Difference between revisions of "PM: Methods in pm4py"
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Onnowpurbo (talk | contribs) (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...") |
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| − | Here’s a | + | 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== | |
| − | |||
| − | | | + | {| class="wikitable" |
| − | | - | + | |- |
| − | | | + | ! '''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 | ||
| + | |} | ||
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| − | |||
| − | + | ==Conformance Checking Methods== | |
| − | |||
| − | |||
| − | |||
| − | |||
| − | --- | + | {| 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 | ||
| + | |} | ||
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| − | + | ==Performance Analysis== | |
| − | + | {| 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 | ||
| + | |} | ||
| − | + | ==Other Techniques== | |
| − | |||
| − | |||
| − | |||
| − | |||
| − | --- | + | {| 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'''. | |
| − | + | * If you need '''fast visualization''' → use '''DFG Discovery'''. | |
| − | + | * For '''compliance checks''' → prefer '''Alignment-based Conformance'''. | |
| + | * For '''real-life noisy data''' → '''Heuristics Miner''' is strong. | ||
Latest revision as of 15:16, 13 September 2025
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.