Increase benchmark runs and fix fixed-iteration ceiling #348

Summary

The current configuration (BENCHMARK_RUNS=1, BENCHMARK_MAX_FIXED_WINDOW_SECONDS=4500) produces too few samples for reliable statistical analysis. The thesis (§4.5, Table 4.5.1) acknowledges that bootstrap CI from fewer than 30 samples is noisy. The current ceiling also caps several RQ2 experiments below their target iteration count.

Proposed changes:

• Split BENCHMARK_RUNS into per-workload counts: 30 RQ1 runs, 3 RQ2 runs
• BENCHMARK_MAX_ITERATION_SECONDS: 4500 → 3600 (60 min)
• BENCHMARK_MAX_FIXED_WINDOW_SECONDS: 75 * 60 → 5 * BENCHMARK_MAX_ITERATION_SECONDS (= 18,000s = 300 min)
• Skip national-scale-spatial-join-duckdb-large — exceeds 60-min per-iteration threshold (median 70.8 min). Existing data from run 2026-05-24-HBJYYT is sufficient. Report as a finding: single-node processing becomes impractical at large scale.
• main.py: partition experiments into RQ1 (pip, knn, bbox) and RQ2 (national-scale-spatial-join) by ID prefix, loop each group with its own run count.

Estimated wall-clock per benchmark run

RQ1 — Sequential stopping (includes 10 min ingestion delay per experiment)

RQ2 — Fixed iteration (1 warmup + 5 timed, 60 min/iter, 300 min ceiling, includes 10 min ingestion)

Skipped experiments (9)

Total suite estimate

Per-iteration times are median estimates from run 2026-05-24-HBJYYT. Cost estimates based on Azure ACI, Databricks, and PostgreSQL pricing from the same run, adjusted for increased iteration counts.