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Measuring the Precision of
Multi-perspective
Process Models
Felix Mannhardt
joint work with
Massimiliano de Leoni, Hajo A....
Precision
Department of Mathematics and Computer Science PAGE 1 / 8
β€œFlower Model” lacking any precision
B
C
A
Precision of Multi-perspective Process Models
Department of Mathematics and Computer Science PAGE 2 / 8
A π‘π‘Ÿπ‘’π‘π‘–π‘ π‘–π‘œπ‘› 𝐴
B
π‘π‘Ÿ...
Approach: Multi-perspective Precision
Department of Mathematics and Computer Science PAGE 3 / 8
Multi-perspective
Process ...
Precision: Observed / Possible Behavior
Department of Mathematics and Computer Science PAGE 4 / 8
C Id Event Loan obs pos
...
Full Example for Model A & Model B
Department of Mathematics and Computer Science PAGE 5 / 8
A
C Id Event Loan 𝒐𝒃𝒔 𝑷 𝒑𝒐𝒔 𝑷...
Evaluation on Road Fines Log
0.30
0.36
0.64
0.83
0.0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1.0
Inductive Miner Inductive Min...
Summary
Department of Mathematics and Computer Science PAGE 7 / 8
β€’ 1st precision measure for
multi-perspective process mo...
Department of Mathematics and Computer Science
Image source: http://commons.wikimedia.org/wiki/File:Pictofigo_-_Idea.png
Q...
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Measuring the Precision of Multi-perspective Process Models

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Process models need to reflect the real behavior of an organization's processes to be beneficial for several use cases, such as process analysis, process documentation and process improvement. One quality criterion for a process model is that they should precise and not express more behavior than what is observed in logging data. Existing precision measures for process models purely focus on the control-flow dimension of a process model, thereby ignoring other perspectives, such as the data objects manipulated by the process, the resources executing process activities, and time-related aspects (e.g., activity deadlines). Focusing on the control-flow only, the results may be misleading. This paper extends existing precision measures to incorporate the other perspectives and, through an evaluation with a real-life process and corresponding logging data, demonstrates how the new measure matches our intuitive understanding of precision.

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Measuring the Precision of Multi-perspective Process Models

  1. 1. Measuring the Precision of Multi-perspective Process Models Felix Mannhardt joint work with Massimiliano de Leoni, Hajo A. Reijers, Wil M.P. van der Aalst
  2. 2. Precision Department of Mathematics and Computer Science PAGE 1 / 8 β€œFlower Model” lacking any precision B C A
  3. 3. Precision of Multi-perspective Process Models Department of Mathematics and Computer Science PAGE 2 / 8 A π‘π‘Ÿπ‘’π‘π‘–π‘ π‘–π‘œπ‘› 𝐴 B π‘π‘Ÿπ‘’π‘π‘–π‘ π‘–π‘œπ‘› 𝐡 π‘π‘Ÿπ‘’π‘π‘–π‘ π‘–π‘œπ‘› 𝐡 > π‘π‘Ÿπ‘’π‘π‘–π‘ π‘–π‘œπ‘› 𝐴 Existing work ignores added precision by multi-perspective rules / constraints
  4. 4. Approach: Multi-perspective Precision Department of Mathematics and Computer Science PAGE 3 / 8 Multi-perspective Process Model (P) Fitting Event Log (L) Precision [0..1] INPUT OUTPUT APPROACH 𝒆 βˆˆπ‘³ 𝒐𝒃𝒔𝒆𝒓𝒗𝒆𝒅 𝑷 (𝒆) π’‘π’“π’†π’„π’Šπ’”π’Šπ’π’(𝑷, 𝑳) = 𝒆 βˆˆπ‘³ π’‘π’π’”π’”π’Šπ’ƒπ’π’† 𝑷(𝒆)
  5. 5. Precision: Observed / Possible Behavior Department of Mathematics and Computer Science PAGE 4 / 8 C Id Event Loan obs pos 1 𝑒1 Handle Request 800 1 𝑒2 Simple Check - 1 𝑒3 Decide - 2 𝑒4 Handle Request 1800 2 𝑒5 Ext. Check - 2 𝑒6 Decide - 𝒐𝒃𝒔 𝑷 𝒆 𝟏 = { Handle Request } = 𝟏 𝒑𝒐𝒔 𝑷 𝒆 𝟏 = Handle Request = 𝟏 𝒔𝒕𝒂𝒕𝒆 𝒆 𝟏 = (< >, { }) C Id Event Loan obs pos 1 𝑒1 Handle Request 800 1 1 1 𝑒2 Simple Check - 1 𝑒3 Decide - 2 𝑒4 Handle Request 1800 2 𝑒5 Ext. Check - 2 𝑒6 Decide - C Id Event Loan obs pos 1 𝑒1 Handle Request 800 1 1 1 𝑒2 Simple Check - 1 1 1 𝑒3 Decide - 2 𝑒4 Handle Request 1800 2 𝑒5 Ext. Check - 2 𝑒6 Decide -𝒐𝒃𝒔 𝑷 𝒆 𝟐 = { Simple Check } = 𝟏 𝒑𝒐𝒔 𝑷 𝒆 𝟐 = Simple Check = 𝟏 𝒔𝒕𝒂𝒕𝒆 𝒆 𝟐 = (< 𝐻 >, { 𝐿 = 800 }) C Id Event Loan obs pos 1 𝑒1 Handle Request 800 1 1 1 𝑒2 Simple Check - 1 1 1 𝑒3 Decide - 1 1 2 𝑒4 Handle Request 1800 2 𝑒5 Ext. Check - 2 𝑒6 Decide - 𝒐𝒃𝒔 𝑷 𝒆 πŸ‘ = { Decide } = 𝟏 𝒑𝒐𝒔 𝑷 𝒆 πŸ‘ = Decide = 𝟏 𝒔𝒕𝒂𝒕𝒆 𝒆 πŸ‘ = (< 𝐻, 𝑆 >, { 𝐿 = 800 }) C Id Event Loan obs pos 1 𝑒1 Handle Request 800 1 1 1 𝑒2 Simple Check - 1 1 1 𝑒3 Decide - 1 1 2 𝑒4 Handle Request 1800 1 1 2 𝑒5 Ext. Check - 2 𝑒6 Decide - 𝒐𝒃𝒔 𝑷 𝒆 πŸ’ = { Handle Request } = 𝟏 𝒑𝒐𝒔 𝑷 𝒆 πŸ’ = Handle Request = 𝟏 𝒔𝒕𝒂𝒕𝒆 𝒆 πŸ’ = (<>, { }) C Id Event Loan obs pos 1 𝑒1 Handle Request 800 1 1 1 𝑒2 Simple Check - 1 1 1 𝑒3 Decide - 1 1 2 𝑒4 Handle Request 1800 1 1 2 𝑒5 Ext. Check - 1 2 2 𝑒6 Decide -𝒐𝒃𝒔 𝑷 𝒆 πŸ“ = { Ext. Check } = 𝟏 𝒑𝒐𝒔 𝑷 𝒆 πŸ“ = Ext. Ceπ‘π‘˜, π‘†π‘–π‘šπ‘π‘™π‘’ πΆβ„Žπ‘’π‘π‘˜ = 𝟐 𝒔𝒕𝒂𝒕𝒆 𝒆 πŸ“ = (< 𝐻 >, { 𝐿 = 1800 }) C Id Event Loan 𝒐𝒃𝒔 𝑷 𝒑𝒐𝒔 𝑷 1 𝑒1 Handle Request 800 1 1 1 𝑒2 Simple Check - 1 1 1 𝑒3 Decide - 1 1 2 𝑒4 Handle Request 1800 1 1 2 𝑒5 Ext. Check - 1 2 2 𝑒6 Decide - 1 1 𝒐𝒃𝒔 𝑷 𝒆 πŸ” = Decide = 𝟏 𝒑𝒐𝒔 𝑷 𝒆 πŸ” = 𝐷𝑒𝑐𝑖𝑑𝑒 = 𝟏 𝒔𝒕𝒂𝒕𝒆 𝒆 πŸ” = (< 𝐻, 𝐸 >, { 𝐿 = 1800 }) C Id Event Loan 𝒐𝒃𝒔 𝑷 𝒑𝒐𝒔 𝑷 1 𝑒1 Handle Request 800 1 1 1 𝑒2 Simple Check - 1 1 1 𝑒3 Decide - 1 1 2 𝑒4 Handle Request 1800 1 1 2 𝑒5 Ext. Check - 1 2 2 𝑒6 Decide - 1 1 6 7 𝒐𝒃𝒔 𝑷 𝒆 = { observed activities at state } 𝒑𝒐𝒔 𝑷 𝒆 = | π‘π‘œπ‘ π‘ π‘–π‘π‘™π‘’ π‘Žπ‘π‘‘π‘–π‘£π‘–π‘‘π‘–π‘’π‘  π‘Žπ‘‘ π‘ π‘‘π‘Žπ‘‘π‘’ | 𝒔𝒕𝒂𝒕𝒆 𝒆 = π‘ π‘‘π‘Žπ‘‘π‘’ π‘œπ‘“ π‘‘β„Žπ‘’ π‘π‘Ÿπ‘œπ‘π‘’π‘ π‘  π‘šπ‘œπ‘‘π‘’π‘™ π‘π‘Ÿπ‘’π‘π‘–π‘ π‘–π‘œπ‘› 𝑃, 𝐿 = π‘’βˆˆπΏ π‘œπ‘π‘ π‘’π‘Ÿπ‘£π‘’π‘‘ 𝑃(𝑒) = 6 π‘’βˆˆπΏ π‘π‘œπ‘ π‘ π‘–π‘π‘™π‘’ 𝑃(𝑒) = 7 β‰ˆ 𝟎. πŸ–πŸ”
  6. 6. Full Example for Model A & Model B Department of Mathematics and Computer Science PAGE 5 / 8 A C Id Event Loan 𝒐𝒃𝒔 𝑷 𝒑𝒐𝒔 𝑷 1 𝑒1 Handle Request 800 1 1 1 𝑒2 Simple Check - 1 2 1 𝑒3 Decide - 1 1 2 𝑒4 Handle Request 1800 1 1 2 𝑒5 Extensive Check - 2 2 2 𝑒6 Decide - 1 1 3 𝑒7 Handle Request 1800 1 1 3 𝑒8 Simple Check - 2 2 3 𝑒9 Decide - 1 1 4 𝑒10 Handle Request 2500 1 1 4 𝑒11 Extensive Check - 1 2 4 𝑒12 Decide - 1 1 14 16 Model A π‘π‘Ÿπ‘’π‘π‘–π‘ π‘–π‘œπ‘› 𝑃, 𝐿 = π‘’βˆˆπΏ π‘œπ‘π‘ π‘’π‘Ÿπ‘£π‘’π‘‘ 𝑃(𝑒) = 14 π‘’βˆˆπΏ π‘π‘œπ‘ π‘ π‘–π‘π‘™π‘’ 𝑃(𝑒) = 16 β‰ˆ 𝟎. πŸ–πŸ•πŸ“ B C Id Event Loan 𝒐𝒃𝒔 𝑷 𝒑𝒐𝒔 𝑷 1 𝑒1 Handle Request 800 1 1 1 𝑒2 Simple Check - 1 1 1 𝑒3 Decide - 1 1 2 𝑒4 Handle Request 1800 1 1 2 𝑒5 Extensive Check - 2 2 2 𝑒6 Decide - 1 1 3 𝑒7 Handle Request 1800 1 1 3 𝑒8 Simple Check - 2 2 3 𝑒9 Decide - 1 1 4 𝑒10 Handle Request 2500 1 1 4 𝑒11 Extensive Check - 1 1 4 𝑒12 Decide - 1 1 14 14 Model B π‘π‘Ÿπ‘’π‘π‘–π‘ π‘–π‘œπ‘› 𝑃, 𝐿 = π‘’βˆˆπΏ π‘œπ‘π‘ π‘’π‘Ÿπ‘£π‘’π‘‘ 𝑃(𝑒) = 14 π‘’βˆˆπΏ π‘π‘œπ‘ π‘ π‘–π‘π‘™π‘’ 𝑃(𝑒) = 14 β‰ˆ 𝟏
  7. 7. Evaluation on Road Fines Log 0.30 0.36 0.64 0.83 0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0 Inductive Miner Inductive Miner & Rules Normative Model Normative Model & Rules ETC Precision Precision Fitness
  8. 8. Summary Department of Mathematics and Computer Science PAGE 7 / 8 β€’ 1st precision measure for multi-perspective process models βˆ’ Fast to calculate βˆ’ Flexible framework βˆ’ Implemented in ProM β€’ Preliminary Evaluation βˆ’ Illustrative examples βˆ’ Real-life dataset with > 500,000 events Handle 750 Simple Decide Handle 1250 Ext. Decide Handle 5000 Simple Decide Handle 750 Simple Decide Handle 1500 Simple Decide precision [0..1]
  9. 9. Department of Mathematics and Computer Science Image source: http://commons.wikimedia.org/wiki/File:Pictofigo_-_Idea.png Questions? Remarks? Ideas?

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