Object-Centric Processes - from cases to objects and relations… and beyond

Data Science > Multi-Dimensional Process Analytics
Dr. Dirk Fahland
d.fahland@tue.nl / @dfahland
From Cases to Objects and Relations … and Beyond

2
Object-Centric Process Mining, The next frontier in business performance
Prof. dr. ir. Wil van der Aalst Chief Scientist, Celonis, 2023

System
System
3
Real World - Data - Models
“Real World”
Model
System
describes
supports &
controls
records
Event
Data
analyze
Process
or prescribes

System
System
4
“Real World”
Model
System
describes
supports &
controls
records
Event
Data
analyze
or prescribes
not just
one process

System
System
5
“Real World”
Model
System
describes
supports &
controls
records
Event
Data
analyze
Processes
Actors
Organizations
Machines
or prescribes
Documents
Items

System
System
System
6
Processes
Actors
Organizations
Machines
Users
“Real World”
Model
describes
records
actionable means of communication
between actors (or workers), stakeholders, managers, and machines
relate own goals and
actions to thoseof
other participants
supports &
controls
or prescribes
Event
Data

7
How did we arrive here? What makes it special?
Object-Centric ProcessMining, The next frontier in business performance
Prof. dr. ir. Wil van der Aalst Chief Scientist, Celonis, 2023
Dirk Fahland: ProcessMining over Multiple BehavioralDimensions with Event
Knowledge Graphs. ProcessMining Handbook2022: 274-319

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From Cases to Object and Relations … and Beyond
Cases vs Objects Objects
…and Relations
…and Beyond
1 2 3

9
Multi-Dimensional Dynamics
1
2
1
2
1
complex network of
processes &
process instances

Execution = Case
10
events in
relational
DB
O1
O2
D5
D6

Execution = Case
11
events in
relational
DB
O1
O2
D5
D6
extract log for case id: O1
1 1
5 5 6 1
6
time

Execution = Case
12
events in
relational
DB
O1
O2
D5
D6
1 1
5 5 6 1
time
6
extract log for case id: O2
5 5 6 6
2 2 2

Execution = Case → Convergence & Divergence
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events in
relational
DB
single-dimensional
discovery
edges: 29 correct, 48 wrong
2 months SAP data
O1
O2
D5
D6
1 1
5 5 6 1
time
6
single case id → de-normalizes underlying data-structure
5 5 6 6
2 2 2
duplicates
events
(convergence)
false
dependencies
(divergence)

Interacting Cases - M:N
order1
order2
delivery5
delivery6
4 cases
order delivery
+
*
*
1st package 2nd package
+
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Proclets: Messages + Cardinality Constraints
load
deliver next
finish
retry
delivery tour
undeliv.
create
split
notify
bill
order
+
*
1
*
*
1 1
*
15
order1
order2
delivery5 delivery6
Wil M. P. van der Aalst, Paulo Barthelmess, Clarence
A. Ellis, Jacques Wainer: Workflow Modeling Using
Proclets. CoopIS 2000: 198-209

Artifact-Centric Process Mining
extract log
per object
discover
identify & add
interactions
merge
related logs
Xixi Lu, Marijn Nagelkerke, Dirk Fahland: “Discovering Interacting Artifacts from ERP systems” IEEE Trans. On Services Computing DOI: 10.1109/TSC.2015.2474358 (2015)
artifact-centric data model
objects
activities
relations

17
… in Practice
→ relations as first-class citizens are too complicated.

18
Some advice from C.A. Petri
→ view the world through synchronizing objects only

Reify Data Model → Normal Form
pack load
retry
deliver
undeliv
bill
package
19
load
deliver next
finish
retry
delivery tour
undeliv.
create
split
notify
bill
order
+
*
1
*
*
1 1
*
order delivery
+
1 + 1
package
Dirk Fahland, Massimiliano de Leoni, Boudewijn F. van Dongen, Wil M. P. van derAalst:
Many-to-Many: Some Observations on Interactions in Artifact Choreographies.ZEUS 2011:9-15

Normal Form for Process-Object Interactions (1:n)
create
pack
notify
bill
order
load
deliver
undeliv.
return
delivery
next
retry
pack load
retry
deliver
undeliv
bill
package
1 +
1 instance of order can
create 1..n packages
1
+
1
1
1
1
1
1
+
1
20
Dirk Fahland, Massimiliano de Leoni, Boudewijn F. van Dongen, Wil M. P. van derAalst:
Many-to-Many: Some Observations on Interactions in Artifact Choreographies.ZEUS 2011:9-15

21
Fuse Transitions of Objects → OC-Petri Nets
Wil M. P. van der Aalst, Alessandro Berti:
Discovering Object-centric Petri Nets. Fundam. Informaticae 175(1-4): 1-40 (2020)

Each object has its path + synchronization
order1
order2
delivery5
delivery6
1st package
3rd package
2nd package
pack
load deliver
bill
pack
retry load deliver undeliv.
bill
1 order
N packages
22

How do we get event data into this shape?
order1
order2
delivery5
delivery6
1st package
3rd package
2nd package
pack
load deliver
bill
pack
retry load deliver undeliv.
bill
23

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Cases vs Objects:
Theory and Practice
Objects
…and Relations
…and Beyond
1 2 3

25
An Example Process
“Real
World”

26
as “Paths per object + synchronization”
Event
Data
Event Table with Entity Types
Object-Centric Event Log

27
Event
Data
“O2 related to I2”
recorded in the event

28
Event
Data
1:n relation

29
Event
Data

30
Event
Data

Object-Centric Process Discovery
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per object (column)
• extract log
• discover model

32
per object (column)
• extract log
• discover model

33
per object (column)
• extract log
• discover model
per activity
• extract interactions
• synchronize transitions

34

35

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Multi-object DFG:
distinguishes choice
from parallelism

Object-Centric Process Mining
extract log
per object
discover
synchronize
activities
Xixi Lu, Marijn Nagelkerke, Dirk Fahland: “Discovering Interacting Artifacts from ERP systems” IEEE Trans. On Services Computing DOI: 10.1109/TSC.2015.2474358 (2015)
objects
activities  relations

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…enables better process analysis
Jan Niklas Adams, Wil M. P. van der Aalst: OCπ: Object-Centric Process Insights. Petri Nets 2022: 139-150

40
…enables better process analysis
Gyunam Park, Jan Niklas Adams, Wil M. P. van der Aalst: OPerA: Object-Centric Performance Analysis. ER 2022: 281-292

41
Cases vs Objects:
Theory and Practice
Objects
…and Relations
…and Beyond
1 2 3
under the hood

43
Entity ID → Entity Node
entity: anything that is
identifiable + tracks over time

47
Infer directly-follows
All events correlated to
entity I1:
• order by time:
e1,e2,...,en
• add df-edge
from ei toei+1
• remember that df-
edge holds for I1

48
Infer directly-follows → Event Knowledge Graph
Approaches differ:
concept model vs
storage model

49
EKG: Object-Centric Log as a Graph / Partial Order

50
Process Discovery = Aggregate Events → Activities
50
extend the Event Knowledge Graph
with a “process layer”

51
Process Discovery = Aggregate Events → Activities
51
extend the Event Knowledge Graph
with a “process layer”

52
Implementation: neo4j + Cypher Queries
https://github.com/multi-dimensional-process-mining/eventgraph_tutorial

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Almost all BPIC datasets are object-centric!
ProMG Python library: https://github.com/PromG-dev/
BPIC’14, BPIC’15, BPIC’16, BPIC’17, BPIC’19
alternative: OCEL → OCPA Python library https://github.com/ocpm/ocpa/
refine
OCED

54
Cases vs Objects:
Theory and Practice
Objects
…and Relations
…and Beyond
1 2 3
under the hood
Inspiration for your research
and your analyses!

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Adding the Actor perspective
• Actor as entity
• Infer directly-
follows

• Actor as entity
• Infer directly-
follows

57
Actor Behavior → “Process Fabric”
Loan Application Process
BPIC’17
cases are waiting for
actors/resources!
→ we need to change
the way measure
bottlenecks!

• Actor as entity
• Infer directly-
follows
• Identify “task
executions”
Task execution
Actor performs
multiple consecutive
actions on same object
Klijn, E.L., Mannhardt, F., Fahland, D., Classifying and
Detecting Task Executions and Routines in EventGraphs. BPM
Forum, pp. 212-229, 2022

Forum, pp. 212-229, 2022

R4 repeats the same
complex task in different
cases → batch processing
Forum, pp. 212-229, 2022

R2 repeats the same
complex task in different
cases → batch processing
but is interrupted
Forum, pp. 212-229, 2022

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Actor Behavior: Complex Patterns
O O O C
O O O
O O
O O
O O O C
O
O O O C
O
O O O O O O C
O O
O O O C
O
How to analyze? → talk by Bianka Bakullari “The Interplay Between High-
Level Problems and the Process Instances that Give Rise to Them” (Wed)

63
Cluster similar “Task Executions”
local model of similar task executions
→ local model of actor behavior
Eva L. Klijn, Felix Mannhardt, Dirk Fahland: Aggregating EventKnowledgeGraphs
for Task Analysis. ICPMWorkshops 2022: 493-505

64
Cluster similar “Task Executions”
Aggregate into Task nodes
C
C
C
C
C
C
end
C
C
C
C
C
C
C
C
C
C C
start
C
local model of similar task executions
→ local model of actor behavior
Eva L. Klijn, Felix Mannhardt, Dirk Fahland: Aggregating EventKnowledgeGraphs
for Task Analysis. ICPMWorkshops 2022: 493-505
global model of
actor interactions
Drift in actor behavior

65
BPIC’14: ITIL → 4 Processes Synchronizing on 1 Object
BPIC 14 – ITIL Service Process
Incident
Activity
Incident
Change
Interaction

66
BPIC’14 – ITIL “process fabric”
changes
over
service components

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Adding the System Perspective
Process objects (object-centricPM)
System entities

69
Process + Context → Inference
add system context of event to EKG
Use to infer missing information
(e.g., which object an event must have worked on)
→ Talk by Ava Swevels “Inferring Missing Entity Identifiers from Context Using Event
Knowledge Graphs” (Wed)
Case study in semi-conductor manufacturing

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Meta-Model for Object-Centric Event Data

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Relations between Objects
ERP data
• object relations
• event updates one object

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Relations between Objects
parent
How to calculate?
→ Dirk Fahland: Process Mining over Multiple
Behavioral DimensionswithEvent Knowledge
Graphs.Process Mining Handbook 2022: 274-319
Case Study in Auditing

73
Relations between Objects → DF-paths between objects
parent
How to calculate?

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Relations between Objects → DF-paths between objects
receives
How to calculate?

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Changes to Relations
Case Study: configuration management in manufacturing of complex
high-tech systems → effective in analyzing change management process
part-of

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Changes to Attributes
Road-Traffic Fines Management Process event log

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Multi-Dimensional Process Thinking
process analysis over
process definitionover
one object
multiple dynamics:
all flows together
+ system usage
+ resources
+….
one dynamic:
one flow
“process
fabric”
classical
processes
“system
fabric”
Object-Centric
“execution
fabric”
multiple
objects

78
Multi-Dimensional Process Thinking – Research Challenges
one object
multiple dynamics:
all flows together
+ system usage
+ resources
+….
one dynamic:
one flow
“process
fabric”
classical
processes
“system
fabric”
Object-Centric
“execution
fabric”
multiple
objects
In each quadrant
• the data is available
• OCEL
• EKGs
• the research questions are
• discovery
• descriptive/predictive
(simulation models)
• local/global
• conformance
• enhancement
• extend with domain
knowledge
• Inference
• changes to data over time

79
Multi-Dimensional Process Thinking – Research Challenges
one object
multiple dynamics:
all flows together
+ system usage
+ resources
+….
one dynamic:
one flow
“process
fabric”
classical
processes
“system
fabric”
Object-Centric
“execution
fabric”
multiple
objects
you can start researching today:
→OCPA
https://github.com/ocpm/ocpa/
→PromG (new)
https://github.com/PromG-dev
• Python library to
• automatically constructEKGs
→ for several BPIC data sets
• script analyses
In each quadrant
• the data is available
• OCEL
• EKGs

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