1. Profiling based unstructured process logs
Peter Khisa Wakholi
Supervisors: Prof Wil Van der Aalst – Eindhoven University
Prof Ddembe Williams – Makerere University
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2. Section 1
Introduction
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3. Process Mining Overview
Construction of models from event logs:
Process Model
Social networks
Organisational Model
Compare model with the event log and analyse discrepancies.
Audit and security
Extend model with a new aspect or perspective
Performance Characteristics
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4. Unstructured Processes
Browsing a website is an example of an unstructured process. Other examples
Patient flow in hospital
Customer care processes
Use of a machine
Unstructured process lack a definite structure or organization and are not formally organized or systematized during their execution.
The execution path depends on:
a set of factors that control the flow
attributes and interests of the actors
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5. The Problem
Construction of models from unstructured event logs is possible but interpretation is difficult.
There is a need to develop a better understanding for unstructured processes.
This understanding would help in;
Behavioural analysis to gain new insights on processes and actors
Predicting the execution path of incomplete processes
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6. Motivation
Profiling can help develop a better understanding of the underlying process models by.
Extracting meaningful process models from logs
Determining the rules that define the control flow for each case
Determining the attributes of the actors that influence observed behaviour.
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7. Section 2
Current Research
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8. Research Questions
How can complete and highly accurate profiles be developed from unstructured event log data?
What techniques that can be used to extract process related profiles based on event log data?
How can these techniques be deployed to develop a complete profile for unstructured processes?
What interpretation or meaning can be attributed to observed behavior in the profiles?
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9. Research Approach Experimentation Develop a concept
Experiment based on model generated event logs
Experiment on real logs
Develop a model, method, guidelines or framework
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10. Hypothesis of DFD for Profiling Process
Event Logs
Profiling Data
High level Petri net
Domain
Knowledge
Profile
Event Log File
Filtered Log File
Association rules
Filtering
Clustering & Filtering
Association Rule Mining
Intra Profile
Process Mining
PROM Analysis
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11. Profiling Hypothesis
Event Log File – This is a log of events for an unstructured business process. It is assumed that it contains process related data for extracting the model and case related data for developing profiles.
Clustering and Filtering – Real life logs contain a lot of noise. In addition, the underlying process models could be complex. The purpose of this stage will be to refine the logs through filtering and clustering based on some attributes. The current PROM plug-ins will be assessed for their appropriateness in profile generation.
Process Mining – The refined log is mined to discover the underlying process model, which is used as the basis for profile generation. This research will seek to identify appropriate Plug-ins for this task.
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12. Profiling Hypothesis ....
Association rule mining – This will pay a major part in generating profiles. The idea is to map every path in the process model with characteristics that define its users based on association rules. The first part of this study will focus on this.
PROM Analysis – We recognise that there are many PROM plug-ins that can be used to provide some profile related information. They will be analysed in order to determine how appropriate they are and to develop some guidelines for profiling.
Intra Profile – Association rules generated are only useful if they do not contradict themselves. This stage will seek to develop a mechanism to refine the rules by removing any contradictions.
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13. Profiling Hypothesis ....
Filtering – Knowledge of the domain under study. This knowledge should be used to ensure that the profile generated clearly reflects the expected behaviour patterns. A specific domain will be identified in order to illustrate the concept.
Profile – The expected output of all the processes explained above is a complete profile. The study will explore how this can be achieved.
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14. Association rule mining for profile generation
Section 3
Association rule mining for profile generation
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15. The Idea
For every path (arc between two places) of an unstructured process model
Develop a list of characteristics that defines attributes of actors that follow the path.
The profile of an actor is the list of attributes defined by the path followed.
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16. Approach Develop an algorithm to generate association rules.
Implement the algorithm in PROM.
Develop a model using CPN tools.
Analyse the results using the plug-in.
Refine the algorithm and idea till the results are satisfactory
Test the plug-in using real life logs
Refine the idea based on the results obtained.
Write a paper on the findings
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17. The Website Browse Model
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18. Discovered Process Model
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19. Association Rule Mining
Goal: Given an unstructured event log each of which contain some event log and data attributes from a given collection.
Develop a process model that defines control flow
For every segment in the model generate association rules
Express the segment as a sequence
Find the attributes of the actors that are associated with the segment
Develop a set of rules that govern the entire path for each case.
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20. Next Steps Develop a definitive and detailed algorithm
Develop a plug-in in PROM to test the algorithm
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