KNIME/Orange Data Preprocessing Audit Workpaper

Audit Focus Area

Evaluating risks in visual workflow tools for data preprocessing:

GUI Opacity: Hidden transformations in visual interfaces
Non-Human-Readable Exports: XML-based workflow definitions
Reproducibility Gaps: Drag-and-drop operations without version control

Evidence Collection Methodology

1. GUI Pipeline Opacity

Risk Indicator	Investigation Method	Documentation Reference
Buried node configurations	Right-click → "Configure" on each node	KNIME Node Description vs Actual Settings
Hidden data flows	Check "Workflow Cohesion" metrics	Orange Canvas Connection Graph
Default parameter risks	Compare node settings to company SOPs	KNIME Analytics Platform Cookbook

Test Procedure:

Generate workflow visualization (File → Export → Workflow Image)
Cross-reference with node configuration dialogs
Verify tooltips match actual operations

2. Non-Human-Readable Exports

File Type	Readability Issue	Mitigation Check
.knwf (KNIME)	Binary blobs in XML	Search for config-key mappings
.ows (Orange)	Minified JSON	Validate with python -m json.tool
Workflow backups	ZIP with internal hashes	Checksum verification reports

import xml.etree.ElementTree as ET
tree = ET.parse('workflow.knwf')
for config in tree.findall('.//config'):
    if config.get('isHidden', 'false') == 'true':
        print(f"Hidden config: {config.get('key')}")

3. Reproducibility Gaps

Reproducibility Risk	Detection Method	Test Case
Manual filtering steps	Check for "Interactive" nodes	Re-execute with different screen sizes
Unversioned workflows	.knwf timestamp analysis	Git history of workflow files
Environment dependencies	"Python Script" node contents	requirements.txt cross-check

Workpaper Template

GUI Opacity Findings

Node Type	Hidden Parameters	Impact	Severity
"Rule Engine"	5 unlogged rules	12% data loss	High
"Column Filter"	Manual selection	Bias introduced	Critical
"Missing Value"	Default imputation	Wrong median	Medium

Export Readability Findings

Workflow	"Human-Readable" Score	Key Opaque Elements
Customer_EDA	2/5	8 binary-encoded configs
Risk_Modeling	3/5	Minified JSON conditions

Reproducibility Findings

Workflow	Interactive Nodes	Environment Drift	Re-Run Variance
Sales_Forecast	3 sliders	Python 3.7 → 3.9	14% output delta
Churn_Analysis	None	Missing R plugin	Failed execution

Key Risks

Critical: 22% of filtering decisions made via unreviewed interactive sliders
High: Core imputation logic buried in 3-layer nested node configurations
Medium: Workflow exports contain 18 binary-encoded parameter blobs

Recommendations

For GUI Transparency

from knime.workflow import WorkflowReader
wf = WorkflowReader.load('workflow.knwf')
wf.generate_markdown_docs(output_file='preprocessing_spec.md')

For Export Readability

orange-canvas --workflow-dump workflow.ows > workflow_audit.json
jq '.' workflow_audit.json > formatted_workflow.json

For Reproducibility

# KNIME snapshot
knime -application org.eclipse.equinox.p2.director \
  -listInstalledRoots > knime_versions.txt

# Orange requirements
pip freeze > orange_requirements.txt

Auditor Notes

Required Attachments:
- Workflow annotation screenshots
- XML/JSON export analysis reports
- Environment specification files
Sign-off:
- Auditor: [Your Name]
- Workflow Owner: [Owner Name]
- QA Engineer: [QA Name]
- Date: [Date]

Standards References

FDA 21 CFR Part 11 (Electronic Records)
KNIME Best Practices Guide v4.7
Orange Data Mining Documentation (Reproducibility Chapter)