Here’s a comparison table of the main methods in process mining (as available in PM4Py) so you can see their differences at a glance:
Process Discovery Methods
| Method |
Output Model |
Pros |
Cons |
Best Use Case
|
| Alpha Miner |
Petri Net |
Simple, foundational, easy to explain |
Very sensitive to noise/incomplete logs |
Educational/demo purposes, very clean logs
|
| Heuristics Miner |
Heuristics Net / Petri Net |
Handles noise, considers frequency |
May oversimplify rare behavior |
Real-life logs with noise and high variability
|
| Inductive Miner |
Petri Net / Process Tree / BPMN |
Always produces sound models, block-structured |
May abstract away some detail |
General-purpose discovery, recommended default
|
| ILP Miner |
Petri Net |
Precise, mathematically grounded |
Heavy computational cost |
Small/medium logs where precision is critical
|
| DFG Discovery |
Directly-Follows Graph |
Very fast, intuitive visualization |
Lacks formal semantics, not executable |
Quick insights, dashboards
|
Conformance Checking Methods
| Method |
Pros |
Cons |
Best Use Case
|
| Token-Based Replay |
Fast, intuitive, easy to compute |
Less precise, may misrepresent deviations |
Quick conformance estimation
|
| Alignment-Based Checking |
Very precise, finds optimal matches |
Computationally expensive for large logs |
Audit scenarios, compliance checking
|
| Log Skeleton |
Lightweight, structural conformance |
Not as expressive as Petri net alignments |
Quick structural validation
|
Performance Analysis
| Technique |
Pros |
Cons |
Best Use Case
|
| Sojourn / throughput times |
Easy to interpret, highlights bottlenecks |
Needs reliable timestamp data |
Detecting slow activities
|
| Time annotations on arcs |
Visual enrichment of models |
Only as good as the log quality |
Identifying bottlenecks in process paths
|
| Case duration analysis |
Summarizes case lifetimes |
Doesn’t explain internal causes |
SLA monitoring
|
Other Techniques
| Method |
Pros |
Cons |
Best Use Case
|
| Trace Variants Analysis |
Simple, shows different execution paths |
Can explode with many variants |
Exploratory analysis
|
| Trace Clustering |
Groups similar behaviors |
Choice of clustering algorithm impacts results |
Finding behavior patterns
|
| Predictive Monitoring (via ML) |
Anticipates outcomes, remaining time |
Needs feature engineering, external ML models |
Predictive SLA, early-warning systems
|
Key Takeaway:
- If you want robust discovery → use Inductive Miner.
- If you need fast visualization → use DFG Discovery.
- For compliance checks → prefer Alignment-based Conformance.
- For real-life noisy data → Heuristics Miner is strong.