Timestamps for each case start near this value and increase per activity.
Resources
Name
Remove
Resources are assigned to activities. If an activity uses "Random", one of these will be picked.
Working Time (Calendar Constraints)
From
To
Event timestamps will be clamped into these working windows.
Activities
Activity name
Avg duration (mins)
Offset min (mins)
Offset max (mins)
Resource assignment
Rework settings
Remove
Duration = avg minutes + random offset (min to max). Rework can loop back to the chosen target before following outgoing transitions.
Conditional Delays (Optional)
Activity
Field
Operator
Value
Delay amount
Delay unit
Remove
Add extra wait before an activity when the condition matches (case or event attributes). Multiple matched rules are summed.
Flow Definition
From activity
Next activity
Flow type
Probability
Remove
Choose how the next step behaves: Directly follows (default sequence), OR (exclusive branch using probabilities), or AND (parallel branches; each taken based on its probability). Use the special "END" target to finish a case.
Case System (case-level source)
System name
Probability
Remove
Pick which system the case runs in, using the configured probabilities.
Case Attributes
Attribute name
Type
Definition
Remove
Adds metadata or dimensions per case. Categorical values are picked randomly; numeric values use a random value within the range.
Use the floating toolbar to generate and download logs.
Organizations often want to explore process mining, run a proof of concept, or compare different process intelligence tools. But there is a recurring blocker: getting suitable event log data.
Real operational data is usually difficult to extract, contains sensitive information, and requires significant preparation before it can be used. This slows down experimentation, onboarding, training, and early prototyping.
The Synthetic Process Mining Log Generator solves this by letting business users sketch a lightweight process model and instantly generate realistic CSV event logs that mimic real-world behaviors — without accessing any confidential systems.
What this project enables
Fast experimentation: Build and adjust simple process flows and immediately produce logs for discovery, conformance checking, and performance analysis.
Training & demos: Generate clean sample datasets to teach process mining concepts or demonstrate tooling.
Early project shaping: Model a “to-be” or illustrative “as-is” process when real data isn’t yet available.
Vendor evaluation: Test multiple process mining platforms with identical synthetic logs.
Why this is valuable for business users
No dependence on IT data extraction
Safe for experimentation — no personal or sensitive data
Quick to iterate — change your process design and regenerate in seconds
Ideal for proofs of concept where clean data is needed quickly
Supports storytelling — demonstrate the value of process mining using relatable, customizable scenarios
How to use it
Configure volumes.Set how many cases you want (up to 2,500 per run) and how many events each should generate. The system validates probabilities, flow completeness, and duration logic automatically.
Define the process structure.Outline activities, decision branches, sequences, and end points. Optionally describe resources/roles, duration expectations, and execution type (manual, system, robotic).
Add realistic behavior.Enrich with rework or loops (with probability and limits), conditional delays (e.g., specific customer segments taking longer), and case attributes such as region, customer type, risk level, or product.
Generate & preview.Simulate the process to create timestamped events, preview the first rows, and download CSV event logs and case-level tables compatible with standard process mining tools.
Import and analyze.Load the synthetic logs into your preferred platform (e.g., Celonis, UIPath Process Mining, Apromore, PAFnow, Disco) to explore variants, bottlenecks, lead times, rework, and compliance patterns.
Questions or feedback?
Share ideas, requests, or bugs through the form below.