This (scientific) presentation puts forward the idea of using event structures as a unifying foundation to handle the problems of variants analysis and conformance analysis in process mining. In a nutshell, whether one is comparing two event logs (each capturing a different business process variant, aka "variants analysis") or an event log capturing the actual process behavior and a normative process model (aka "conformance analysis"), event structures can be derived from event logs and from process models and compared against each other. The result is a set of statements, in natural language, as well as visualizations overlaid on a BPMN model, describing the differences between the two logs or between a log and a model. Long story short, using this approach the results can be interpreted by an end user (say a business analyst), as opposed to state-of-the-art techniques for variants analysis and conformance analysis.
Deep Learning Models for Question AnsweringSujit Pal
Talk about a hobby project to apply Deep Learning models to predict answers to 8th grade science multiple choice questions for the Allen AI challenge on Kaggle.
Incremental and Interactive Process Model RepairMarlon Dumas
Paper presentation delivered by Abel Armas-Cervantes at the International Conference on Cooperative Information Systems (CoopIS), on 26 October 2018. The paper presents a technique to repair a given BPMN process model in such a way that it better first a given event log extracted from an information system. The paper is available at http://kodu.ut.ee/~dumas/pubs/coopis2017repair.pdf
This process model repair technique is implemented in the Apromore Business Process Analytics platform -- http://apromore.org
Efficient Process Model Discovery Using Maximal Pattern MiningDr. Sira Yongchareon
In recent years, process mining has become one of the most important and promising areas of research in the field of business process management as it helps businesses understand, analyze, and improve their business processes. In particular, several proposed techniques and algorithms have been proposed to discover and construct process models from workflow execution logs (i.e., event logs). With the existing techniques, mined models can be built based on analyzing the relationship between any two events seen in event logs. Being restricted by that, they can only handle special cases of routing constructs and often produce unsound models that do not cover all of the traces seen in the log. In this paper, we propose a novel technique for process discovery using Maximal Pattern Mining (MPM) where we construct patterns based on the whole sequence of events seen on the traces—ensuring the soundness of the mined models. Our MPM technique can handle loops (of any length), duplicate tasks, non-free choice constructs, and long distance dependencies. Our evaluation shows that it consistently achieves better precision, replay fitness and efficiency than the existing techniques.
Scalable Conformance Checking of Business ProcessesMarlon Dumas
Paper presentation delivered by Abel Armas-Cervantes at the International Conference on Cooperative Information Systems (CoopIS), on 26 October 2018. The paper presents a technique to compare a BPMN process model and an event log in order to identify deviations in the actual execution of the process with respect to the model. The paper is available at http://kodu.ut.ee/~dumas/pubs/coopis2017conformance.pdf
Process Mining and Predictive Process Monitoring in ApromoreMarlon Dumas
Seminar delivered at University of Hasselt on 14 May 2019. The seminar covers the research efforts underpinning Apromore's automated process discovery, conformance checking, log delta analysis, and predictive process monitoring plugins.
Deep Learning Models for Question AnsweringSujit Pal
Talk about a hobby project to apply Deep Learning models to predict answers to 8th grade science multiple choice questions for the Allen AI challenge on Kaggle.
Incremental and Interactive Process Model RepairMarlon Dumas
Paper presentation delivered by Abel Armas-Cervantes at the International Conference on Cooperative Information Systems (CoopIS), on 26 October 2018. The paper presents a technique to repair a given BPMN process model in such a way that it better first a given event log extracted from an information system. The paper is available at http://kodu.ut.ee/~dumas/pubs/coopis2017repair.pdf
This process model repair technique is implemented in the Apromore Business Process Analytics platform -- http://apromore.org
Efficient Process Model Discovery Using Maximal Pattern MiningDr. Sira Yongchareon
In recent years, process mining has become one of the most important and promising areas of research in the field of business process management as it helps businesses understand, analyze, and improve their business processes. In particular, several proposed techniques and algorithms have been proposed to discover and construct process models from workflow execution logs (i.e., event logs). With the existing techniques, mined models can be built based on analyzing the relationship between any two events seen in event logs. Being restricted by that, they can only handle special cases of routing constructs and often produce unsound models that do not cover all of the traces seen in the log. In this paper, we propose a novel technique for process discovery using Maximal Pattern Mining (MPM) where we construct patterns based on the whole sequence of events seen on the traces—ensuring the soundness of the mined models. Our MPM technique can handle loops (of any length), duplicate tasks, non-free choice constructs, and long distance dependencies. Our evaluation shows that it consistently achieves better precision, replay fitness and efficiency than the existing techniques.
Scalable Conformance Checking of Business ProcessesMarlon Dumas
Paper presentation delivered by Abel Armas-Cervantes at the International Conference on Cooperative Information Systems (CoopIS), on 26 October 2018. The paper presents a technique to compare a BPMN process model and an event log in order to identify deviations in the actual execution of the process with respect to the model. The paper is available at http://kodu.ut.ee/~dumas/pubs/coopis2017conformance.pdf
Process Mining and Predictive Process Monitoring in ApromoreMarlon Dumas
Seminar delivered at University of Hasselt on 14 May 2019. The seminar covers the research efforts underpinning Apromore's automated process discovery, conformance checking, log delta analysis, and predictive process monitoring plugins.
Barbara Fusinska - Machine Learning with R - Codemotion Milan 2017Codemotion
Data Science is becoming more and more popular. It covers a variety of topics and requires a wide range of skills. R is a programming language dedicated to working with data. The platform offers numerous libraries and implementations of machine learning algorithms. This makes it a perfect tool for exploratory data analysis and presenting the results of inquiries and data science in general. In this talk, Barbara will present capabilities of R in a field of data science. Along with the language's basics, the session will cover specific data applications.
Do I need tests when I have the compiler - Andrzej Jóźwiak - TomTom Dev Day 2020Andrzej Jóźwiak
Functional programming returned to the main stream after long years of hiatus. Languages like Haskell, Coq, Agda promise us better code just by using their advanced type systems. Although the dreaded null hides around every corner in Java is it possible to structure our code in a way that illegal states are not representable? Can the type system alone be enough for us to be sure that the code is correct? Do types mean that no tests are required?
During this talk, we will look at examples of code where the types control what code can be written (and there is no other way to do it). We will explore the possibilities to lower the number of unit tests or avoid some of them completely just by using the type system alone.
We will try to find an answer what stronger type systems can give us, what are dependent types and how could they look in Java.
This slides describes the basic concepts of industrial-strength compiler design. This includes basic concept of static single-assignment form (SSA) and various optimizations such as dead code elimination, global value numbering, constant propagation, etc. This is intend for a 150 minutes undergraduate compiler class.
Digicrome- It's a US Based Company that Provides Online Professional Courses. Digicrome is Asia's leading Brand that provides Online Data Science & Artificial intelligence Courses in 60+ Countries like Australia, Canada, America, Singapore, etc. with 50+ live projects & a 100% Job Placement Guarantee in Written with 3 Months stipend Internship
Digicrome is a provider of Job-ready professional Online courses, certification training, and test preparation for people of all ages. We offer comprehensive online training in a variety of subjects, including Data Science, Artificial intelligence, Python, Web Development, Cyber Security, and many more. we emphasize sectors where technology and best practices are fast evolving, and where the demand for qualified personnel far outnumbers supply.
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Digicrome- It's a US Based Company that Provides Online Professional Courses. Digicrome is Asia's leading Brand that provides Online Data Science & Artificial intelligence Courses in 60+ Countries like Australia, Canada, America, Singapore, etc. with 50+ live projects & a 100% Job Placement Guarantee Written with 3 Months stipend Internship.
Digicrome is a provider of Job-ready professional Online courses, certification training, and test preparation for people of all ages. We offer comprehensive online training in a variety of subjects, including Data Science, Artificial intelligence, Python, Web Development, Cyber Security, and many more. we emphasize sectors where technology and best practices are fast evolving, and where the demand for qualified personnel far outnumbers supply.
Building machine learning systems remains something of an art, from gathering and transforming the right data to selecting and finetuning the most fitting modeling techniques. If we want to make machine learning more accessible and foster skilfull use, we need novel ways to share and reuse findings, and streamline online collaboration. OpenML is an open science platform for machine learning, allowing anyone to easily share data sets, code, and experiments, and collaborate with people all over the world to build better models. It shows, for any known data set, which are the best models, who built them, and how to reproduce and reuse them in different ways. It is readily integrated into several machine learning environments, so that you can share results with the touch of a button or a line of code. As such, it enables large-scale, real-time collaboration, allowing anyone to explore, build on, and contribute to the combined knowledge of the field. Ultimately, this provides a wealth of information for a novel, data-driven approach to machine learning, where we learn from millions of previous experiments to either assist people while analyzing data (e.g., which modeling techniques will likely work well and why), or automate the process altogether.
Process Mining and Predictive Process MonitoringMarlon Dumas
Seminar delivered Sapienza University of Rome on 28/04/2017 and at Tallinn Tech on 16/02/2017. Video recording of the Rome delivery is available at: https://www.youtube.com/watch?v=hMQolsRT0K0
Introduction to Algorithms and Asymptotic NotationAmrinder Arora
Asymptotic Notation is a notation used to represent and compare the efficiency of algorithms. It is a concise notation that deliberately omits details, such as constant time improvements, etc. Asymptotic notation consists of 5 commonly used symbols: big oh, small oh, big omega, small omega, and theta.
Process Mining Reloaded: Event Structures as a Unified Representation of Proc...Marlon Dumas
Keynote talk at the 36th International Conference on Application and Theory of Petri Nets and Concurrency (Petri Nets 2015).
Screencast available at: https://youtu.be/9bQr0r_WaoE
Correcting Deadlocking Service Choreographies Using a Simulation-Based Graph ...Universität Rostock
Conference presentation given by Niels Lohmann on September 2, 2008 in Milan, Italy at the Sixth International Conference on Business Process Management (BPM 2008).
In his public lecture, Christian Timmerer provides insights into the fascinating history of video streaming, starting from its humble beginnings before YouTube to the groundbreaking technologies that now dominate platforms like Netflix and ORF ON. Timmerer also presents provocative contributions of his own that have significantly influenced the industry. He concludes by looking at future challenges and invites the audience to join in a discussion.
UiPath Test Automation using UiPath Test Suite series, part 5DianaGray10
Welcome to UiPath Test Automation using UiPath Test Suite series part 5. In this session, we will cover CI/CD with devops.
Topics covered:
CI/CD with in UiPath
End-to-end overview of CI/CD pipeline with Azure devops
Speaker:
Lyndsey Byblow, Test Suite Sales Engineer @ UiPath, Inc.
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Data Science is becoming more and more popular. It covers a variety of topics and requires a wide range of skills. R is a programming language dedicated to working with data. The platform offers numerous libraries and implementations of machine learning algorithms. This makes it a perfect tool for exploratory data analysis and presenting the results of inquiries and data science in general. In this talk, Barbara will present capabilities of R in a field of data science. Along with the language's basics, the session will cover specific data applications.
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Building machine learning systems remains something of an art, from gathering and transforming the right data to selecting and finetuning the most fitting modeling techniques. If we want to make machine learning more accessible and foster skilfull use, we need novel ways to share and reuse findings, and streamline online collaboration. OpenML is an open science platform for machine learning, allowing anyone to easily share data sets, code, and experiments, and collaborate with people all over the world to build better models. It shows, for any known data set, which are the best models, who built them, and how to reproduce and reuse them in different ways. It is readily integrated into several machine learning environments, so that you can share results with the touch of a button or a line of code. As such, it enables large-scale, real-time collaboration, allowing anyone to explore, build on, and contribute to the combined knowledge of the field. Ultimately, this provides a wealth of information for a novel, data-driven approach to machine learning, where we learn from millions of previous experiments to either assist people while analyzing data (e.g., which modeling techniques will likely work well and why), or automate the process altogether.
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Asymptotic Notation is a notation used to represent and compare the efficiency of algorithms. It is a concise notation that deliberately omits details, such as constant time improvements, etc. Asymptotic notation consists of 5 commonly used symbols: big oh, small oh, big omega, small omega, and theta.
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End-to-end overview of CI/CD pipeline with Azure devops
Speaker:
Lyndsey Byblow, Test Suite Sales Engineer @ UiPath, Inc.
Goodbye Windows 11: Make Way for Nitrux Linux 3.5.0!SOFTTECHHUB
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Dr. Sean Tan, Head of Data Science, Changi Airport Group
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• Q/A
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Paper: https://eprint.iacr.org/2023/1886
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https://arxiv.org/abs/2306.08302
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In the rapidly evolving landscape of technologies, XML continues to play a vital role in structuring, storing, and transporting data across diverse systems. The recent advancements in artificial intelligence (AI) present new methodologies for enhancing XML development workflows, introducing efficiency, automation, and intelligent capabilities. This presentation will outline the scope and perspective of utilizing AI in XML development. The potential benefits and the possible pitfalls will be highlighted, providing a balanced view of the subject.
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- https://x.com/viglovikov
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Metrics: The success indicators such as downloads, daily active users, GitHub stars, and financial contributions.
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Development Practices: Best practices for creating, maintaining, and scaling open-source libraries, including code hygiene, CI/CD, and fast iteration.
Community Building: Strategies for making adoption easy, iterating quickly, and fostering a vibrant, engaged community.
Marketing: Both online and offline marketing tactics, focusing on real, impactful interactions and collaborations.
Mental Health: Maintaining balance and not feeling pressured by user demands.
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While the dev and ops silo continues to crumble….many organizations still relegate monitoring & observability as the purview of ops, infra and SRE teams. This is a mistake - achieving a highly observable system requires collaboration up and down the stack.
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What will you get from this session?
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Join Maher Hanafi, VP of Engineering at Betterworks, in this new session where he'll share a practical framework to transform Gen AI prototypes into impactful products! He'll delve into the complexities of data collection and management, model selection and optimization, and ensuring security, scalability, and responsible use.
3. /process mining
algorithms
live data
historical data
process model
differences,
root-causes…
conformance
report
process
performance
A ⇒ B
“actionable”
process
knowledge
Process mining in a nutshell
15
4,318
14
14
858
13
7,128
26
3,794
32
31
734 28
6,212
9
1,526
941
4,324
258
186
4,360
4,360
Created
4,360
Waiting for Support
12,587
Waiting for Customer
8,681
Resolved
5,023
Closed
4,360
Waiting for Internal
923
Escalation
42
Waiting for Approval
14
Waiting for Triage
31
6. Given two logs L1 and L2, explain the differences between the
two logs
Simple claims and quick Simple claims and slow
Variants analysis
MODEL
S. Suriadi et al.: Understanding Process Behaviours in a Large Insurance Company in Australia: A Case Study. CAiSE 2013
9. Variants analysis: possible approaches
L1 - Short stay
448 cases
7329 events
L2 - Long stay
363 cases
7496 events
• Manual visual inspection: time-consuming and error prone, or
• Automated sequence classification…
At an Australian hospital…
10. Variants analysis: possible approaches
• Manual visual inspection: time-consuming and error prone, or
• Automated sequence classification…
Sequence classification
t1: <e11[d111:v111, …, d11n:v11m] e12[d121:v121, …, d12m:v12m] … e1p[d1p1:v1p1, …, d1pm:v1pm]>
…
tq: <eq1[dq11:vq11, …, dq1n:vq1m] eq2[dq21:vq21, …, dq2m:vq2m] … eqp[dqp1:vqp1, …, dqpm:vqpm]>
Find a function F: Trace Boolean such that
• F is an accurate approximation of the given labeling
• F is explainable, e.g. set of simple rules
11. Variants analysis: possible approaches
L1 - Short stay
448 cases
7329 events
L2 - Long stay
363 cases
7496 events
Sequence classification
106-130 statements
IF |“NursingProgressNotes”| > 7.5
THEN L1
IF |“Nursing Progress Notes”| ≤ 7.5
AND |“Nursing Assessment”| > 1.5
THEN L2
…
H. Nguyen, M. Dumas, M. La Rosa, F. Maggi, S. Suriadi: Mining Business Process Deviance: A Quest for Accuracy.
CoopIS, 2014
• Manual visual inspection: time-consuming and error prone, or
• Automated sequence classification…
At an Australian hospital…
13. 1. Compliance auditing
• detect deviations with respect to a normative model (unfitting behavior)
2. Model maintenance
• unfitting behavior
• additional model behavior
3. Automated process model discovery
• Iterative model improvement
Conformance analysis
14. Given an event log L and a process model M, explain the
differences between L and M in terms of process behavior
Conformance analysis
Log Model
15. State of the art: Trace alignment
Log Model
A B C DA B B C
Trace alignment
E
W. van der Aalst, A. Adriansyah, B. van Dongen: Replaying history on process models for conformance checking and performance analysis.
Wiley.: Data Mining and Knowledge Discovery 2(2): 2012
ABBCE13
E H
16. Trace alignment: typical output
A B C H E I J K C D I J K C E G
A B C H E I J K C D I J K C E
A B C H E I J K C E I K CJ F
A B C H E I J K C D I J K G
A B C H E I J K C D I J K G
A B C H E I J K C E I KJ
A B C H E I J K C E I KJ
A B C D I J K C I J KE G
A B C D I J K I J K C E G
A B C H E I J K C I KJH
H
H
H
H
H
A B C H E I J K C I KJH
A B C H I J K C E I KJH
A B C H E I J K I K CJ FH
A B C H E I J K I K CJ FH
A B C D I J K C I J KEH
A B C H E I J K I KJC D
A B C H E I J K I KJC D
A B C H E I J K I KJH
A B C H E I J K I KJH
A B C H E I J K GEC
A B C H E I J K GEC
A B C H E I J K EC
A B C H E I J K EC
A B C H I J K EC G
A B C D I J K GEC
A B C H I J K C F
A B C H I J K C F
A B C H I J K G
A B C H E I J K
A B C GE
A IE J K
A GE
Activity occurs in the log only,
but occurs in the model in another path
Activity occurs in the model only
and is not observed anywhere in the log
Activity occurs in the model only,
but occurs in the log in another trace
Activity occurs both in the model and the log
Legend
17. Trace alignment: shortcomings
Designed to identify the number and exact location of
the differences
Doesn’t provide a “high-level” diagnosis that easily
allows analysts to pinpoint differences:
• Unable to identify differences across traces
• Unable to fully characterize extra model behavior not
present in the log
19. Identify all differences between the process behaviors:
• of two logs (variants analysis)
• of a model and a log (conformance analysis)
Describe each difference via a natural language
statement
Fully automated, scalable
Solution requirements
20. An example (conformance analysis)
Desired conformance output:
• task C is optional in the log
• the cycle including IGDF is not observed in the log
Log
ABCDEH
ACBDEH
ABCDFH
ACBDFH
ABDEH
ABDFH
Model
ABDEH
ABDFH
21. Prime Event Structure (PES) as a unifying foundation
Model of concurrency based on events (occurrences
of process activities) and three relations:
• Causality
• Conflict
• Concurrency
causal
conflict
concurrent
22. From log to PES
Log
Trace Ref N
A B C E t1 3
A C B E t2 2
A B E t3 2
A D E t4 3
Runs
e0:A
e1:B e2:C
e3:E
f0:A
f1:B
f2:E
g0:A
g1:D
g2:E
t1, t2 → p1 t3 → p2 t4 → p3
PES
{e0,f0,g0}:A
{e1,f1}:B
{f2}:E {e3}:E {g2}:E
{e2}:C {g1}:D
23. From model to PES
BPMN model
Petri net
Branching process
24. From model to PES
Branching process
Complete prefix unfolding
Cutoff event
Corresponding
event
Cutoff event
Corresponding
event
27. Comparing PESs
Log PES Model PES
e0:A
e1:B e2:C e3:D
e4:E e5:E e6:E
Trace Ref N
A B C E t1 3
A C B E t2 2
A B E t3 2
A D E t4 3
A
B
D
E
C
f0:A
f1:B f2:C f3:D
f4:E f5:E
28. match B
lh = {}, rh = {}
m = {(e0,f0)A,(e1,f1)B}
rhide Cmatch C
lh = {}, rh = {f2:C}
m = {(e0,f0)A,(e1,f1)B}
lh = {}, rh = {}
m = {(e0,f0)A,(e1,f1)B,(e2,f2)C}
lh = {}, rh = {}
m = {(e0,f0)A}
match A
lh = {}, rh = {}
m = {}
match E
lh = {}, rh = {}
m = {(e0,f0)A,(e1,f1)B,(e2,f2)C,(e5,f4)E}
Comparing PESs
Log PES Model PES
e0:A
e1:B e2:C e3:D
e4:E e5:E e6:E
f0:A
f1:B f2:C f3:D
f4:E f5:E
29. match B
lh = {}, rh = {}
m = {(e0,f0)A,(e1,f1)B}
rhide Cmatch C
lh = {}, rh = {f2:C}
m = {(e0,f0)A,(e1,f1)B}
lh = {}, rh = {}
m = {(e0,f0)A,(e1,f1)B,(e2,f2)C}
lh = {}, rh = {}
m = {(e0,f0)A}
match A
lh = {}, rh = {}
m = {}
match E
lh = {}, rh = {}
m = {(e0,f0)A,(e1,f1)B,(e2,f2)C,(e5,f4)E}
Comparing PESs
Log PES Model PES
e0:A
e1:B e2:C e3:D
e4:E e5:E e6:E
f0:A
f1:B f2:C f3:D
f4:E f5:E
30. match B
lh = {}, rh = {}
m = {(e0,f0)A,(e1,f1)B}
rhide Cmatch C
lh = {}, rh = {f2:C}
m = {(e0,f0)A,(e1,f1)B}
lh = {}, rh = {}
m = {(e0,f0)A,(e1,f1)B,(e2,f2)C}
lh = {}, rh = {}
m = {(e0,f0)A}
match A
lh = {}, rh = {}
m = {}
match E
lh = {}, rh = {}
m = {(e0,f0)A,(e1,f1)B,(e2,f2)C,(e5,f4)E}
Comparing PESs
Log PES Model PES
e0:A
e1:B e2:C e3:D
e4:E e5:E e6:E
f0:A
f1:B f2:C f3:D
f4:E f5:E
31. match B
lh = {}, rh = {}
m = {(e0,f0)A,(e1,f1)B}
rhide Cmatch C
lh = {}, rh = {f2:C}
m = {(e0,f0)A,(e1,f1)B}
lh = {}, rh = {}
m = {(e0,f0)A,(e1,f1)B,(e2,f2)C}
lh = {}, rh = {}
m = {(e0,f0)A}
match A
lh = {}, rh = {}
m = {}
match E
lh = {}, rh = {}
m = {(e0,f0)A,(e1,f1)B,(e2,f2)C,(e5,f4)E}
Comparing PESs
Log PES Model PES
e0:A
e1:B e2:C e3:D
e4:E e5:E e6:E
f0:A
f1:B f2:C f3:D
f4:E f5:E
32. match B
lh = {}, rh = {}
m = {(e0,f0)A,(e1,f1)B}
rhide Cmatch C
lh = {}, rh = {f2:C}
m = {(e0,f0)A,(e1,f1)B}
lh = {}, rh = {}
m = {(e0,f0)A,(e1,f1)B,(e2,f2)C}
lh = {}, rh = {}
m = {(e0,f0)A}
match A
lh = {}, rh = {}
m = {}
match E
lh = {}, rh = {}
m = {(e0,f0)A,(e1,f1)B,(e2,f2)C,(e5,f4)E}
Comparing PESs
Log PES Model PES
e0:A
e1:B e2:C e3:D
e4:E e5:E e6:E
f0:A
f1:B f2:C f3:D
f4:E f5:E
33. match B
lh = {}, rh = {}
m = {(e0,f0)A,(e1,f1)B}
rhide Cmatch C
lh = {}, rh = {f2:C}
m = {(e0,f0)A,(e1,f1)B}
lh = {}, rh = {}
m = {(e0,f0)A,(e1,f1)B,(e2,f2)C}
lh = {}, rh = {}
m = {(e0,f0)A}
match A
lh = {}, rh = {}
m = {}
match E
lh = {}, rh = {}
m = {(e0,f0)A,(e1,f1)B,(e2,f2)C,(e5,f4)E}
Comparing PESs
Log PES Model PES
e0:A
e1:B e2:C e3:D
e4:E e5:E e6:E
f0:A
f1:B f2:C f3:D
f4:E f5:E
34. match B
lh = {}, rh = {}
m = {(e0,f0)A,(e1,f1)B}
rhide Cmatch C
lh = {}, rh = {f2:C}
m = {(e0,f0)A,(e1,f1)B}
lh = {}, rh = {}
m = {(e0,f0)A,(e1,f1)B,(e2,f2)C}
lh = {}, rh = {}
m = {(e0,f0)A}
match A
lh = {}, rh = {}
m = {}
match E
lh = {}, rh = {}
m = {(e0,f0)A,(e1,f1)B,(e2,f2)C,(e5,f4)E}
Comparing PESs
Log PES Model PES
e0:A
e1:B e2:C e3:D
e4:E e5:E e6:E
f0:A
f1:B f2:C f3:D
f4:E f5:E
In the log, C is optional
after {A,B}, whereas in
the model it is not
match Dmatch C
35. Mismatch patterns (conformance analysis)
Unfitting behavior patterns:
• Relation mismatch patterns
1. Causality-Concurrency
2. Conflict
• Event mismatch patterns
3. Task skipping
4. Task substitution
5. Unmatched repetition
6. Task relocation
7. Task insertion / absence
Additional model behavior patterns:
8. Unobserved acyclic interval
9. Unobserved cyclic interval
L. Garcia-Banuelos, N.R. van Beest, M. Dumas, M. La Rosa, W. Mertens, Complete and Interpretable Conformance Checking of Business
Processes, IEEE Transactions on Software Engineering, 2017
3. Task skipping
36. Additional model behavior: precision vs generalization
Log
⟨A⟩
⟨A,A⟩
⟨A,A,A⟩
In the log, the cycle involving [A] does not occur
40. Coming back to our example (variants analysis)
L1 - Short stay
448 cases
7329 events
L2 - Long stay
363 cases
7496 events
Sequence classification
106-130 statements
IF |“NursingProgressNotes”| > 7.5
THEN L1
IF |“Nursing Progress Notes”| ≤ 7.5
AND |“Nursing Assessment”| > 1.5
THEN L2
…
Our approach (PSP-based)
48 statements
In L2, “Nursing Primary Assessment”
is repeated after “Medical Assign”
and “Triage Request”, while in L2 it is
not
…
N.R. van Beest, L. Garcia-Banuelos, M. Dumas, M. La Rosa, Log Delta Analysis: Interpretable Differencing of Business Process Event Logs.
BPM 2015: 386-405
At an Australian hospital…
41. Evaluation (conformance analysis)
1. Qualitative evaluation on real life process:
• Traffic fines management process in Italy
(2000-2013; 150,370 traces; 231 distinct traces)
2. Quantitative evaluation on two large process model collections:
• IBM Business Integration Unit (BIT): 735 models
• SAP R/3: 604 models
3. User evaluation (academics vs practitioners)
42. Qualitative evaluation: traffic fines model
Start Create
Fine
Payment
Send
Fine
Insert
Fine
Notification
Add
Penalty
Appeal
to Judge
Send for
Credit
Collection
Notify
Result
Appeal to
Offender
Insert Date
Appeal to
Prefecture
Receive
Result
Appeal from
Prefecture
Send
Appeal
to Prefecture
End
Tau10
Created from the process specification
44. Qualitative evaluation: output of our approach
15 statements, e.g.
1. In the log, “Send for credit collection” occurs after
“Payment”
2. In the model, after “Insert fine notification”, “Add penalty”
occurs before “Appeal to judge”, while in the log they are
concurrent
3. In the log, after “Add penalty”, “Receive results appeal from
prefecture” is substituted by “Appeal to judge”
4. In the log, the cycle involving “Insert date appeal to
prefecture, Send appeal to prefecture, Receive result appeal
from prefecture, Notify result appeal to offender” does not
occur after “Insert fine notification”
5. …
Cannot be detected by trace alignment,
as diagnostics are provided at the level
of individual traces
Cannot be entirely detected by trace
alignment, as this difference
concerns additional model behavior
45. Quantitative evaluation
• For each model in the SAP R/3 and IBM BIT collections, we
generated an event log artificially
• Injected different levels of noise (0-20%) to simulate differences
• Total logs: 712
Results:
• Generally slower, but reasonable execution times: < 10 sec
• Extreme cases (8,000+ events, 15-20% noise): < 2 min
• Consistently more compact diagnosis than trace alignment
46. User evaluation
Online survey:
• Simple Petri net model with 31 nodes, created from a real-life
claims handling process
• small size to avoid understandability bias
• anonymized to avoid domain bias
• Accompanied by a log with 53 traces
Output of trace alignment (misalignments)
vs
Output of our approach (list of statements)
47. User evaluation
Responded stated their experience (years, models created and analyzed) and
expertise in Petri nets (familiarity, competence and confidence)
Respondents compared both approaches using the Technology Acceptance Model:
1. What is the easiest approach for checking the conformance of an event log to
a process model?
2. What is the easiest approach for identifying the differences between a process
model and an event log?
3. What is the most useful approach for checking the conformance of an event
log to a process model?
4. What is the most useful approach for identifying the differences between a
process model and an event log?
5. Which approach would you likely use for checking the conformance of an
event log to a process model?
6. Which approach would you likely use for identifying the differences between a
process model and an event log?
48. User evaluation: population
Academics (38 responses)
• Expertise: more familiar, confident and competent in working with Petri nets
• Experience: analysed and created more models in the past 12 months
Professionals (33 responses)
• Less expert and experienced with Petri nets
• Mostly rely on professional training (higher than academics)
49. User evaluation: hypotheses
H1: respondents would have a preference for verbalization
H2: respondents with less experience, familiarity, confidence and
competence in the use of Petri nets would have a stronger
preference for verbalization
50. User evaluation: results
H1: preference for verbalization
• Tested for the full sample and for the two cohorts separately
• For the full sample there is no general preference for our approach: the
median was zero (“neutral”)
• Professionals did show a preference for verbalization (especially along
ease of use) while academics preferred alignment
• H1 is supported for the professionals cohort only
H2: little knowledge of Petri nets -> stronger preference
• Respondents with more experience with and expertise in Petri nets have
a stronger preference for alignments
• H2 is supported
51. Pushing it a bit further… Process model repair
• Rank statements based on impact
• Visualize differences on top of BPMN model
• Repair process model interactively and incrementally
A. Armas Cervantes, N. van Beest, M. La Rosa, M. Dumas, L. Garcia-Banuelos, Interactive and Incremental Business Process Model Repair,
CoopIS 2017
52. Pushing it a bit further… Process model repair
• Rank statements based on impact
• Visualize differences on top of BPMN model
• Repair process model interactively and incrementally
A. Armas Cervantes, N. van Beest, M. La Rosa, M. Dumas, L. Garcia-Banuelos, Interactive and Incremental Business Process Model Repair,
CoopIS 2017
53. Tool support: Apromore (apromore.org)
• Open-source BPM analytics platform as Software as a Service
• Focus is on end users (business analytics), not on data scientists
• 50+ plugins
!
!
54. Nirdizati: predictive process monitoring (nirdizati.com)
• Predict process outcome (e.g. “Is this loan offer going to be rejected?”)
• Predict process performance (e.g. “Will this claim take longer than 5 days to be
handled?”)
• Predict future events (e.g. “What activity is likely to be executed next? And after that?”)
55. BPM Discipline
Information Systems School
Science & Engineering Faculty
Queensland University of Technology
m.larosa@qut.edu.au
marcellolarosa.com
@mlr80