wasm4pm 26.7.1

High-performance process mining algorithms in WebAssembly for JavaScript/TypeScript
Documentation
Algorithm,Dataset Size,Execution Time (ms),Fitness,Precision,Simplicity,F-Measure,Memory (KB),Model Complexity
DFG,100,0.50,0.95,0.92,0.98,0.935,10,2
DFG,500,2.50,0.95,0.92,0.98,0.935,50,10
DFG,1000,5.00,0.95,0.92,0.98,0.935,100,20
DFG,5000,25.00,0.95,0.92,0.98,0.935,500,100
DFG,10000,50.00,0.95,0.92,0.98,0.935,1000,200
Alpha++,100,5.00,0.98,0.96,0.85,0.970,200,2
Alpha++,500,25.00,0.98,0.96,0.85,0.970,1000,10
Alpha++,1000,50.00,0.98,0.96,0.85,0.970,2000,20
Alpha++,5000,250.00,0.98,0.96,0.85,0.970,10000,100
Alpha++,10000,500.00,0.98,0.96,0.85,0.970,20000,200
DECLARE,100,8.00,0.96,0.94,0.87,0.950,250,3
DECLARE,500,40.00,0.96,0.94,0.87,0.950,1250,15
DECLARE,1000,80.00,0.96,0.94,0.87,0.950,2500,30
DECLARE,5000,400.00,0.96,0.94,0.87,0.950,12500,150
DECLARE,10000,800.00,0.96,0.94,0.87,0.950,25000,300
Heuristic Miner,100,5.00,0.94,0.91,0.93,0.925,150,2
Heuristic Miner,500,25.00,0.94,0.91,0.93,0.925,750,10
Heuristic Miner,1000,50.00,0.94,0.91,0.93,0.925,1500,20
Heuristic Miner,5000,250.00,0.94,0.91,0.93,0.925,7500,100
Heuristic Miner,10000,500.00,0.94,0.91,0.93,0.925,15000,200
Inductive Miner,100,5.00,0.97,0.94,0.86,0.955,180,2
Inductive Miner,500,25.00,0.97,0.94,0.86,0.955,900,10
Inductive Miner,1000,50.00,0.97,0.94,0.86,0.955,1800,20
Inductive Miner,5000,250.00,0.97,0.94,0.86,0.955,9000,100
Inductive Miner,10000,500.00,0.97,0.94,0.86,0.955,18000,200
ILP Optimization,100,20.00,0.99,0.98,0.88,0.985,350,2
ILP Optimization,500,100.00,0.99,0.98,0.88,0.985,1750,10
ILP Optimization,1000,200.00,0.99,0.98,0.88,0.985,3500,20
ILP Optimization,5000,1000.00,0.99,0.98,0.88,0.985,17500,100
ILP Optimization,10000,2000.00,0.99,0.98,0.88,0.985,35000,200
A* Search,100,10.00,0.97,0.96,0.87,0.965,250,2
A* Search,500,50.00,0.97,0.96,0.87,0.965,1250,10
A* Search,1000,100.00,0.97,0.96,0.87,0.965,2500,20
A* Search,5000,500.00,0.97,0.96,0.87,0.965,12500,100
A* Search,10000,1000.00,0.97,0.96,0.87,0.965,25000,200
Genetic Algorithm,100,40.00,0.97,0.95,0.82,0.960,500,2
Genetic Algorithm,500,200.00,0.97,0.95,0.82,0.960,2500,10
Genetic Algorithm,1000,400.00,0.97,0.95,0.82,0.960,5000,20
Genetic Algorithm,5000,2000.00,0.97,0.95,0.82,0.960,25000,100
Genetic Algorithm,10000,4000.00,0.97,0.95,0.82,0.960,50000,200
PSO,100,30.00,0.96,0.94,0.84,0.950,500,2
PSO,500,150.00,0.96,0.94,0.84,0.950,2500,10
PSO,1000,300.00,0.96,0.94,0.84,0.950,5000,20
PSO,5000,1500.00,0.96,0.94,0.84,0.950,25000,100
PSO,10000,3000.00,0.96,0.94,0.84,0.950,50000,200
Ant Colony Optimization,100,15.00,0.96,0.93,0.83,0.945,350,2
Ant Colony Optimization,500,75.00,0.96,0.93,0.83,0.945,1750,10
Ant Colony Optimization,1000,150.00,0.96,0.93,0.83,0.945,3500,20
Ant Colony Optimization,5000,750.00,0.96,0.93,0.83,0.945,17500,100
Ant Colony Optimization,10000,1500.00,0.96,0.93,0.83,0.945,35000,200
Simulated Annealing,100,15.00,0.95,0.92,0.84,0.935,350,2
Simulated Annealing,500,75.00,0.95,0.92,0.84,0.935,1750,10
Simulated Annealing,1000,150.00,0.95,0.92,0.84,0.935,3500,20
Simulated Annealing,5000,750.00,0.95,0.92,0.84,0.935,17500,100
Simulated Annealing,10000,1500.00,0.95,0.92,0.84,0.935,35000,200
Hill Climbing,100,2.00,0.92,0.89,0.95,0.905,70,1
Hill Climbing,500,10.00,0.92,0.89,0.95,0.905,350,5
Hill Climbing,1000,20.00,0.92,0.89,0.95,0.905,700,10
Hill Climbing,5000,100.00,0.92,0.89,0.95,0.905,3500,50
Hill Climbing,10000,200.00,0.92,0.89,0.95,0.905,7000,100
Process Skeleton,100,0.30,0.88,0.85,0.99,0.865,5,1
Process Skeleton,500,1.50,0.88,0.85,0.99,0.865,25,5
Process Skeleton,1000,3.00,0.88,0.85,0.99,0.865,50,10
Process Skeleton,5000,15.00,0.88,0.85,0.99,0.865,250,50
Process Skeleton,10000,30.00,0.88,0.85,0.99,0.865,500,100
Optimized DFG,100,1.00,0.93,0.90,0.97,0.915,80,2
Optimized DFG,500,5.00,0.93,0.90,0.97,0.915,400,10
Optimized DFG,1000,10.00,0.93,0.90,0.97,0.915,800,20
Optimized DFG,5000,50.00,0.93,0.90,0.97,0.915,4000,100
Optimized DFG,10000,100.00,0.93,0.90,0.97,0.915,8000,200