Workload Characterization Techniques
This lecture covers the following topics:
- Terminology
- Components and Parameter Selection
- Workload Characterization Techniques
- Averaging
- Case Study: Program Usage in Educational Environments
- Characteristics of an Average Editing Session
- Single Parameter Histograms
- Multi-parameter Histograms
- Principal Component Analysis
- Finding Principal Factors
- Principal Component Example
- Markov Models
- Transition Probability
- Clustering
- Clustering Steps
- 1. Sampling
- 2. Parameter Selection
- 3. Transformation
- 4. Outliers
- 5. Data Scaling
- Distance Metric
- Clustering Techniques
- Minimum Spanning Tree-Clustering Method
- Minimum Spanning Tree Example
- Dendogram
- Nearest Centroid Method
- Cluster Interpretation
- Problems with Clustering
Presentation slides in Adobe Acrobat Format:
1 slide/page (641,232 bytes)
Slides+Audio (
Part 1,
Part 2
)
Right click and save to download
Slides+Audio (
Part 1,
Part 2
) podcast.
Frequently asked questions about remote audio/video viewing
Back to other lectures of the series
Complete List of Audio/Video Lectures by Raj Jain
Back to Raj Jain's Home Page