
and one person
in charge
fails when we
have 100 and need
a committee
Changes in resources affect the intermediary modules
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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 else 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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Architecture instances
Applications . . . .
Mediators . . . . . .
Resources . . .
_
….
…. .
_
….
…. .
_
….
…. .
include computational resources
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Environmental Restoration at INEL
Undoing 50 years
….
Overall Architecture
MSL [Stanford]
OQL [ODMG]
MQL [ISX]
QEM
OEM
QEM
other
mediators
wrapper
OEM
OEM
QEM
QEM
OEM
mediator
QEM
OEM
OEM
QEM
CORBA
OEM
QEM
QEM
wrapper
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
Commanders
User Interface
Trainees
Observers
Training
Analysts
Developers
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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MIFT
.
Result
.
Analyses:
• Force ratio
• Losses
• Area gain
Exercise
Simulator
Type
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A mediator Is not just static software
Application
Interface
Changes of
user needs
Software & Peo ple
Owner/ Creator
Maintainer
Lessor - Seller
Advertisor
Models, programs,
rules, caches, . . .
Resource Interfaces
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Domain
changes
Resource
changes
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Alternative solutions
• A Super Database
– unwieldly
– obsolete before it is established
• Distributed, free standing databases (today)
– awkward for sharing information
(much knowledge derives from the intersections)
– hyperlinks and shared references allow navigation
• Distributed databases & a single standard allowing interoperation
– standards follow progress, cannot lead it
• Distributed databases with published formats
– requires rapid adaptation to keep up with resources
(but the number of resources per project will be limited)
with mediators to isolate projects from resources
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Mediator generation
• Today mainly handcraftrd
– tools at INEEL, using CLIPS for rules
– many partial tools in industry (TUXEDO, M.. )
• TSIMMIS
– from wrapper descriptions and OEM formats
• Blades using RDF [Melnik: DigLib meeting 2000]
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Coverage of Current I3 Efforts
(web,schema
searching)
)
for relevance
to customer
Maintenance
(rule technology?)
Integration
over sources
Wrapping (syntactical heterogeneity)
:-(
Databases / Web / Text / Simulation
:-[
:-(
:-)
for cooperation
for multiple domains
:-(
:-|
Security
Mediators
:-(
:-[
History
(auto linking)
:-|
Caching /
Facilitation
:-(
Abstraction
:-[
:-)
Discovery
|
(
]
Good progress / active research / related work / poor coverage
:-[
:-)
:-)
Some Research Issues
• Performance
– An intermediate layer induces cost (vs. benefits)
 caching, up to warehousing,
– optimizing the balance based on rate-of-change
• Semantic ambiguities
– Words mean differing things in differing contexts
– Integration over all contexts reduces precision
 Ontology algebra (topic in presentation # 8)
• Functions for decision support
– temporal algebra, planning, assessing alternatives
 Simulation as a service (topic in presentation # 10)
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DM support is disjoint
Sciences
Technologies
do not interoperate
Planning Science
Distribution
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extensions to move
to networked support
are also disjoint
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Summary
To sustain the trend
1. The value of the results has to keep increasing
precision, relevance not volume
2. Value is provided by experts,
encoded as models of
diverse resources, customers
Problems to be addressed
mismatches
quality
Clear models
temporal extensions
maintenance
}
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