Multi-perspective Process Analysis:
Mining the Association between
Control Flow and Data Objects
Dina Bayomie, Kate Revoredo, Jan Mendling

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Introduction
Multi-
perspective
Control-
flow
Resource
Time
Data
objects
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Introduction
RQ: What is the relation between control-
flow perspective and data objects behavior
perspective?
We propose a multi-perspective mining
technique based on association rule mining
to discover the data connections between
the control-flow and data objects behavior.
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Event Log Rule Miner (EL-RM)

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Preparing the Event log
• Prepare the data to align with the process analysts analysis
objectives
Log partition-
time frame
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Encoding event log into transaction table
• A transaction table sustains the control-flow perspective and
the change behavior over the data perspective.
• A transaction represents the
behavior of the data attributes
over a pair of events.
Control-flow perspective Atomic perspective Complex perspective

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Encoding event log into transaction table

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Encoding event log into transaction table

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Association rule mining
𝐈𝐅 𝑒𝑖. 𝐴𝑐𝑡 = 𝑎 ∧ 𝑒𝑗. 𝐴𝑐𝑡 = 𝑏 𝐓𝐇𝐄𝐍 𝑒𝑖. 𝐴𝑡𝑡𝑟 ≶ 𝑒𝑗. 𝐴𝑡𝑡𝑟
≶: < | > | = | ≠

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Association rule mining

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Analysing the rules
Ranking
Combining
Comparing

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Analysing the rules - Ranking
 We rank the rules using the known measures of association rules.
𝑠𝑢𝑝𝑝𝑜𝑟𝑡 𝑅 =
|(𝑎𝑛𝑡𝑒𝑐𝑒𝑑𝑒𝑛𝑡 𝑐𝑜𝑛𝑠𝑒𝑞𝑢𝑒𝑛𝑡) ⊆ 𝑇 |
|𝑇|
where 𝑇 𝑖𝑠 𝑡ℎ𝑒 𝑡𝑟𝑎𝑛𝑠𝑎𝑐𝑡𝑖𝑜𝑛𝑠
𝑐𝑜𝑛𝑓𝑖𝑑𝑒𝑛𝑐𝑒 𝑅 =
𝑠𝑢𝑝𝑝𝑜𝑟𝑡(𝑅)
𝑠𝑢𝑝𝑝𝑜𝑟𝑡(𝑎𝑛𝑡𝑒𝑐𝑒𝑑𝑒𝑛𝑡)
𝑙𝑖𝑓𝑡 𝑅 =
𝑠𝑢𝑝𝑝𝑜𝑟𝑡(𝑅)
𝑠𝑢𝑝𝑝𝑜𝑟𝑡 𝑎𝑛𝑡𝑒𝑐𝑒𝑑𝑒𝑛𝑡 ∗ 𝑠𝑢𝑝𝑝𝑜𝑟𝑡(𝑐𝑜𝑛𝑠𝑒𝑞𝑢𝑒𝑛𝑡)

 In this step, We prepare the rules to align with the analysis objectives.
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Analysing the rules - combining

 EL-RM aggregates the rules based on common antecedent and common
consequent. We propose three possible aggregations.
1. The first option focuses on the control-flow perspective.
2. The second option focuses on the data object perspective.
3. The third option focuses on combine both the perspectives.
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Analysing the rules - combining

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Analysing the rules - compare
• we compare the rules generated from the different partitions to detect
the changes in behaviour over the divisions.
• We induce various sets of rules from the extracted rules by using the set
operations:
 All rules (Union set operation)
 Common rules (intersection set operation)
 Difference rules in a partition against Common rules (difference set operation)
 Partition unique rules (difference set operation)

 We conducted three exploratory experiments on three real datasets.
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Evaluation
 BPIC-2017 contains the events of the loan application
process of a Dutch financial institute. The events are
generated from three sub-processes, i.e., application,
offer and workflow.
 The log contains cases that started at the beginning
of 2016 until the 1st of February 2017.

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Experiment 1 – BPIC2017
Discovered rules = 751
Combine rules = 15
Confidence = [0.94,1]
Lift = [0.95, 13.63]
• R1: IF ei.Act = “OCreate Offer” and
ej.Act = “ OCreated”
THEN ei.EventID = ej.OfferID
• R2: IF ei.Act =”A_Complete” and
ej.Act = “ W_ValideApplication”
THEN ei.Resource = ej.Resource

 We conducted three exploratory experiments on three real datasets.
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Evaluation
 BPIC-2020 (prepaid travel) contains the events of
the prepaid travel request process at Eindhoven
University of Technology (TU/e).
 The log covers the cases from the beginning of
2017 till the 21st of February 2019.

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Experiment 2 – BPIC2020(prepaid travel)
All rules = 425
Common rules = 85
Confidence = [0.99, 1]
Lift = [1, 8.90]
• R1: IF ei.Act =“Permit APPROVED by
ADMINISTRATION” and ej.Act =
“Permit APPROVED by BUDGET
OWNER” THEN ei.Resource =
ej.Resource
• R2: IF ei.Act = “Permit APPROVED by
BUDGET OWNER” and ej.Act = “Permit
FINAL_APPROVED by SUPERVISOR”
THEN ei.org:role != ei.org:role

 We conducted three exploratory experiments on three real datasets.
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Evaluation
 Road traffic fine management process dataset
contains the events of the road traffic fines process.
The cases have a diverse cycle time duration
behavior. [mention duration of shortest and longest]
 The log covers the cases from the beginning of 2000
till the 18th of June 2013

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Experiment 3 – Road traffic fine
All rules = 239
Common rules = 159
Average confidence = 0.96
Average lift = 1.98
• R1: IF ei.Act =“Insert Fine Notification”
and ej.Act = “Insert Date Appeal to
Prefecture” THEN ei.NotifcationType !=
ej. NotifcationType
• R2: IF ei.Act =“Create Fine” and ej.Act =
“payment” THEN ei.NotifcationType =
ej. NotifcationType

 We proposed a multi-perspective mining technique for the discovery of data
connection.
 Our method uses association rules to represent the relation between the control
flow perspective and its impacts on the behavior of the data objects perspective
 The results of our evaluation showed the potential of the approach to extract
relevant insights about the change behavior of the attributes over
the events.
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Conclusion

Dina Bayomie
dbayomie@wu.ac.at
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