PM: Methods in pm4py - Revision history

Unknown user at 08:16, 13 September 2025

2025-09-13T08:16:29Z

← Older revision		Revision as of 08:16, 13 September 2025
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	\| '''ILP Miner''' \|\| Petri Net \|\| Precise, mathematically grounded \|\| Heavy computational cost \|\| Small/medium logs where precision is critical		\| '''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		\| '''DFG Discovery''' \|\| Directly-Follows Graph \|\| Very fast, intuitive visualization \|\| Lacks formal semantics, not executable \|\| Quick insights, dashboards
	\|}		\|}

Unknown user at 08:15, 13 September 2025

2025-09-13T08:15:29Z

← Older revision		Revision as of 08:15, 13 September 2025
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	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:		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==

	~~### 🔹 Process Discovery Methods~~

	\| Method \| Output Model \| Pros \| Cons \| Best Use Case \|		{\| class="wikitable"
	\| -~~------------------- \| ------------------------------- \| ---------------------------------------------- \| --------------------------------------- \| ---------------------------------------------- \|~~		\|-
	\| Alpha Miner \| Petri Net \| Simple, foundational, easy to explain \| Very sensitive to noise/incomplete logs \| Educational/demo purposes, very clean logs \|		! '''Method''' !! '''Output Model''' !! '''Pros''' !! '''Cons''' !! '''Best Use Case'''
	\| 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 \|		\| '''Alpha Miner''' \|\| Petri Net \|\| Simple, foundational, easy to explain \|\| Very sensitive to noise/incomplete logs \|\| Educational/demo purposes, very clean logs
	\| 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 \|		\| '''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 \|		==Conformance Checking Methods==
	~~\| ---------------------------- \| ----------------------------------- \| ----------------------------------------- \| ------------------------------------ \|~~
	~~\| 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 \|~~

	---		{\| class="wikitable"
			\|-
			! '''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 \|~~

	~~---~~		==Performance Analysis==

	~~### 🔹 Other Techniques~~		{\| class="wikitable"
			! '''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
			\|}

	\| Method \| Pros \| Cons \| Best Use Case \|		==Other Techniques==
	~~\| ---------------------------------- \| --------------------------------------- \| ---------------------------------------------- \| ------------------------------------- \|~~
	~~\| 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 \|~~

	---		{\| class="wikitable"
			! '''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.		=='''Key Takeaway:'''==
	* If you need fast visualization → use DFG Discovery.
	* For compliance checks → prefer Alignment-based Conformance.
	* For real-life noisy data → Heuristics Miner is strong.

	~~---~~		* If you want '''robust discovery''' → use '''Inductive Miner'''.
			* If you need '''fast visualization''' → use '''DFG Discovery'''.
	~~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?~~		* For '''compliance checks''' → prefer '''Alignment-based Conformance'''.
			* For '''real-life noisy data''' → '''Heuristics Miner''' is strong.

Unknown user: Created page with "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 Me..."

2025-09-12T23:46:46Z

Created page with "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 Me..."

New page

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?

PM: Methods in pm4py - Revision history

Unknown user at 08:16, 13 September 2025

Unknown user at 08:15, 13 September 2025

Unknown user: Created page with "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 Me..."

Unknown user: Created page with "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 Me..."