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![Understand, share, explain
Semantic DMN 17
Cer. Exp.
(date)
Length
(m)
Draft
(m)
Capacity
(TEU)
Cargo
(mg/cm2)
0 0 0 0 0
Enter
y, n
Ship Clearance
today
> today < 260 < 10 < 1000
> today < 260 < 10 1000
> today < 260 [10,12] < 4000 0.75
> today < 260 [10,12] < 4000 > 0.75
> today [260,320) (10,13] < 6000 0.5
> today [260,320) (10,13] < 6000 > 0.5
> today [320,400) 13 > 4000 0.25
> today [320,400) 13 > 4000 > 0.25
n
y
n
y
n
y
n
y
n
Table 1: DMN representation of the ship clearance decision of Figure 1b
Enter Length
(m)
Cargo
(mg/cm2)
y,n 0 0
Refuel Area
none, indoor, outdoor
U
Refuel area determination
n
y 350
y > 350 0.3
y > 350 > 0.3
none
indoor
indoor
outdoor
1
2
3
4
Ship
id-code
name
Certificate
exp-date
Harbor
location
Attempt
when
outcome
tried entering into
owns
1
0..1
* *
receive
entrance request
record
ship info
inspect ship
ship id
acquire
certificate
record
cargo
residuals
record
exp. date
cargo residuals
certificate exp. date
decice
clearance
enter
refuel area
enter?
send
refusal
send
fuel area info
open
dock
N
Y
ship type (short name)](https://image.slidesharecdn.com/fit-for-digital-2019-montali-191031155445/75/Processes-and-organizations-a-look-behind-the-paper-wall-8-2048.jpg)
![Understand, share, explain
receive
entrance request
record
ship info
inspect ship
ship id
acquire
certificate
record
cargo
residuals
record
exp. date
cargo residuals
certificate exp. date
decice
clearance
enter
refuel area
enter?
send
refusal
send
fuel area info
open
dock
N
Y
ship type (short name)
Semantic DMN 17
Cer. Exp.
(date)
Length
(m)
Draft
(m)
Capacity
(TEU)
Cargo
(mg/cm2)
0 0 0 0 0
Enter
y, n
Ship Clearance
today
> today < 260 < 10 < 1000
> today < 260 < 10 1000
> today < 260 [10,12] < 4000 0.75
> today < 260 [10,12] < 4000 > 0.75
> today [260,320) (10,13] < 6000 0.5
> today [260,320) (10,13] < 6000 > 0.5
> today [320,400) 13 > 4000 0.25
> today [320,400) 13 > 4000 > 0.25
n
y
n
y
n
y
n
y
n
Table 1: DMN representation of the ship clearance decision of Figure 1b
Enter Length
(m)
Cargo
(mg/cm2)
y,n 0 0
Refuel Area
none, indoor, outdoor
U
Refuel area determination
n
y 350
y > 350 0.3
y > 350 > 0.3
none
indoor
indoor
outdoor
1
2
3
4
Ship
id-code
name
Certificate
exp-date
Harbor
location
Attempt
when
outcome
tried entering into
owns
1
0..1
* *
internal members
(management, IT, domain experts)
external stakeholders
(customers, citizens, auditors)
digital agents
(applications, robots, …)](https://image.slidesharecdn.com/fit-for-digital-2019-montali-191031155445/75/Processes-and-organizations-a-look-behind-the-paper-wall-9-2048.jpg)
![Dutch Municipalities28 Ube van der Ham
[van der Ham, BPI Challenge 2015]](https://image.slidesharecdn.com/fit-for-digital-2019-montali-191031155445/75/Processes-and-organizations-a-look-behind-the-paper-wall-25-2048.jpg)
![Dutch Municipalities28 Ube van der Ham
Although the processes are centrally designed, several differences are visible that
make the difference in speed and objections very clear. We will focus on MuniC
versus MuniB and MuniD, because the difference in performance is most obvious.
Fig. 5 shows the control flow for the three municipalities in Disco.
[van der Ham, BPI Challenge 2015]](https://image.slidesharecdn.com/fit-for-digital-2019-montali-191031155445/75/Processes-and-organizations-a-look-behind-the-paper-wall-26-2048.jpg)
Processes and organizations - a look behind the paper wall
1) The document discusses business process management and how conceptual modeling and process mining can help understand and improve digital enterprises. 2) Process mining techniques like process discovery from event logs, decision mining, and social network mining can provide insights into how processes are executed in reality. 3) Replay techniques can enhance process models with timing information and detect deviations to help align actual behaviors with expected behaviors.
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Processes and organizations - a look behind the paper wall
- 1. Processes and Organizations Alook behind the paper wall Marco Montali :: Free University of Bozen-Bolzano Fit for Digital, 23/10/2019
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- 8. Understand, share, explain SemanticDMN 17 Cer. Exp. (date) Length (m) Draft (m) Capacity (TEU) Cargo (mg/cm2) 0 0 0 0 0 Enter y, n Ship Clearance today > today < 260 < 10 < 1000 > today < 260 < 10 1000 > today < 260 [10,12] < 4000 0.75 > today < 260 [10,12] < 4000 > 0.75 > today [260,320) (10,13] < 6000 0.5 > today [260,320) (10,13] < 6000 > 0.5 > today [320,400) 13 > 4000 0.25 > today [320,400) 13 > 4000 > 0.25 n y n y n y n y n Table 1: DMN representation of the ship clearance decision of Figure 1b Enter Length (m) Cargo (mg/cm2) y,n 0 0 Refuel Area none, indoor, outdoor U Refuel area determination n y 350 y > 350 0.3 y > 350 > 0.3 none indoor indoor outdoor 1 2 3 4 Ship id-code name Certificate exp-date Harbor location Attempt when outcome tried entering into owns 1 0..1 * * receive entrance request record ship info inspect ship ship id acquire certificate record cargo residuals record exp. date cargo residuals certificate exp. date decice clearance enter refuel area enter? send refusal send fuel area info open dock N Y ship type (short name)
- 9. Understand, share, explain receive entrancerequest record ship info inspect ship ship id acquire certificate record cargo residuals record exp. date cargo residuals certificate exp. date decice clearance enter refuel area enter? send refusal send fuel area info open dock N Y ship type (short name) Semantic DMN 17 Cer. Exp. (date) Length (m) Draft (m) Capacity (TEU) Cargo (mg/cm2) 0 0 0 0 0 Enter y, n Ship Clearance today > today < 260 < 10 < 1000 > today < 260 < 10 1000 > today < 260 [10,12] < 4000 0.75 > today < 260 [10,12] < 4000 > 0.75 > today [260,320) (10,13] < 6000 0.5 > today [260,320) (10,13] < 6000 > 0.5 > today [320,400) 13 > 4000 0.25 > today [320,400) 13 > 4000 > 0.25 n y n y n y n y n Table 1: DMN representation of the ship clearance decision of Figure 1b Enter Length (m) Cargo (mg/cm2) y,n 0 0 Refuel Area none, indoor, outdoor U Refuel area determination n y 350 y > 350 0.3 y > 350 > 0.3 none indoor indoor outdoor 1 2 3 4 Ship id-code name Certificate exp-date Harbor location Attempt when outcome tried entering into owns 1 0..1 * * internal members (management, IT, domain experts) external stakeholders (customers, citizens, auditors) digital agents (applications, robots, …)
- 10. Machines can usemodels… receive entrance request record ship info inspect ship ship id acquire certificate record cargo residuals record exp. date cargo residuals certificate exp. date decice clearance enter refuel area enter? send refusal send fuel area info open dock N Y ship type (short name) for automated analysis for execution support …
- 11. Do models reflectreality? (Does management understand what is going on?)
- 12. Do models reflectreality? (Does management understand what is going on?)
- 13. Do models reflectreality? (Does management understand what is going on?)
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- 17. Going digital inreality event log completeness quality integration
- 18. A sea ofevent data tracing the evolution of process instances…
- 19. … to computeindicators
- 20. … to computeindicators Why is reality so?
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- 22. Process Mininging eventsto model ntial for process mining process model Replay: elemen event log Play-In Play-Out insights
- 23. Replay: Connecting eventsto elements is essential for proc event log process model Play-In event logprocess model Play-Out Replay • extended model showing times, elements is essential for pro event log process model Play-In event logprocess model Play-Out Replay • extended model showing times, frequencies, etc. • diagnostics • predictions event log process model Play-In event logprocess model Play-Out event log process model Replay • extended model showing times, frequencies, etc. • diagnostics • predictions • recommendations Play in Play out Replay Process Mining
- 24. Play in: processdiscovery Executed process
- 25. Dutch Municipalities28 Ubevan der Ham [van der Ham, BPI Challenge 2015]
- 26. Dutch Municipalities28 Ubevan der Ham Although the processes are centrally designed, several differences are visible that make the difference in speed and objections very clear. We will focus on MuniC versus MuniB and MuniD, because the difference in performance is most obvious. Fig. 5 shows the control flow for the three municipalities in Disco. [van der Ham, BPI Challenge 2015]
- 27. Play in: notjust control-flow… Decision mining: how do people route process instances?
- 28. Play in: notjust control-flow… Social network mining: “real” organizational structure (handover of work)
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- 30. Replay: enhancement Although theprocesses are centrally designed, several differences are visible that make the difference in speed and objections very clear. We will focus on MuniC versus MuniB and MuniD, because the difference in performance is most obvious. Fig. 5 shows the control flow for the three municipalities in Disco.
- 31. Replay: enhancement Although theprocesses are centrally designed, several differences are visible that make the difference in speed and objections very clear. We will focus on MuniC versus MuniB and MuniD, because the difference in performance is most obvious. Fig. 5 shows the control flow for the three municipalities in Disco. MuniC is somewhat slower. The still from the animation (Fig. 6) shows not only the bottleneck before BB_5 in MuniB and MuniD, but also the more regular distributed cases over the rest of the steps in MuniC.
- 32. Replay: conformance checking Detectdeviations and align the actual with the expected behaviors
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- 35. Runtime operational support Predictivemonitoring: what will likely happen to my process instance?
- 36. This is forus to take, and manage wisely Not to control… …but to continuously improve!
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