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Mining Complex Evolutionary Phenomena. D. Thompson, B. Gatlin Center for Computational Sytems Mississippi State University. M. Jiang, M. Coatney, S. Mehta, S. Parthasarthy, R. Machiraju Computer and Information Science The Ohio State University. T-S. Choy, S. Barr, J. Wilkins

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mining complex evolutionary phenomena

Mining Complex Evolutionary Phenomena

D. Thompson, B. Gatlin

Center for Computational SytemsMississippi State University

M. Jiang, M. Coatney, S. Mehta, S. Parthasarthy, R. Machiraju

Computer and Information Science

The Ohio State University

T-S. Choy, S. Barr, J. Wilkins

Department of Physics

Ohio State University

insights into evolutions
Insights Into Evolutions
  • Study evolution through simulations
  • Model them using continuum models
  • Obtain discrete models and solve
  • Generate data
  • However, …
slide4

Data Horror Stories …

4.5 million points

1500 time steps with full volume output every 4 time steps (375 solutions)

750 MB per solution

281.25GB of data

O(108) grid points

Generates >10 Terabytes per day (every day)

Write to disk every 1/1000 time steps (99.9% discarded)

Final database ~1 Terabyte

All analysis is done after final database is obtained

slide6

Solutions !

  • Get the rings of the smoke
  • Track them in time
  • Mine their properties
  • Use some science drivers
cfd of interest bronchial flow
CFD Of Interest – Bronchial Flow
  • Complex Non-rigid, Fractal-like Geometry
  • Deep recursive branching structure
  • Need insights into how flow changes
  • Study Vortices, swirling flow
  • Q: Persistence of vortex ?
  • Implications
    • Pulmonary drug delivery
    • Carcinogen Deposition
object of study vortices
Object of Study: Vortices
  • Swirling regions
  • Core (Center of vortex) and swirling streamlines …
slide14

MD Of Interest – Defect Evolution

  • Active Device sizes (Si-based transistors) passive components (alloys) are shrinking
  • At sub-micron levels extended defects effect performance
  • Extended defects
    • Si is doped with Boron in a “Hot Bath”
    • Non-uniform solidification
    • Arise from point defects
  • Study evolution of point defects and formation of extended defects
  • Q: What structures finally remain ?
object of study defects
Object of Study: Defects

Defect Atoms - Red !

  • Point defects – interstitial and vacancy
  • Interstitial – Si atoms located at non-bulk position
problem statement
Problem Statement
  • Need – Locating, Characterizing & Tracking Structures in Large Domains.
  • Acts of Discovery and Perseverance!
  • Approach desired
    • Tied to simulations
    • Multiple time scales
    • Organized Search
    • Encode Structure, dynamics and relationships
    • Incorporate complex physics in discovery
    • Classification and categorization (similarity)
    • Verification of discovered entities for veracity
    • Generalize to other domains
framework
Framework

ApplicationCFD, MD, …

Sensor

Multires Transforms

Meta-stability Detection

Transient Detection

Feature Mining

Event Detection

Feature Tracking

Catalog

Spatio-temporal Rule Mining

components
Components
  • Sensors –
    • Monitoring a stream
    • Swirl (CFD), Energy (MD)
  • Multiresolution Analysis
    • Temporal wavelet transform
    • Casual transforms
    • Eulerian Framework
    • Can be used with a spatial sub-division
  • Event Detection
    • Changes in Feature Demographics
    • Birth, death, continuation
    • Aggregation, bifurcation
    • Has impact on tracking
tracking correspondence
Tracking - Correspondence

Lagrangian Framework

feature mining mechanics
Feature Mining Mechanics
  • Do not just use raw data
  • Features – A feature is a manifestation of the correlations between various parameters
  • Feature Mining –
    • Extract meta-stable features using underlying physics
    • Describe features as tangible shapes
shapes
Shapes

Point cloud

Proximity graphs

Conical frusta

slide23

Similar Efforts - CFD

Marusic, Kumar, Karypis, Interrante, U of Minn.

Frequent subgraphs

similar efforts md
Similar Efforts - MD
  • Defect is infrequent, atomsets of bulk are not !
  • Run common substructure discovery algorithm
  • Get bulk !
  • Remove atoms contained in common substructure atomsets
  • Remainder of structure is defect!

Alloys (Ni3Al)

I1 Defect !

slide26

Feature Mining 1

Data

Transform

Tour Grid

Operator

Aggregate

Classify Points

Denoise

Track

Rank

Catalog

ROIs

Classify-Aggregate

applying to defect detection
Applying To Defect Detection

Visit all atom sites

Atom-site: Is it part of defect ?

Spatially aggregate atomsin located areas !

Works for quenched defects (local equilibria)

feature mining for defects
Feature Mining for Defects
  • Build spatially local classifiers
  • Define Bulk
    • Form Rules to define Bulk --- C1, C2,…,Cn
    • Typical Rules:
      • C1 = prescribed bond length
      • C2 = prescribed bond angle
  • Defect is not bulk
feature mining for defects1
Feature Mining for Defects
  • Core Defect Atoms will satisfy

C = ~C1 AND ~C2AND ~C3 … AND ~Cn

  • Find neighborhood by locating atoms which satisfy

D = ~C1 OR ~ C2OR ~C3 …. OR ~Cn

  • Defect = Embed C graph in D graph
  • D is needed to deal with noise and uncertainty of conditions Ci
  • Cluster all atoms in D
results i3 defects
Results – I3 Defects

I3A Defect

I3B Defect

slide31

Related Work - SAL

Aggregate

Classify

Original

Redescribe

Yip&Zhao 96

slide32

Does It Work Always ?

  • Compute Swirl
  • Local Classification Method
  • Swirling regions contain vortices
  • False Positives !
  • Cannot extract structures!

Classify-Aggregate

slide33

Solution - Feature Mining 2

Data

Transform

Tour Grid

Operator

Verify

Aggregate

Denoise

Track

Rank

Catalog

ROIs

Aggregate-Classify (Verify)

slide34

Classify-Aggregate

Yellow: Good Green:Bad

Yellow ones really swirl !

classifier
Classifier
  • Simple and efficient !
  • Can be error prone 
  • Since One verifies
  • Point-based approach:
    • Label neighbors
  • Combinatorial:
    • Locally check for complete triangles
defects at finite temp
Defects at Finite Temp.

Visit all atom sites

Atom-site Is part of defect ?

Spatially aggregate atomsin located areas !

Quench defect to verify

current work
Current Work
  • Streaming
  • Tracking and Correspondence
  • Shape Descriptors
  • Data Structures for Data Management
  • Spatio-temporal associations
summary
Summary
  • Computational Sciences need computational instruments
  • Need to be scalable and use all lessons learned from parallel, distributed, streaming and out-of-core implemenations
  • Need to exploit underlying source of data
  • Should provide good hooks to data-mining and intelligent systems
  • Need very Interdisciplinary work !