Quality Measurement System (QMS) & Strategic Foresight

Technology Watch

This technology watch is part of my systems & cloud engineering apprenticeship at CIXI.

Automated Pipeline: Multi-source RSS intake, ingestion via n8n, AI pre-filtering and summarization (Mistral AI), curation into a Notion Kanban, and automated audit metric exports to GitHub as CSV.

The framework implements a Quality Measurement System (QMS) compliant with SMART criteria, continuously evaluating source relevance, temporal horizons (H1, H2, H3), linguistic authenticity, and weak signal detection.

QMS Quality Score

88/ 100

✓ Meets SLA (Target > 80)

Composite relevance, freshness & rigor

Weak Signals Detected

35signals tagged

29 disruptive articles

Isolated leading indicators

Noise Filtration Rate

70.0%306/437

⚡ Mistral AI + n8n Pruning

Raw articles pruned by AI

Average Efficiency

72min / session

✓ Total Searches & KPIs

Per curation session

Methodological Rigor, Freshness & Consistency (QMS)

Information compliance metrics, weekly cadence adherence, and source cross-checking.

Weekly Cadence & Consistency

100%compliant

Average interval: 6.3d (3/3 cycles fulfilled)

Detection Freshness (Lead Time)

96%< 7 jours

< 7d (weekly cycle): 96%7-14d (cycle N-1): 4%

Original Language (Linguistic Integrity)

100.0%original language

Original English articles without translation distortion

Cross-Check Verification

100%H2 & H3

H2 & H3 articles backed by official technical sources (Git repos, CVE advisories)

Distribution by Time Horizon & Cadence

H1 - Flaws, Releases & SysOps (Weekly)19 articles (66%)

H2 - AI, Supply Chain & Workflows (Monthly)9 articles (31%)

H3 - Disruption, Quantum & Energy (Quarterly)1 article (3%)

💡 Methodological balance: The watch successfully covers immediate updates (H1), enterprise distro lifecycles (H2), and disruptive security ruptures (H3).

Distribution by Technical Domain

3 categories

AI & Supply Chain8 (28%)

Sysops & Infra20 (69%)

Conferences, IT & AI1 (3%)

Temporal Trends & Continuous Improvement (PDCA)

Dynamic metric trajectories across sessions (S01 to S12)

Achieved QMS score

Target SLA threshold (80/100)

Y Axis: QMS Quality Score (out of 100)•X Axis: Chronological weekly sessions (S01 to S12)

💡 Hover over any point to inspect session details

Watch Sessions History (Audit Log)

Full session records generated automatically by the n8n pipeline

Download Raw Dataset (CSV)

Session	Date	Collected	Retained	Weak S.	Native Lang.	Duration	QMS Score
S01	2026-09-29	116	8	15	100%	80m	86/100
S02	2026-10-04	112	9	10	100%	80m	93/100
S03	2026-10-12	144	7	9	100%	80m	82/100
S04	2026-10-18	65	5	7	100%	46m	91.9/100

RSS Feed Quality Matrix & Source Audit

Longitudinal source yield tracking, 3-session rolling SLA evaluation, and pruning decision log.

Download Sources History (CSV)

Source	Category	Volume (Kept / In)	All-Time Yield	Rolling 3-Session Yield	Trend	SLA Status & Action
Youtube	Everything	4 / 4	100%	100%	Stable	Core Anchor (Tier 1/2)
wccftech		1 / 1	100%	100%	Stable	Passive / Low-Frequency
Phoronix	Linux & Hardware	18 / 217	8%	9%	Improving	Under Review (<30%)
Bleeping Computer	AI & Cybersecurity & Linux	4 / 84	5%	5%	Stable	Pruned / Replaced