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Conformance Checking and Diagnosis in Process Mining Comparing Observed and Modeled Processes
Author
Title
Conformance Checking and Diagnosis in Process Mining [electronic resource] : Comparing Observed and Modeled Processes / by Jorge Munoz-Gama.
ISBN
9783319494517
Publication
Cham : Springer International Publishing : Imprint: Springer, 2016.
Physical Description
XIV, 202 p. 90 illus : online resource.
Local Notes
Access is available to the Yale community.
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Access restricted by licensing agreement.
Summary
Process mining techniques can be used to discover, analyze and improve real processes, by extracting models from observed behavior. The aim of this book is conformance checking, one of the main areas of process mining. In conformance checking, existing process models are compared with actual observations of the process in order to assess their quality. Conformance checking techniques are a way to visualize the differences between assumed process represented in the model and the real process in the event log, pinpointing possible problems to address, and the business process management results that rely on these models. This book combines both application and research perspectives. It provides concrete use cases that illustrate the problems addressed by the techniques in the book, but at the same time, it contains complete conceptualization and formalization of the problem and the techniques, and through evaluations on the quality and the performance of the proposed techniques. Hence, this book brings the opportunity for business analysts willing to improve their organization processes, and also data scientists interested on the topic of process-oriented data science.
Variant and related titles
Springer eBooks.
Other formats
Printed edition:
Format
Books / Online
Language
English
Added to Catalog
December 01, 2016
Series
Lecture Notes in Business Information Processing, 270
Contents
Introduction
1.1 Processes, Models, and Data
1.2 Process Mining
1.3 Conformance Checking Explained: The University Case
1.4 Book Outline
Part I Conformance Checking in Process Mining
2 Conformance Checking and its Challenges
2.1 The Role of Process Models in Conformance Checking
2.2 Dimensions of Conformance Checking
2.3 Replay-based and Align-based Conformance Checking
2.4 Challenges of Conformance Checking
3 Conformance Checking and its Elements
3.1 Basic Notations
3.2 Event Log
3.3 Process Models
3.4 Process Modeling Formalisms
3.4.1 Petri Nets
3.4.2 Workflow Nets
3.4.3 Other Formalisms
Part II Precision in Conformance Checking
4 Precision in Conformance Checking
4.1 Precision: The Forgotten Dimension
4.2 The Importance of Precision
4.3 Measures of Precision
4.4 Requirements for Precision
5 Measuring Precision
5.1 Precision based on Escaping Arcs
5.2 Constructing the Observed Behavior
5.3 Incorporating Modeled Behavior
5.4 Detecting Escaping Arcs and Evaluating Precision
5.5 Minimal Imprecise Traces
5.6 Limitations and Extensions
5.6.1 Unfitting Scenario
5.6.2 Indeterministic Scenario
5.7 Summary
6 Evaluating Precision in Practice
6.1 The University Case: The Appeals Process
6.2 Experimental Evaluation
7 Handling Noise and Incompleteness
7.1 Introduction
7.2 Robustness on the Precision
7.3 Confidence on Precision.-7.3.1 Upper Confidence Value
7.3.2 Lower Confidence Value
7.4 Experimental Results
7.5 Summary
8 Assessing Severity
8.1 Introduction
8.2 Severity of an Escaping Arc
8.2.1 Weight of an Escaping Arc
8.2.2 Alternation of an Escaping Arc
8.2.3 Stability of an Escaping Arc
8.2.4 Criticality of an Escaping Arc
8.2.5 Visualizing the Severity
8.2.6 Addressing Precision Issues based on Severity
8.3 Experimental Results
8.4 Summary
9 Handling non-Fitness
9.1 Introduction
9.2 Cost-Optimal Alignment
9.3 Precision based on Alignments
9.4 Precision from 1-Alignment
9.5 Summary
10 Alternative and Variants to Handle non-Fitness
10.1 Precision from All-Alignment
10.2 Precision from Representative-Alignment
10.3 Abstractions for the Precision based on Alignments
10.3.1 Abstraction on the Order
10.3.2 Abstraction on the Direction
10.4 Summary
11 Handling non-Fitness in Practice
11.1 The University Case: The Exchange Process
11.2 Experimental Results
Part III Decomposition in Conformance Checking
12 Decomposing Conformance Checking. -12.1 Introduction
12.2 Single-Entry Single-Exit and Refined Process Structure Tree
12.3 Decomposing Conformance Checking using SESEs
12.4 Summary
13 Decomposing for Fitness Checking
13.1 Introduction
13.2 Bridging a Valid Decomposition
13.3 Decomposition with invisible/duplicates
13.4 Summary
14 Decomposing Conformance Checking in Practice
14.1 The Bank Case: The Transaction Process
14.2 Experimental Results
15 Diagnosing Conformance
15.1 Introduction
15.2 Topological Conformance Diagnosis
15.3 Multi-level Conformance Diagnosis and its Applications
15.3.1 Stand-alone Checking
15.3.2 Multi-Level Analysis
15.3.3 Filtering
15.4 Experimental Results
15.5 Summary
16 Data-aware Processes and Alignments
16.1 Introduction
16.2 Data-aware Processes
16.2.1 Petri nets with Data
16.2.2 Event Logs and Relating Models to Event Logs
16.2.3 Data Alignments
16.3 Summary
17 Decomposing Data-aware Conformance
17.1 Introduction
17.2 Valid Decomposition of Data-aware Models
17.3 SESE-based Strategy for a Valid Decomposition
17.4 Implementation and Experimental Results
17.5 Summary
18 Event-based Real-time Decomposed Conformance Checking
18.1 Introduction
18.2 Event-based Real-time Decomposed Conformance
18.2.1 Model and Log Decomposition
18.2.2 Event-based Heuristic Replay
18.3 Experimental Results
18.4 Summary
Part IV Conclusions and Future Work
19 Conclusions
19.1 Conclusion and Reflection
19.2 Summary of Contributions
19.3 Challenges and Directions for Future Work
References.
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