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.

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. Precision
Department of Mathematics and Computer Science PAGE 1 / 8
“Flower Model” lacking any precision
B
C
A

3. Precision of Multi-perspective Process Models
Department of Mathematics and Computer Science PAGE 2 / 8
A 𝑝𝑟𝑒𝑐𝑖𝑠𝑖𝑜𝑛 𝐴
B
𝑝𝑟𝑒𝑐𝑖𝑠𝑖𝑜𝑛 𝐵
𝑝𝑟𝑒𝑐𝑖𝑠𝑖𝑜𝑛 𝐵 > 𝑝𝑟𝑒𝑐𝑖𝑠𝑖𝑜𝑛 𝐴
Existing work ignores added precision
by multi-perspective rules / constraints

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 ∈𝑳
𝒐𝒃𝒔𝒆𝒓𝒗𝒆𝒅 𝑷 (𝒆)
𝒑𝒓𝒆𝒄𝒊𝒔𝒊𝒐𝒏(𝑷, 𝑳) =
𝒆 ∈𝑳
𝒑𝒐𝒔𝒔𝒊𝒃𝒍𝒆 𝑷(𝒆)

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 𝟑 = { Decide } = 𝟏
𝒑𝒐𝒔 𝑷 𝒆 𝟑 = Decide = 𝟏
𝒔𝒕𝒂𝒕𝒆 𝒆 𝟑 = (< 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 𝟒 = { Handle Request } = 𝟏
𝒑𝒐𝒔 𝑷 𝒆 𝟒 = Handle Request = 𝟏
𝒔𝒕𝒂𝒕𝒆 𝒆 𝟒 = (<>, { })
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 𝟓 = { Ext. Check } = 𝟏
𝒑𝒐𝒔 𝑷 𝒆 𝟓 = Ext. Ce𝑐𝑘, 𝑆𝑖𝑚𝑝𝑙𝑒 𝐶ℎ𝑒𝑐𝑘 = 𝟐
𝒔𝒕𝒂𝒕𝒆 𝒆 𝟓 = (< 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 𝟔 = Decide = 𝟏
𝒑𝒐𝒔 𝑷 𝒆 𝟔 = 𝐷𝑒𝑐𝑖𝑑𝑒 = 𝟏
𝒔𝒕𝒂𝒕𝒆 𝒆 𝟔 = (< 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 = | 𝑝𝑜𝑠𝑠𝑖𝑏𝑙𝑒 𝑎𝑐𝑡𝑖𝑣𝑖𝑡𝑖𝑒𝑠 𝑎𝑡 𝑠𝑡𝑎𝑡𝑒 |
𝒔𝒕𝒂𝒕𝒆 𝒆 = 𝑠𝑡𝑎𝑡𝑒 𝑜𝑓 𝑡ℎ𝑒 𝑝𝑟𝑜𝑐𝑒𝑠𝑠 𝑚𝑜𝑑𝑒𝑙
𝑝𝑟𝑒𝑐𝑖𝑠𝑖𝑜𝑛 𝑃, 𝐿 =
𝑒∈𝐿 𝑜𝑏𝑠𝑒𝑟𝑣𝑒𝑑 𝑃(𝑒) = 6
𝑒∈𝐿 𝑝𝑜𝑠𝑠𝑖𝑏𝑙𝑒 𝑃(𝑒) = 7
≈ 𝟎. 𝟖𝟔

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
𝑝𝑟𝑒𝑐𝑖𝑠𝑖𝑜𝑛 𝑃, 𝐿 =
𝑒∈𝐿 𝑜𝑏𝑠𝑒𝑟𝑣𝑒𝑑 𝑃(𝑒) = 14
𝑒∈𝐿 𝑝𝑜𝑠𝑠𝑖𝑏𝑙𝑒 𝑃(𝑒) = 16
≈ 𝟎. 𝟖𝟕𝟓
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
𝑝𝑟𝑒𝑐𝑖𝑠𝑖𝑜𝑛 𝑃, 𝐿 =
𝑒∈𝐿 𝑜𝑏𝑠𝑒𝑟𝑣𝑒𝑑 𝑃(𝑒) = 14
𝑒∈𝐿 𝑝𝑜𝑠𝑠𝑖𝑏𝑙𝑒 𝑃(𝑒) = 14
≈ 𝟏

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. Summary
Department of Mathematics and Computer Science PAGE 7 / 8
• 1st precision measure for
multi-perspective process models
− Fast to calculate
− Flexible framework
− Implemented in ProM
• Preliminary Evaluation
− Illustrative examples
− 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. Department of Mathematics and Computer Science
Image source: http://commons.wikimedia.org/wiki/File:Pictofigo_-_Idea.png
Questions? Remarks? Ideas?