SimQL
Accessing Simulation as Services
to Information Systems
Gio Wiederhold
July 2001
Gio Wiederhold SimQL 1
Decision-making (DM)
Analyze Alternatives
• Current Capabilities
• Future Expectations
Process tasks:
• List resources
• Enumerate alternatives
• Prune alternative
• Compare alternatives
now
future
Gio Wiederhold SimQL 2
Prediction Requires Tools
E-mail this book,
Alfred Knopf, 1997
Gio Wiederhold SimQL 3
DM support is disjoint
does not interoperate
Planning Science
Distribution
extensions to move
to networked support
are also disjoint
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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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Current state of DM Support
past
now
organized support
Data integration
Databases
distributed, heterogeneous
future
time
disjointed support
x17 @qbfera
ffga 67 .78 jjkl,a
nsnd nn 23.5a
Intuition +
• Spreadsheets
• Planning of allocations
• Other simulations
various point assessments
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Information Systems should also
Project into the Future
past
now
future
time
Support of 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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Information Systems should also
Project into the Future
One Consistent World
past

A Bush of Possible Worlds
now
future
time
Databases,
accessed via SQL or
CORBA compliant
wrappers
Msg
systems,
sensors
Simulations,
accessed via SimQL and
compliant wrappers
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The branches can be labeled with probabilities,
then assessed using the outcome with values
Next period alternatives
1200
and subsequent periods
0.6
past
-1220
600
0.1
0.1 0.3 1100 0.2500
66
134
0.4
0.4
0.5
1266
-1086
1000
0.1 100 0.3
200
0.2
0.3
0.1
-420
0.07
0.2
now
time
2000
5000
1000
200
0
0
-6000
-820 0.13-400 -3000
future
Values
Spreadsheets,
other simulations,
Databases, . . .
Msgs
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Integrating data & planning support will make
our data reusable and much more valuable
A Pruned Bush
Re-assess as time
marches forward !
100
1200
1266 ?
66
?
?
600
1100
200
500
200
0
past
now
future
1000
2000
5000
1000
0
time
Spreadsheets,
other simulations,
Databases, . . .
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Msgs
Stanford experiment, supported by DARPA & NIST
Phase 1 Architectures
Logistics
Application
Manufacturing
Application
SimQL access
SimQL access
SimQL access
SQL access
wrapper
Spreadsheets
wrapper
wrapper
wrapper
Weather
(short-, long-term)
Test
Data
Engineering
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Current State of SimQL research
GUI
collect language
requirements
Test Application
wrapper
wrapper
Spreadsheets
Weather
wrapper
Engineering
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Moving to a Service Paradigm
• Server is an independent contractor, defines service
• Client selects service, and specifies parameters
• Server’s success depends on value provided
•
Some form of payment received for services
x,y
Databases are a current example.
Simulations have the same potential.
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Enabling Interoperation
Databases
• serve clients via SQL by
Sharing a Model (The Schema)
A query language over the model
the SQL interface enables
• independence of
application development
DBMS technology development
reuse of infrastructure
Today
• most new systems use a
DBMS for data storage
even with less performance,
inability to handle all problems,
but enough of them well enough.
Simulations should
• serve clients via SimQL by
Sharing a Model (research q.)
A query language over the model
a SimQL interface will enable
• independence of
application development
simulation technology develop’t
reuse of infrastructure
Objective
• build information systems
combining DBMS, Simulations
even with less performance,
inability to handle all problems,
but enough of them . . .
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Internet requirements
• Ubiquitous acess to simulations
of a wide variety of types
• Rapid response to parameter changes
– often High-Performance computation is
needed
– distributed simulations with synchronization
• Rapid Service Composition
– High bandwidth among simulations
– Acces to multiple services in parallel
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Even the present needs SimQL
last recorded observations
point-in-time for
situational
assessment
simple simulations
to extrapolate data
past
now
time
future
Is the delivery truck in X?
• Is the right stuff on the truck?
Not all data are current::
• Will the crew be at X?
• Will the forces be ready to accept delivery?
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Use of Simulation Results
Simulation results can be composed for
Alternative Courses-of-actions
Composition should be seamless, elegant,
with computation and recomputation of
likelihoods
Results change as now moves forwards and
eliminates earlier alternatives.
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Types of simulation services
1. Continously executing: weather prediction
– SimQL result reports best match samples
2. Execution specific to query: what-if assessment
– may require HPC power for adequate response
3. Past simulations collect results in a base: materials
– performs inter- or extra-polations to match query parameters
4. Combinations, i.e., 2. + 3.: top layer simulation using stored
partial lower level results: weapon performance in new 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 spec.
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SimQL research questions
• How little of the model needs to be exposed?
• How can defaults be set rationally?
• How should expected execution cost be reported?
• How should uncertainty be reported?
• Are there differences among application areas that
require different language structures?
• Are there differences among application areas that
require different language features?
• How will the language interface support effective
partitioning and distribution?
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New DM research questions
• How to move seamlessly from the past to the future?
• How can multiple futures be managed (indexed)?
• How can multiple futures be compared, selected?
• How should joint uncertainty be computed?
• How can the NOW point be moved automatically?
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Summary of SimQL
A new service for Decision Making:
• follows database paradigm
– (by about 25 years)
• coherence in prediction
– displacement of ad-hoc practices
• seamless integration over time
– single paradigm for decision makers
• systematic partitioning
– better software management
• simulation industry infrastructure
– investment has a potential market
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