Date post: | 14-Jul-2015 |
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A Semantic Web Platform for
Automating the Interpretation of
Finite Element Bio-simulations
Andre Freitas, Kartik Asooja, Joao B. Jares,
Stefan Decker, Ratnesh Sahay
SWAT4LS 2014
Insight Centre for Data Analytics
Goals
Automate the interpretation of finite element (FE) biosimulations ...
... by providing a supporting a symbolic representation of FE data.
Insight Centre for Data Analytics Slide 3
Reproducibility of FE Simulations
Insight Centre for Data Analytics Slide 4
Efficiency & Automation of FE Simulations
Insight Centre for Data Analytics Slide 5
Motivational Scenario: Cochlear
mechanics
Insight Centre for Data Analytics Slide 6
Characteristics of the FE Domain
• Relatively small set of concepts
Insight Centre for Data Analytics Slide 7
Characteristics of the FE Domain
• But difficult to represent • Physics, geometrical models, topological relations, algoithmic,
mathematics
Insight Centre for Data Analytics Slide 8
Characteristics of the FE Domain
• Most data is at the numeric level
• Highly dependent on visualization (man in the middle)
Insight Centre for Data Analytics Slide 9
Dimensions of a FE Bio-simulation
Insight Centre for Data Analytics Slide 10
Geometrical Model
Insight Centre for Data Analytics Slide 11
Physics Model
• FE equilibrium for solid
• FE equilibrium for fluid
Insight Centre for Data Analytics Slide 12
Numerical Models/Solvers
• Incremental-iterative implicit solution scheme
Insight Centre for Data Analytics Slide 13
Experimental Data
• A
Insight Centre for Data Analytics Slide 14
And others ... (which are not covered here)
• Anatomical
• Physiological
• Histological
• ...
02 May 2014 Insight Centre for Data Analytics Slide 15
Lid-driven cavity flow
Insight Centre for Data Analytics Slide 16
Physical Model
Solver
FEM Model
If there a vortex close to
the lid?
Lid-driven cavity flow
Insight Centre for Data Analytics Slide 17
Physical Model
Solver
FEM Model
If there a vortex close to
the lid?
Informal definition of a
valid simulation
Numerical Data Interpretation
02 May 2014 Insight Centre for Data Analytics Slide 18
informal description of
the simulation
Rules using references
to anatomical, physical
and data feature
elements
Is translated into
Multiple simulations
Feature extraction
Interpretation = rules
applied over data at
the symbolic level
Automatic Interpretation
Insight Centre for Data Analytics Slide 19
Expected physical behavior (Experiment intent):
Velocity in X starts at zero at the bottom of the box
followed by a slow velocity decrease reaching a
minima which is followed by a very fast velocity
increase close to the lid.
Numeric Level
Symbolic Lifting
IF
Predicates
Data View
Insight Centre for Data Analytics Slide 20
Data Selection
y
0.05
Feature Extraction (Symbolic Lifting)
Insight Centre for Data Analytics Slide 21
Minima=(0.055,-0.20)
fast increase
slow decrease
followed by
(avg first derivative > 35)
velocity starts
at 0 at the
bottom
maximum
velocity is 0.93
at the lid
Based on the TEDDY
ontology
Data Interpretation Statements
Insight Centre for Data Analytics Slide 22
:DataView1 :hasDimensionY :VelocityX .
:DataView1 :hasDimensionX :DistanceFromTheCavityBase .
:DataView1 :x0 “0.0"^^xsd:double .
:DataView1 :y0 “0.0"^^xsd:double .
:DataView1 :hasMinimumX “-0.055"^^xsd:double .
:DataView1 :hasMinimumY “-0.20"^^xsd:double .
:DataView1 :hasFeature :PositiveSecondDerivative .
:DataView1 :hasBehaviour :BehaviourRegion1 .
:DataView1 :hasBehaviour :BehaviourRegion2 .
:BehaviourRegion1 :avgFirstDerivative “-3.63"^^xsd:double .
:BehaviourRegion1 :hasFeature EndRegion .
:BehaviourRegion1 :hasFeature :Decreases .
:BehaviourRegion1 :hasFeature :DecreasesSlowly .
:BehaviourRegion2 :avgFirstDerivative “33.35"^^xsd:double .
:BehaviourRegion2 :hasFeature EndRegion .
:BehaviourRegion2 :hasFeature :Increases .
:BehaviourRegion2 :hasFeature :IncreasesFast .
:BehaviourRegion1 :isFollowedBy :BehaviourRegion1 .
: LidSimulation :hasInterpretation :ValidVelocityBehaviour .
Data Analysis Rule
Data Analysis Rules
Insight Centre for Data Analytics Slide 23
CONSTRUCT
{ :LidSimulation sif: hasInterpretation :ValidVelocityBehaviour }
WHERE {
?dataview rdf:type dao:DataView .
?dataview dao:hasFeature ?x .
...
}
IF( minima(velocity) is negative AND
decreases very slowly(velocity) AND
increases very fast (velocity) )
VALID VELOCITY BEHAVIOUR
SPARQL Rule
Data Analysis Workflow
Insight Centre for Data Analytics Slide 24
Data Analysis Workflow
Insight Centre for Data Analytics Slide 25
Data Analysis Workflow
Insight Centre for Data Analytics Slide 26
Data Analysis Workflow
Insight Centre for Data Analytics Slide 27
Data Analysis Workflow
Insight Centre for Data Analytics Slide 28
Conceptual Model Excerpt
Insight Centre for Data Analytics Slide 29
Conceptual Model Excerpt
Insight Centre for Data Analytics Slide 30
Going back to the Cochlea
simulation scenario
02 May 2014 Insight Centre for Data Analytics Slide 31
Output Data Views
Insight Centre for Data Analytics Slide 32
Feature Extraction
Insight Centre for Data Analytics Slide 33
:DataView1 :hasDimensionY :BasilarMembraneMagnitude .
:DataView1 :hasDimensionX :DistanceFromTheCochleaBasis .
:DataView1 :hasFeature :isSingleWave .
:DataView1 :hasMaximumAmplitude “0.0031 "^^xsd:double.
:DataView1 :hasMaximumY “0.0020 e^-6 "^^xsd:double .
:DataView1 :hasMaximumX “14"^^xsd:double .
:DataView1 :hasMinimumY “-0.0011 e^-6 "^^xsd:double .
:DataView1 :hasMinimumX “17"^^xsd:double .
Demonstration (Video)
Insight Centre for Data Analytics Slide 34
http://bit.ly/1rEZYh7
Take-away message
• Contemporary science demands new infrastructures to
scale scientific discovery in a complex knowledge
environment.
• Numerical data is everywhere, not only in FE simulations.
• In this work we started exploring how to represent and
extract numerical data features to a conceptual level.
• Which could match user intents specified as rules in the
data.
Insight Centre for Data Analytics Slide 35
Future Directions
• Better integration of the proposed representation and data
analysis framework to the TEDDY conceptual model.
• Use of the feature set and rules as a heuristic method to
prune the simulation configuration space.
Insight Centre for Data Analytics Slide 36