PM: Methods in pm4py
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.
---
Would you like me to also make a **visual diagram (infographic-style)** that shows how these methods connect (Discovery → Conformance → Performance) in a full process mining cycle?