AISE502/moodle/week_09.html

<!-- ==========================================================================
     AISE502 · Moodle course page · Week 9
     Section title (Edit section → Section name): Week 9: Classes C6–C9 – and Ten Profiles Side by Side
     Three blocks follow, matching the FHGR section template. Paste each block into the
     corresponding 'Text and media area' (editor → source code view), or all three into one.
     The slide set of lecture 9 is added by the lecturer as a file resource below block 2.
     Generated by src/build_moodle.py from src/content/week_09.json – edit the JSON, not this file.
     ========================================================================== -->

<!-- BLOCK 1 · Learning objectives -->
<h4>🎯 Learning objectives</h4>
<ul>
  <li>I can explain why C6 replaces the interactive vocabulary with makespan, batch-window adherence and cost per simulation.</li>
  <li>I can distinguish the third consistency semantics – reproducibility and refresh contracts – from ACID and eventual consistency.</li>
  <li>I know that C7 is an integration product over C1–C5 whose ADR to write is the freshness contract.</li>
  <li>I can explain why C8's hard requirement is declared correctness semantics under failure, not throughput.</li>
  <li>I can judge, per stream and per scenario, when at-least-once plus idempotence is legitimate instead of exactly-once.</li>
  <li>I can explain why C9's binding load is connection fan-out and push, with concurrent-connection count as tiebreaker.</li>
  <li>I can explain each H of C6–C9 as a veto trigger backed by a scenario with a response measure.</li>
  <li>I can read the consolidated requirements table by columns and by rows, including its seventeen footnotes.</li>
  <li>I can name the five cross-class observations, including that D11 High, not scale, forces microservices.</li>
  <li>I can explain why the freshness contract appears twice in the course project's measurement contract.</li>
</ul>

<!-- BLOCK 2 · Theory -->
<h4>🧑‍🏫 Theory</h4>
<p><strong>Lecture 9: Classes C6–C9 – and Ten Profiles Side by Side</strong> · 3 lessons lecture + 1 lesson standup/coaching<br>
This lecture completes the demand side: classes C6–C9 in the same rhythm, then the consolidated requirements table with its seventeen footnotes and five cross-class observations.</p>
<ul>
  <li>C6 replaces the interactive vocabulary: makespan, batch-window adherence and cost per simulation are the response measures; correctness means bit-level reproducibility from versioned inputs and seeds (Highs D2, D9, D10).</li>
  <li>C6 and C7 define a third consistency semantics beside ACID and eventual: reproducibility (“as of this run”) and freshness by refresh contract (“as of yesterday 24:00”), contracts with response measures.</li>
  <li>C7 is an integration product over C1–C5 (Highs D1, D10); the ADR to write is the freshness contract; tightening it towards real time is a class change into C8 economics.</li>
  <li>The course project inherits C6 (ingestion and eval pipelines) and C7 (analytics); its freshness contract appears twice: as a pipeline fitness function and as a grounding rule for generated answers.</li>
  <li>C8 must keep pace with the world indefinitely (Highs D2, D3, D5); the hard requirement is declared correctness semantics under failure; at-least-once plus idempotence is decided per stream, per scenario.</li>
  <li>AI lens: C7 docks text-to-SQL onto the governed semantic layer, not onto raw tables; C8 rules out per-event LLM calls; models score events in the stream as checkpointed operators.</li>
  <li>C9's binding load is connection fan-out and push (Highs D3, D5); same domain, four structures: scale and business model move the weights at the edges; concurrent-connection count is the tiebreaker.</li>
  <li>In the consolidated requirements table an H is a veto trigger backed by a scenario, an L a licence not to pay; the seventeen footnotes are part of the semantics.</li>
  <li>Five observations: read/write ratio and consistency semantics discriminate most; D11 High, not scale, forces microservices; rollbacks cut dataflows along technical seams; regulation lives in K(a); C10 inherits before it innovates.</li>
  <li>The ten profiles are derivable, not arbitrary: weights trace to binding scenarios, constraints to statutes or contracts, workload shapes to production numbers – the other operand, R(a), is ready.</li>
</ul>
<p>📎 <strong>Materials:</strong> Slide set of Lecture 9 <em>(added below by the lecturer)</em> · Script: Part III – Sections 24 (C6 – Scientific Simulation / Batch Compute), 25 (C7 – Decision Support / BI Analytics), 26 (C8 – Real-Time / IoT Streaming), 27 (C9 – Collaboration / Messaging) and 29 (Stepping Back: Ten Profiles Side by Side).</p>

<!-- BLOCK 3 · Self-study and assignments -->
<h4>🧑‍💻 Self-study and assignments</h4>
<p><strong>📖 Reading before the lecture:</strong> Part III – Sections 24 (C6 – Scientific Simulation / Batch Compute), 25 (C7 – Decision Support / BI Analytics), 26 (C8 – Real-Time / IoT Streaming), 27 (C9 – Collaboration / Messaging) and 29 (Stepping Back: Ten Profiles Side by Side).</p>
<p><strong>Also before the lecture:</strong></p>
<ul>
  <li>Have the walking skeleton ready for the milestone check M3 in the coaching session: it must run end-to-end.</li>
</ul>
<p><strong>🧩 Exercise session:</strong> Coaching session (1 lesson): the teams finish the walking skeleton and the milestone check M3 takes place – the end-to-end thin slice must run.</p>
<p><strong>🛠️ Project work this week</strong> · Milestone M3 – Walking Skeleton (weeks 8–9)</p>
<ul>
  <li>Finish the walking skeleton: the end-to-end thin slice runs – MarketDataService delivers prices, a minimal ResearchAgent produces one validated Insight, delivered through the stable API; a placeholder UI calls it.</li>
  <li>Keep the deterministic core free of LLM calls – this is the line that is graded.</li>
  <li>Use a snapshot of market and news data so that demo and grading are reproducible even if the live APIs misbehave.</li>
  <li>Commit after the milestone and record your decisions as ADRs as you go.</li>
  <li>Apply today's lecture: your ingestion and eval pipelines are C6, your analytics are C7 – the skeleton should already show where an Insight's freshness stamp comes from.</li>
</ul>
<div style="border-left:4px solid #1b6ec2; background:#eef5fc; padding:8px 12px; margin:10px 0;">📌 <strong>Due this week:</strong> Milestone check (week 9): walking skeleton runs end-to-end</div>