The document discusses a semantic web platform aimed at automating the interpretation of finite element (FE) bio-simulations, focusing on depicting finite element data symbolically. It highlights the challenges in representing complex FE concepts while performing efficient data analysis and feature extraction. Future directions include better integration with conceptual models and utilizing heuristic methods to enhance simulation configuration efficiency.

1. 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

2. Insight Centre for Data Analytics

3. Goals
Automate the interpretation of finite element (FE) biosimulations ...
... by providing a supporting a symbolic representation of FE data.
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4. Reproducibility of FE Simulations
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5. Efficiency & Automation of FE Simulations
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6. Motivational Scenario: Cochlear mechanics
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7. Characteristics of the FE Domain
•Relatively small set of concepts
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8. Characteristics of the FE Domain
•But difficult to represent
•Physics, geometrical models, topological relations, algoithmic, mathematics
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9. Characteristics of the FE Domain
•Most data is at the numeric level
•Highly dependent on visualization (man in the middle)
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10. Dimensions of a FE Bio-simulation
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11. Geometrical Model
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12. Physics Model
•FE equilibrium for solid
•FE equilibrium for fluid
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13. Numerical Models/Solvers
•Incremental-iterative implicit solution scheme
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14. Experimental Data
•A
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15. And others ... (which are not covered here)
•Anatomical
•Physiological
•Histological
•...
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16. Lid-driven cavity flow
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Physical Model
Solver
FEM Model
If there a vortex close to the lid?

17. Lid-driven cavity flow
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Physical Model
Solver
FEM Model
If there a vortex close to the lid?
Informal definition of a valid simulation

18. Numerical Data Interpretation
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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

19. Automatic Interpretation
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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

20. Data View
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Data Selection
y
0.05

21. Feature Extraction (Symbolic Lifting)
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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

22. Data Interpretation Statements
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: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

23. Data Analysis Rules
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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

24. Data Analysis Workflow
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25. Data Analysis Workflow
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26. Data Analysis Workflow
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27. Data Analysis Workflow
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28. Data Analysis Workflow
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29. Conceptual Model Excerpt
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30. Conceptual Model Excerpt
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31. Going back to the Cochlea simulation scenario
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32. Output Data Views
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33. Feature Extraction
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: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 .

34. Demonstration (Video)
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http://bit.ly/1rEZYh7

35. 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.
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36. 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.
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