TLOGic

Ver 0.297

Your Files

Drop TLOG files here

or click the "Add Files" button above

Quick Actions

Analyze

Deep dive into transactions

AI Analysis

AI-powered insights

Load Test

Generate test packages

Non-TLOG Test

Test with RIONTQ.DAT files

Bulk Translate/Export

Export to POSLOG, JSON, CSV, or Excel

Keyed File Viewer

Parse 4690 keyed data files

DEV

ACE Reports

Generate POS system reports

No files loaded

Drop TLOG files above or click "Add Files" to get started

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Rows:

Transaction Details

ELERA JSON not generated yet.

Download

ELERA node ID (store)

Transformation warnings and estimates

POSLOG not generated yet.

Download

Select a transaction and click "Compare" to analyze production vs development POSLOG.

Production POSLOG TLPSERVER

Development POSLOG WASM/Rust

Comparison Results

Click "Compare" to see differences between
production and development POSLOG.

Select a transaction and click "Send" to send RioRequest to remote service.

RioRequest OUTBOUND

RioResponse INBOUND

Load Test content is managed by the Testing workspace.

AI Test - Quick Operator Analysis

Use Google Gemini/OpenAI/Claude to analyze operator performance data and get actionable insights.
Click the AI Settings button below to configure your API key(s) and model(s).

Quick Analysis Prompts

Prompt

Stats

Debug Info

Department Data

Total Departments

Total Items

Total Sales

$0.00

Returns

$0.00

Voids

$0.00

Department Performance

Size = Sales Amount | Color = Return Rate

Top 10 Departments by Sales Amount

Top 10 Departments by Return Amount

Bottom 10 Departments by Sales Amount

Operator Data

Explore KPIs by operator. Sort columns or download the data.

Tune weights (optional). Charts and scores update live.

Txn Count

Sales Amount

Avg $/Txn

Void Rate (/100)

Refund Rate (/100)

Void $ / Sales

Refund $ / Sales

Timing Weights (affect Composite)

Positive weights reward lower time/percent (we invert internally). Set to 0 to ignore.

Ring seconds / txn

1.0

Tender seconds / txn

1.0

Inactive share (%)

0.5

Special/locked share (%)

0.5

Operator Leaderboard (Composite Score)

ranks operators based on weighted KPIs for a single view of best/worst performers

Speed vs Accuracy — Avg Sales per Txn vs Void Rate (size = Txn Count)

shows trade-offs between ring speed and void rate; bubble size indicates transaction volume

KPI Heatmap (Z-Scores)

Compares all KPIs across operators, highlighting strengths (green) and weaknesses (red). Green = above average; Red = below average.

Operator Time Breakdown

Shows ring/tender/inactive/special time. Default is seconds per transaction. (You can flip to percentage shares in code by passing { mode: "share" }.)

Display:

Distinct Customers

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Total Sales $

—

Returns $

—

Voids $

—

Top Customer (by Sales $)

—

Pareto (Top 20%)

—

Txns (with ID): —

Sales Count: —

Return Count: —

Void Count: —

Top 10 Customers by Sales $

Visits (Top 10 by Txns)

Visits ≈ # of transactions with an attached customer ID.

Pareto of Sales $

Sorted by Sales $. Bars show per-customer contribution; the line shows cumulative share (often a small group contributes most revenue).

Home Store Leaderboard — Top 10 by Sales $

Aggregated across customers by their home store from the 11:FF record. Txns count includes only transactions where a customer was identified.

Customers — Top 100

Customer #	Home Store	Txns	ItmSalesCnt	ItmRtrnCnt	ItmVoidCnt	Sales $	Return $	Void $

Click headers to sort • default is by Sales $ (desc).

Distinct Items

—

Total Sales $

—

Total Returns $

—

Total Voids $

—

Top 10 Items by Sales $

Void % (Top 10 by Sales Count)

Void % = void $ / (sales $ + void $)

Pareto of Sales $ (Bars + Cumulative %)

The Pareto chart sorts items by Sales $. Bars show each item's contribution; the line shows cumulative contribution. Typically, a small set of items accounts for a large share of sales (e.g., 20% of items drive 80% of sales).

Items — Top 50

Item	Occurrences	Sales Cnt	Return Cnt	Void Cnt	Sales $	Return $	Void $

Click headers to sort • default is by Sales Count (desc).

Sales Heatmap

Visualize sales data over the course of the day with a heatmap based on transaction times.

TLOGic: Actionable Intelligence from Tlog Data

TLOGic is a powerful analysis tool for IBM Transaction Log (Tlog) files. It transforms raw transaction data into critical business insights, enabling retailers to take decisive action and build advanced AI models.

Powered by the REDList Tlog Parser

At the heart of TLOGic is a next-generation parsing engine developed by REDList Solutions. Built entirely in Rust, it delivers a foundation of elite performance and security.

Secure & Resilient: Leveraging Rust's memory safety features to protect your critical data.
High-Performance: Engineered for speed to process massive Tlog volumes effortlessly.
Scalable Architecture: Designed for modern infrastructure, ready to run anywhere—from the Edge to the Cloud.

Features

Transaction parsing and analysis
Advanced filtering capabilities
Statistical analysis
Multiple data export formats
Real-time data visualization

Usage Tips

Use the filter panel to narrow down transactions
Click on any transaction to view detailed information
Export data in multiple formats for further analysis
Use the statistics panel to understand data patterns

Supported Formats

.tlg - IBM Transaction Log files
.dat - IBM Transaction Log files
.db0 - IBM Tlog Archive files

Technical Details

Parser Engine: WASM-based

Frontend: Bootstrap 5 + Vanilla JS

Data Processing: Client-side

Testing Tools

Load a TLOG file first to access Load Test generation and RIO integration features.

Files

Format

Options

Select Files to Export

No files loaded in the Files Hub

Select Export Format

POSLOG

ARTS XML format

JSON

JSON format

CSV

Comma-separated

Excel

XLSX workbook

Export Summary

Files: 0 selected
Transactions: 0 total
Format: Not selected

Your Tlog has been successfully parsed!

Quick Tour: Explore these powerful features to get the most out of your data.

Raw Data

View and filter transaction data with advanced search capabilities. Select multiple transactions for bulk export in various formats.

AI Insights

NEW

Get AI-powered insights on operator performance using Google Gemini. Ask questions and receive intelligent analysis.

Stats

Comprehensive statistical analysis including transaction counts, string distributions, and performance metrics.

Department Data

Interactive treemap visualization of department sales, returns, and void patterns. Export department data for further analysis.

Operator Data

NEW AI FEATURE

Discover our latest AI-powered operator analytics! Get intelligent insights into operator performance, patterns, and anomalies.

Check it out in the sidebar!

Pro Tips:

Use Shift+Click to select transaction ranges
Right-click selected transactions for quick export options
Toggle between light and dark themes in the navbar
Advanced filters support ranges (e.g., 100..500) and comparisons (e.g., >=1000)

Issue summary

Severity

Contact email

Steps to reproduce

Expected result

Actual result

Additional notes

Include in bundle

Sanitized client logs and runtime errors

Visible debug panel snapshot

Current screen data and selected transaction summary

Screenshot captured by me

Optional attachments

Attach a sample export, screenshot, or other file you want support to see.

Screenshot capture

No screenshot captured

I understand this will package the selected diagnostics into a ZIP and upload it for troubleshooting.

Bundle preview

Bundle preview not generated yetClick Review Bundle

Current Schema:

Embedded

TLOG Metadata Schema

Built-in schema IBM/Toshiba ACE standard

Custom schema Your tlog-metadata.xml

Custom Schema Help

What is a Custom Schema?

A custom TLOG metadata schema allows you to parse transaction logs that contain proprietary string formats not defined in the standard IBM/Toshiba specification.

When Do I Need One?

Your POS system uses custom transaction strings (e.g., "00", "01")
You have proprietary tlog string formats
Your TLOG output was customized by your vendor

Schema File Format

Your custom schema must be an XML file following this structure:

<?xml version="1.0" encoding="UTF-8"?>
<TlogTemplate key="..." alt_key="...">
  <fileinfo keylen="4" reclen="64" />

  <record key='99' name='CustomString'>
    <field id='1' name='FieldName'
           type='P' length='2' />
  </record>
</TlogTemplate>

Field Types

P Packed BCD

A/C ASCII/Character

N Numeric

D Date

I Integer

Important Notes

Custom schemas are saved in your browser and persist across sessions
You can switch back to the embedded schema at any time
Loading a new schema will prompt you to re-parse your current TLOG
Use "Validate Schema" to check your XML before loading

Tip: Custom schemas must match your TLOG's record structure exactly. Field types must align with your Tlog Strings format.

Appearance

Dark Mode Switch between light and dark themes

Compact View Show more transactions on screen

Display Options

Show Timestamps Display transaction date/time

Syntax Highlighting Color-code transaction fields

Tender Mappings

Define how tender type codes are displayed in JSON2 output.

0 mappings configured

About TLOGic

Version: .297

Build: 2026-11-05

Parser: WASM v-0.2.3

Environment: Production

License: —

License Tiers:

Trial — 14-day full access to all features
Standard — Core analysis & load testing
Professional — All features including AI Tools, RIO, POSLOG-D, exports

Documentation Get Help

Map IBM tender type codes to human-readable names for JSON2 output. These mappings are stored locally and persist across sessions.

Add New Mapping

Code

Display Name

Code	Display Name	Actions

Hide 20 strings

Tips: Use , for OR and ; for AND. Ranges: .. Comparisons: < <= = >= >

Record #

Original list number, starting at 1; unchanged by sorting or filtering

Transaction

Filter by transaction number

Terminal

Terminal ID or range

Operator

Operator ID with comparison

Offset

File offset position

Size

Transaction size in bytes

Date/Time

Date or datetime filter

TranType

Type code or name (e.g., SALE, VOID)

Your Files

Drop TLOG files here

Loaded Files

Quick Actions

Analyze

AI Analysis

Load Test

Non-TLOG Test

Bulk Translate/Export

Keyed File Viewer

ACE Reports

No files loaded

Transaction Details

Quick Analysis Prompts

Department Data

Department Performance

Top 10 Departments by Sales Amount

Top 10 Departments by Return Amount

Bottom 10 Departments by Sales Amount

Operator Data

Timing Weights (affect Composite)

Operator Leaderboard (Composite Score)

Speed vs Accuracy — Avg Sales per Txn vs Void Rate (size = Txn Count)

KPI Heatmap (Z-Scores)

Operator Time Breakdown

Top 10 Customers by Sales $

Visits (Top 10 by Txns)

Pareto of Sales $

Home Store Leaderboard — Top 10 by Sales $

Customers — Top 100

Top 10 Items by Sales $

Void % (Top 10 by Sales Count)

Pareto of Sales $ (Bars + Cumulative %)

Items — Top 50

Sales Heatmap View Data

TLOGic: Actionable Intelligence from Tlog Data

Powered by the REDList Tlog Parser

Features

Usage Tips

Supported Formats

Technical Details

Testing Tools

Department Data

Operator Data

Sales Heatmap Data

Settings

TLOG Metadata Schema

What is a Custom Schema?

When Do I Need One?

Schema File Format

Field Types

Important Notes

Appearance

Display Options

Tender Mappings

About TLOGic

Advanced Filters

Sales Heatmap