Types of Process Mining

Transcription

1 1

2 Types of Process Mining 2

3 Types of Mining Algorithms 3

4 Types of Mining Algorithms 4

5 Control-Flow Mining 1. Start Get Ready Start Start Travel by Get Train Ready Start 3. Get Beta Event Travel Ready Starts by Train Get 4. Visit Ready Travel Brewery Beta by Event Car Starts Travel 5. Beta Have by Dinner Give Car Event a Talk Starts Conference 6. Give Go Home Visit a Starts Talk Brewery Join 7. Visit Travel Reception by Have Brewery Train Dinner Have 8. Dinner Have Go Dinner Home Go 9. Home Go Home Travel by Train Pay Parking Pay Parking Travel Travel by Car by Car 10. End 5

6 Mining Common Constructs Sequence Splits Joins Loops Non-Free Choice Invisible Tasks Duplicate Tasks 6

7 Mining Common Constructs Sequence Splits Joins Loops Non-Free Choice Invisible Tasks Duplicate Tasks + noise in logs! 7

8 Process Mining Algorithms α-algorithm Heuristics Miner Genetic Miner Fuzzy Miner 8

9 α-algorithm 1. Read a log 2. Get the set of tasks 3. Infer the ordering relations 4. Build the net based on inferred relations 5. Output the net 9

10 α-algorithm - Ordering Relations >,,,# Direct succession: x>y iff for some case x is directly followed by y. Causality: x y iff x>y and not y>x. Parallel: x y iff x>y and y>x Unrelated: x#y iff not x>y and not y>x. 10

11 α-algorithm - Insight 11

12 α-algorithm Quick Exercise Case ID Task Name Originator Timestamp Case ID Task Name Originator Timestamp 1 File Fine Anne :00:00 3 Reminder John :00:00 2 File Fine Anne :00:00 2 Process Payment system :05:00 1 Send Bill system :05:00 2 Close case system :06:00 2 Send Bill system :07:00 4 Reminder John :10:00 3 File Fine Anne :00:00 4 Reminder Mary :10:00 3 Send Bill system :00:00 4 Process Payment system :01:00 4 File Fine Anne :00:00 4 Close Case system :30:00 4 Send Bill system :10:00 3 Reminder John :00:00 Process 1 Payment system :05:00 3 Reminder John :00:00 1 Close Case system :06:00 3 Process Payment system :00:00 2 Reminder Mary :00:00 3 Close Case system :01:00

13 α-algorithm Log properties + target nets If log is complete with respect to relation >, it can be used to mine SWF-net without short loops Structured Workflow Nets (SWF-nets) have no implicit places and the following two constructs cannot be used: 13

14 α-algorithm No short loops B>B and not B>B implies B B (impossible!) Why no short loops? One-length Two-length A>B and B>A implies A B and B A instead of A B and B A 14

15 α-algorithm Common Constructs Why no duplicate tasks? Why not invible tasks? Why noisefree logs? No invisible tasks, non-free-choice or duplicate tasks No noisy logs 15

16 Process Mining Algorithms α-algorithm Heuristics Miner Genetic Algorithm Fuzzy Miner 16

17 Heuristics Miner 1. Read a log 2. Get the set of tasks 3. Infer the ordering relations based on their frequencies 4. Build the net based on inferred relations 5. Output the net 17

18 Heuristics Miner The more frequently a task A directly follows another task B, and the less frequently the opposite occurs, the higher the probability that A causally follows B! Robust to invisible tasks and noisy logs No non-free-choice or duplicate tasks 18

19 Process Mining Algorithms α-algorithm Heuristics Miner Genetic Miner Fuzzy Miner 19

20 Genetic Process Mining (GPM) Genetic Algorithms + Process Mining Genetic Algorithms Search technique that mimics the process of evolution in biological systems Advantages Tackle all common structural constructs Robust to noise Disadvantages Computational Time 20

21 Genetic Process Mining (GPM) Algorithm: Internal Representation Fitness Measure Genetic Operators 21

22 GPM Fitness Measure Guides the search! 22

23 GPM Fitness Measure 23

24 GPM Fitness Measure Overgeneral solution Punish for the amount of enabled tasks during the parsing! 24

25 GPM Fitness Measure Overspecific solution Punish for the amount of duplicate tasks with common input/output tasks! 25

26 Process Mining Algorithms α-algorithm Heuristics Miner Genetic Miner Fuzzy Miner 26

27 Fuzzy Miner - Motivation Mine less structured processes! 27

28 Fuzzy Miner - Motivation 28

29 Fuzzy Miner 29

30 No Ask Question or Fuzzy Miner Give Talk! Abstracting even more from details! All details! 30

31 Want to now more? Why not do a Masters thesis on the application of process mining for discovering business services and objects? The ACSI project is looking for help: 31