Digi@Flow Transaction Engine — 12→16 Column Processor with Deterministic RoundingIntroduction

[digico Iwate (Digico)]

This engine is part of a production workflow built on MementoDB.

It converts a 12-column merged transaction CSV into a deterministic 16-column Digi@Flow format, with a focus on reproducibility and stable financial outcomes in a mobile execution environment.

I’m documenting an approach and interested in how others think about rounding, tax normalization, and transaction modeling.

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Snapshot Model (Input Layer)

The engine operates on a snapshot of merged transaction data.

In this model:

• each row is treated as an immutable input state

• no external lookups or cross-row dependencies

• all calculations are finalized per row

• upstream inconsistencies are normalized at this layer

This keeps the output deterministic regardless of how the source data was produced.

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Why This Exists

In practice, financial pipelines in MementoDB often require:

• deterministic rounding at the smallest currency unit (0.01)

• row-level finalization (no cross-row adjustments)

• tolerance for inconsistent tax rate formats (0.08 / 8 / 1.08)

• safe handling of quoted CSV fields

• predictable behavior under mobile constraints

This engine is used in production, so operational stability is prioritized over mathematical symmetry.

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Design PrinciplesDeterministic Rounding

All monetary values are finalized per row using Math.floor() with a small epsilon to suppress floating-point noise.

Tax Rate Normalization

Accepts multiple formats (0.08, 8, 1.08) and normalizes to 0.08.

Service vs. Product Logic

If cost == unit price and margin resolves to zero,
the full ex-tax amount is treated as profit (service/labor model).

CSV Handling

A lightweight parser handles quoted fields.
Optimized for controlled input, not full RFC4180 compliance.

Execution Context

Designed for MementoDB’s JavaScript runtime with mobile-first constraints.

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Trade-offs

These choices are intentional:

• floor-based rounding ensures consistent accumulation

• epsilon is used only to neutralize floating-point artifacts

• date formatting follows the local runtime timezone

• service detection is heuristic and model-dependent

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Code// Digi@Flow Transaction Processor (International Edition) // Input: 12-column merged transaction CSV ("取引結合") // Output: 16-column Digi@Flow CSV // Version: 2026-01-31_2045 Copilot Assisted — Digico Edition // Columns: // 1 Customer, 2 CustomerID, 3 Date, 4 VoucherID, 5 BranchID, // 6 Item, 7 Qty, 8 UnitPrice, 9 Unit, 10 TaxRate, // 11 Cost, 12 AmountExTax, 13 TaxAmount, 14 AmountIncTax, // 15 Profit, 16 Note function parseCSV(line){ var r = [], c = "", q = false; for (var i = 0; i < line.length; i++) { var x = line[i]; if (x === '"') { q = !q; continue; } if (x === "," && !q) { r.push(c); c = ""; } else c += x; } r.push(c); return r; } function toDateString(sec){ var n = Number(sec) || 0; if (n > 1000000000000) n = Math.floor(n / 1000); var d = new Date(n * 1000); var y = d.getFullYear(); var m = ("0" + (d.getMonth() + 1)).slice(-2); var da = ("0" + d.getDate()).slice(-2); return y + "/" + m + "/" + da; } function num(x){ return Number(String(x == null ? "" : x).trim()) || 0; } function str(x){ return String(x == null ? "" : x).trim(); } var EPS = 0.0000001; var raw = entry().field("取引結合") || ""; var rows = raw.split(/\r?\n/); var out = rows.reduce(function(a, line){ line = line.trim(); if (!line) return a; while (line.charAt(0) === "*") { line = line.substring(1); } var cols = parseCSV(line); if (cols.length < 12) return a; var c1 = str(cols[0]); var c2 = num(cols[1]); var c3s = cols[2]; var c4 = num(cols[3]); var c5 = num(cols[4]); var c6 = str(cols[5]); var c7 = num(cols[6]); var c8 = num(cols[7]); var c9 = str(cols[8]); var c10 = num(cols[9]); var c11 = num(cols[10]); var c16 = str(cols[11]); var c3 = c3s ? toDateString(c3s) : ""; var ex = Math.floor(c7 * c8 + EPS); var rate = c10; if (rate >= 2) { rate = rate / 100; } else if (rate > 1 && rate < 2) { rate = rate - 1; } var tax = Math.floor(ex * rate + EPS); var inc = ex + tax; var prof = ex - Math.floor(c11 * c7 + EPS); if (c7 > 0 && prof === 0 && c11 === c8) { prof = ex; } var lineOut = "*" + "\"" + c1 + "\"," + c2 + "," + c3 + "," + c4 + "," + c5 + "," + "\"" + c6 + "\"," + c7 + "," + c8 + "," + "\"" + c9 + "\"," + c10 + "," + c11 + "," + ex + "," + tax + "," + inc + "," + prof + "," + "\"" + c16 + "\""; a.push(lineOut); return a; }, []); out.join("\n");

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Closing

Curious how others approach this layer:

• Do you prefer floor, round, or banker’s rounding for accumulation consistency?

• How do you normalize tax inputs in loosely structured data?

• Do you model service vs. product explicitly, or infer it like this?

Interested in different trade-offs and design directions.

[Bill Crews]

WTF?

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