Finite Element; eScience; Data Analysis; Semantic Web; Data Analysis

1. Insight Centre for Data Analytics

2. How Semantic Technologies can help to cure Hearing Loss
An Introduction to the SIFEM EU Project
André Freitas, Ratnesh Sahay
October 3rd, 2014

3. SIFEM Team
Kartik Asooja
João Jares
Marggie Jones
Oya Beyan
Yasar Khan
Stefan Decker
Ratnesh Sahay
André Freitas
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4. Outline
•Motivation: Modelling the Mechanics of Hearing
•Challenges in Contemporary Science
•Semantic Infrastructure
•Demonstration
•Take-away message
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5. Goals
•Discuss the challenges that contemporary scientific practice faces
•Discuss how Semantic Technologies can help.
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Outside the computer science community

6. Multi-scale Models
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7. Finite Element Models (Video)
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8. Geometrical Model
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9. Physics Model
•FE equilibrium for solid
•FE equilibrium for fluid
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10. Numerical Models/Solvers
•Incremental-iterative implicit solution scheme
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11. Experimental Data
•A
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12. Common Challenges for ‘Big Science’
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13. Coordination Complexity (L. Floridi)
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14. Reproducibility
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15. Efficiency & Automation
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16. How to build an infrastructure which addresses these dimensions?
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17. Characteristics of the SIFEM Domain
•Most data is at the numeric level
•Highly dependent on visualization (man in the middle)
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18. Characteristics of the SIFEM Domain
•Relatively small set of concepts
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19. Characteristics of the SIFEM Domain
•But difficult to represent
•Physics, geometrical models, topological relations, algoithmic, mathematics
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20. Semantic Infrastructure
•Coordination Complexity
•Semantic Web standards and standardized vocabularies for representing FE resources
•Simulation Platform with built-in standardized data representation
•One-stop shop for FE simulation resources (inner ear)
•Reproducibility
•Web platform for sharing FE Simulations
•Simulation output as Linked Data
•Executable papers
•Efficiency & Automation
•Facilitating data interpretation
•Attribution & Incentives
•ORCID & Altmetrics
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21. Lid-driven cavity flow
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Physical Model
Solver
FEM Model
If there a vortex close to the lid?

22. 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

23. Physical Object
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Left wall
Bottom
Lid
Right wall

24. FE Core Conceptual Model
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25. FE Elements
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Cell
Patch boundary
Patch boundary
Mesh
Block

26. FE Elements
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27. Physics/Material Properties
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Kinematic viscosity
Dynamic viscosity (μ)/Fluid density(ρ)
Fluid
Solid
Velocity
Velocity
Pressure
Navier-Stokes Equation

28. Physics/Material Properties
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29. Insight Centre for Data Analytics
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are Connected
are Connected
are Connected
are Connected
Box
Wall
Is Part of
Fluid
is Inside
Topology

30. Topology
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31. Simulation Results
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32. FE Core Conceptual Model
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33. The SIFEM Conceptual Model
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34. Data View
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Data Selection
y
0.05

35. 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

36. 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

37. 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

38. Data Analysis Workflow
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39. FE Data Analysis
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40. FE Data Analysis
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41. Insight Centre for Data Analytics
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42. Insight Centre for Data Analytics
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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 .

43. Architecture
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44. Infrastructure/Data Attribution
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45. Future Directions
•Finalization of the semantic infrastructure
•Explore heuristics for the automatic exploration of the parameter space
•Replicate an existing scientific discovery
•Engage users
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46. Demonstration (Video)
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47. Take-away message
•Contemporary science demands new infrastructures to scale scientific discovery in a complex knowledge environment.
•In SIFEM we aim at experimenting with new infrastructures based on Semantic Web standards to support better:
•Resource Coordination
•Reproducibility
•Efficiency & Automation
•Infrastructure/Data Attribution
•This institute can be a protagonist in this process.
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must!