### Interactive Model Prototyping in Visualization Space

Identifying and building models, and quantifying model parameters

-- **Ove Daae Lampe** and Helwig Hauser

### Who am I?

Ove Daae Lampe

+Ove Lampe
| ii.uib.no/vis
| cmr.no/cmr_computing

Researcher at CMR Computing and PhD Student at UiB

Slides are available here: http://folk.uib.no/ola062/sigrad2011

# Interactive Model Prototyping in Visualization Space

### Model Building

is

- Understanding underlying behaviour of the data
- Abstracting data
- Quantifying controlling parameters

### Model Constructs

- Point Construct
- Linear Construct
- Exponential Construct

### Visualization Space

Model prototyping in Visualization Space analyzes **visual representations** of the data rather than the data.

In effect we rasterize the data once, and optimize the difference between this and a rasterized version of a model

### But **WHY** Visualization Space

- The data is rasterized
**once**
- Optimize and fit multiple models efficiently
- Local models can be sketched
- Directly observe convergence / divergence and the Residual

### User sketching and Automated Fitting

### Interactive Convergence

*Constant-step* iterations while interacting. Newton's optimization applied afterwards.

Optimizing:

- Position / mean /
**μ**
- Radius / std.dev /
**σ**
- Scale / integral

### Workflow I

### Workflow II

- Visualize and Observe
- Sketch and Fit
- Externalize and Subtract
- Iterate

### Test Dataset

- A(x) = N(0,1)
- C(x) = 0.05N(1,0.2)
- B(x) = A(x)+C(x)

### Step 1. Visualize and Observe

### Step 2. Sketch and Fit

### Step 3. Externalize ...

**Modeled:**

- μ = (0.02,0.01)
- σ = (1.035,1.04)

**Reference:**

- μ = (0.0,0.0)
- σ = (1.0,1.0)

### Step 3. ... and Subtract

### Step 4. Iterate

**Modeled:**

- μ = (1.004,1.004)
- σ = (0.1989,0.2015)

**Reference:**

- μ = (1.0,1.0)
- σ = (0.2,0.2)

# Domain

## Example

### Drilling Data I

### Drilling Data II

Torque measured in kN.m over time in seconds.

### Step 1: Visualize and Observe

### Step 2+3: Sketch, Fit, ... and Subtract

### Step 4: Iterate

### Externalized Result

Result: a quantitative plot of mean torque over depth and error (one standard deviation ≅ 68% of the measurements).

### Summary

- A workflow for sketching models in visualization space
- Combining sketching and automated fitting
- Externalization through quantification
- Efficient algorithm

# Thank You

## Any Questions?

http://folk.uib.no/ola062/sigrad2011