Post on 16-Apr-2017
Measuring the Precision ofMulti-perspective Process Models
Felix Mannhardt joint work withMassimiliano de Leoni, Hajo A. Reijers,Wil M.P. van der Aalst
Department of Mathematics and Computer Science
Precision
PAGE 2 / 8
“Flower Model” lacking any precision
B
C
A
Department of Mathematics and Computer Science
Precision of Multi-perspective Process Models
SimpleCheck
ExtensiveCheck
Handle Request Decide
Loan
PAGE 3 / 8
A 𝑝𝑟𝑒𝑐𝑖𝑠𝑖𝑜𝑛𝐴
SimpleCheck
ExtensiveCheck
Handle Request Decide
Loan
Loan < 2,000
Loan > 1,000
B𝑝𝑟𝑒𝑐𝑖𝑠𝑖𝑜𝑛𝐵
𝑝𝑟𝑒𝑐𝑖𝑠𝑖𝑜𝑛𝐵>𝑝𝑟𝑒𝑐𝑖𝑠𝑖𝑜𝑛𝐴
Existing work ignores added precision by multi-perspective rules / constraints
Department of Mathematics and Computer Science
Approach: Multi-perspective Precision
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Multi-perspective Process Model (P)
Fitting EventLog (L)
Precision[0..1]
INPUT OUTPUT
APPROACH
∑𝒆∈𝑳
𝒐𝒃𝒔 𝒆𝒓𝒗𝒆𝒅𝑷 (𝒆)𝒑𝒓𝒆𝒄𝒊𝒔𝒊𝒐𝒏 (𝑷 ,𝑳)=¿ ∑
𝒆∈𝑳𝒑𝒐𝒔𝒔𝒊𝒃𝒍𝒆𝑷 (𝒆)
Department of Mathematics and Computer Science
SimpleCheck
ExtensiveCheck
Handle Request Decide
Loan
Loan < 2,000
Loan > 1,000
SimpleCheck
ExtensiveCheck
Handle Request Decide
Loan
Loan < 2,000
Loan > 1,000
Precision: Observed / Possible Behavior
PAGE 5 / 8
SimpleCheck
ExtensiveCheck
Handle Request Decide
Loan
Loan < 2,000
Loan > 1,000C Id Event Loan obs pos1 Handle Request 800
1 Simple Check -
1 Decide -
2 Handle Request 1800
2 Ext. Check -
2 Decide -
SimpleCheck
ExtensiveCheck
Handle Request Decide
Loan
Loan < 2,000
Loan > 1,000
𝒐𝒃𝒔 𝑷 (𝒆𝟏 )=|{Handle Request }|=𝟏𝒑𝒐𝒔 𝑷 (𝒆𝟏 )=|{Handle Request }|=𝟏
𝒔𝒕𝒂𝒕𝒆 (𝒆𝟏 )=(¿>, {})
C Id Event Loan obs pos1 Handle Request 800 1 11 Simple Check -
1 Decide -
2 Handle Request 1800
2 Ext. Check -
2 Decide -
C Id Event Loan obs pos1 Handle Request 800 1 1
1 Simple Check - 1 11 Decide -
2 Handle Request 1800
2 Ext. Check -
2 Decide -𝒐𝒃𝒔 𝑷 (𝒆𝟐 )=|{Simple }̌|=𝟏𝒑𝒐𝒔 𝑷 (𝒆𝟐 )=¿
𝒔𝒕𝒂𝒕𝒆 (𝒆𝟐 )=(¿𝐻>, {𝐿=800 })
SimpleCheck
ExtensiveCheck
Handle Request Decide
Loan
Loan < 2,000
Loan > 1,000C Id Event Loan obs pos1 Handle Request 800 1 1
1 Simple Check - 1 1
1 Decide - 1 12 Handle Request 1800
2 Ext. Check -
2 Decide -𝒐𝒃𝒔 𝑷 (𝒆𝟑 )=|{Decide }|=𝟏𝒑𝒐𝒔 𝑷 (𝒆𝟑 )=|{Decide }|=𝟏
𝒔𝒕𝒂𝒕𝒆 (𝒆𝟑 )=(¿𝐻 ,𝑆>, {𝐿=800 })
SimpleCheck
ExtensiveCheck
Handle Request Decide
Loan
Loan < 2,000
Loan > 1,000C Id Event Loan obs pos1 Handle Request 800 1 1
1 Simple Check - 1 1
1 Decide - 1 1
2 Handle Request 1800 1 12 Ext. Check -
2 Decide -𝒐𝒃𝒔 𝑷 (𝒆𝟒 )=|{Handle Request }|=𝟏𝒑𝒐𝒔 𝑷 (𝒆𝟒 )=|{Handle Request }|=𝟏
𝒔𝒕𝒂𝒕𝒆 (𝒆𝟒 )=(¿ , {})
SimpleCheck
ExtensiveCheck
Handle Request Decide
Loan
Loan < 2,000
Loan > 1,000C Id Event Loan obs pos1 Handle Request 800 1 1
1 Simple Check - 1 1
1 Decide - 1 1
2 Handle Request 1800 1 1
2 Ext. Check - 1 22 Decide -𝒐𝒃𝒔 𝑷 (𝒆𝟓 )=|{Ext . }̌|=𝟏
𝒑𝒐𝒔 𝑷 (𝒆𝟓 )=|{Ext .Ce𝑐𝑘 ,𝑆𝑖𝑚𝑝𝑙𝑒 h𝐶 𝑒𝑐𝑘}|=𝟐
𝒔𝒕𝒂𝒕𝒆 (𝒆𝟓 )=(¿𝐻>, {𝐿=1800 })
C Id Event Loan1 Handle Request 800 1 1
1 Simple Check - 1 1
1 Decide - 1 1
2 Handle Request 1800 1 1
2 Ext. Check - 1 2
2 Decide - 1 1 𝒑𝒐𝒔 𝑷 (𝒆𝟔 )=|{𝐷𝑒𝑐𝑖𝑑𝑒 }|=𝟏
𝒔𝒕𝒂𝒕𝒆 (𝒆𝟔 )=(¿𝐻 ,𝐸>, {𝐿=1800 })
SimpleCheck
ExtensiveCheck
Handle Request Decide
Loan
Loan < 2,000
Loan > 1,000C Id Event Loan1 Handle Request 800 1 1
1 Simple Check - 1 1
1 Decide - 1 1
2 Handle Request 1800 1 1
2 Ext. Check - 1 2
2 Decide - 1 1
6 7
𝒐𝒃𝒔 𝑷 (𝒆 )=|{observed activities at state }|𝒑𝒐𝒔 𝑷 (𝒆 )=¿ {𝑝𝑜𝑠𝑠𝑖𝑏𝑙𝑒𝑎𝑐𝑡𝑖𝑣𝑖𝑡𝑖𝑒𝑠 𝑎𝑡 𝑠𝑡𝑎𝑡𝑒 }∨¿
𝒔𝒕𝒂𝒕𝒆 (𝒆 )=𝑠𝑡𝑎𝑡𝑒𝑜𝑓 h𝑡 𝑒𝑝𝑟𝑜𝑐𝑒𝑠𝑠𝑚𝑜𝑑𝑒𝑙
Department of Mathematics and Computer Science
Full Example for Model A & Model B
PAGE 6 / 8
SimpleCheck
ExtensiveCheck
Handle Request Decide
Loan
AC Id Event Loan1 Handle Request 800 1 1
1 Simple Check - 1 2
1 Decide - 1 1
2 Handle Request 1800 1 1
2 Extensive Check - 2 2
2 Decide - 1 1
3 Handle Request 1800 1 1
3 Simple Check - 2 2
3 Decide - 1 1
4 Handle Request 2500 1 1
4 Extensive Check - 1 2
4 Decide - 1 1
14 16
Model A
𝑝𝑟𝑒𝑐𝑖𝑠𝑖𝑜𝑛 (𝑃 ,𝐿 )=∑𝑒∈ 𝐿
𝑜𝑏𝑠𝑒𝑟𝑣𝑒𝑑𝑃(𝑒)=14
∑𝑒∈𝐿
𝑝𝑜𝑠𝑠𝑖𝑏𝑙𝑒𝑃 (𝑒)=16 ≈𝟎 .𝟖𝟕𝟓
SimpleCheck
ExtensiveCheck
Handle Request Decide
Loan
Loan < 2,000
Loan > 1,000B
C Id Event Loan1 Handle Request 800 1 1
1 Simple Check - 1 11 Decide - 1 1
2 Handle Request 1800 1 1
2 Extensive Check - 2 2
2 Decide - 1 1
3 Handle Request 1800 1 1
3 Simple Check - 2 2
3 Decide - 1 1
4 Handle Request 2500 1 1
4 Extensive Check - 1 14 Decide - 1 1
14 14
Model B
𝑝𝑟𝑒𝑐𝑖𝑠𝑖𝑜𝑛 (𝑃 ,𝐿 )=∑𝑒∈ 𝐿
𝑜𝑏𝑠𝑒𝑟𝑣𝑒𝑑𝑃(𝑒)=14
∑𝑒∈ 𝐿
𝑝𝑜𝑠𝑠𝑖𝑏𝑙𝑒𝑃(𝑒)=14 ≈𝟏
Evaluation on Road Fines Log
Inductive Miner Inductive Miner & Rules
Normative Model Normative Model & Rules
0.00.10.20.30.40.50.60.70.80.91.0
0.300.36
0.64
0.83
ETC Precision Precision Fitness
Department of Mathematics and Computer Science
Summary
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• 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
Handle750 Simple Decide
Handle1250 Ext. Decide
Handle5000 Simple Decide
Handle
750Simple Decide
Handle
1500Simpl
eDecid
e
SimpleCheck
ExtensiveCheck
Handle Request Decide
Loan
Loan < 2,000
Loan > 1,000
precision [0..1]
Department of Mathematics and Computer ScienceImage source: http://commons.wikimedia.org/wiki/File:Pictofigo_-_Idea.png
Questions? Remarks? Ideas?