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Conformance Checking and Diagnosis in Process Mining: Comparing Observed and Modeled Processes 1st ed. 2016 [Pehme köide]

  • Formaat: Paperback / softback, 202 pages, kõrgus x laius: 235x155 mm, kaal: 3343 g, 90 Illustrations, black and white; XIV, 202 p. 90 illus., 1 Paperback / softback
  • Sari: Lecture Notes in Business Information Processing 270
  • Ilmumisaeg: 25-Nov-2016
  • Kirjastus: Springer International Publishing AG
  • ISBN-10: 3319494503
  • ISBN-13: 9783319494500
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  • Formaat: Paperback / softback, 202 pages, kõrgus x laius: 235x155 mm, kaal: 3343 g, 90 Illustrations, black and white; XIV, 202 p. 90 illus., 1 Paperback / softback
  • Sari: Lecture Notes in Business Information Processing 270
  • Ilmumisaeg: 25-Nov-2016
  • Kirjastus: Springer International Publishing AG
  • ISBN-10: 3319494503
  • ISBN-13: 9783319494500

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.

1 Introduction
1(10)
1.1 Processes, Models, and Data
1(2)
1.2 Process Mining
3(2)
1.3 Conformance Checking Explained: The University Case
5(2)
1.4 Book Outline
7(4)
Part I Conformance Checking in Process Mining
2 Conformance Checking and its Challenges
11(8)
2.1 The Role of Process Models in Conformance Checking
11(1)
2.2 Dimensions of Conformance Checking
12(3)
2.3 Replay-based and Align-based Conformance Checking
15(1)
2.4 Challenges of Conformance Checking
15(4)
3 Conformance Checking and its Elements
19(14)
3.1 Basic Notations
20(2)
3.2 Event Logs
22(1)
3.3 Process Models
23(3)
3.4 Process Modeling Formalisms
26(7)
3.4.1 Petri Nets
26(2)
3.4.2 Workflow Nets
28(1)
3.4.3 Other Formalisms
28(5)
Part II Precision in Conformance Checking
4 Precision in Conformance Checking
33(6)
4.1 Precision: The Forgotten Dimension
34(1)
4.2 The Importance of Precision
34(1)
4.3 Measures of Precision
35(2)
4.4 Requirements for Precision
37(2)
5 Measuring Precision
39(16)
5.1 Precision based on Escaping Arcs
40(2)
5.2 Constructing the Observed Behavior
42(1)
5.3 Incorporating Modeled Behavior
43(3)
5.4 Detecting Escaping Arcs and Evaluating Precision
46(3)
5.5 Minimal Imprecise Traces
49(2)
5.6 Limitations and Extensions
51(2)
5.6.1 Unfitting Scenario
51(1)
5.6.2 Indeterministic Scenario
52(1)
5.7 Summary
53(2)
6 Evaluating Precision in Practice
55(6)
6.1 The University Case: The Appeals Process
56(2)
6.2 Experimental Evaluation
58(3)
7 Handling Noise and Incompleteness
61(14)
7.1 Introduction
62(1)
7.2 Robustness on the Precision
62(5)
7.3 Confidence on Precision
67(3)
7.3.1 Upper Confidence Value
67(2)
7.3.2 Lower Confidence Value
69(1)
7.4 Experimental Results
70(3)
7.5 Summary
73(2)
8 Assessing Severity
75(10)
8.1 Introduction
76(1)
8.2 Severity of an Escaping Arc
76(6)
8.2.1 Weight of an Escaping Arc
77(1)
8.2.2 Alternation of an Escaping Arc
78(1)
8.2.3 Stability of an Escaping Arc
78(2)
8.2.4 Criticality of an Escaping Arc
80(1)
8.2.5 Visualizing the Severity
80(1)
8.2.6 Addressing Precision Issues based on Severity
81(1)
8.3 Experimental Results
82(2)
8.4 Summary
84(1)
9 Handling non-Fitness
85(12)
9.1 Introduction
86(2)
9.2 Cost-Optimal Alignment
88(4)
9.3 Precision based on Alignments
92(1)
9.4 Precision from 1-Alignment
93(3)
9.5 Summary
96(1)
10 Alternative and Variants to Handle non-Fitness
97(10)
10.1 Precision from All-Alignment
98(2)
10.2 Precision from Representative-Alignment
100(2)
10.3 Abstractions for the Precision based on Alignments
102(4)
10.3.1 Abstraction on the Order
104(1)
10.3.2 Abstraction on the Direction
105(1)
10.4 Summary
106(1)
11 Handling non-Fitness in Practice
107(14)
11.1 The University Case: The Exchange Process
108(4)
11.2 Experimental Results
112(9)
Part III Decomposition in Conformance Checking
12 Decomposing Conformance Checking
121(8)
12.1 Introduction
122(1)
12.2 Single-Entry Single-Exit and Refined Process Structure Tree
123(2)
12.3 Decomposing Conformance Checking using SESEs
125(2)
12.4 Summary
127(2)
13 Decomposing for Fitness Checking
129(12)
13.1 Introduction
130(1)
13.2 Bridging a Valid Decomposition
130(5)
13.3 Decomposition with invisible/duplicates
135(3)
13.4 Summary
138(3)
14 Decomposing Conformance Checking in Practice
141(10)
14.1 The Bank Case: The Transaction Process
142(3)
14.2 Experimental Results
145(6)
15 Diagnosing Conformance
151(12)
15.1 Introduction
152(1)
15.2 Topological Conformance Diagnosis
153(3)
15.3 Multi-level Conformance Diagnosis and its Applications
156(3)
15.3.1 Stand-alone Checking
156(1)
15.3.2 Multi-Level Analysis
157(1)
15.3.3 Filtering
158(1)
15.4 Experimental Results
159(2)
15.5 Summary
161(2)
16 Data-aware Processes and Alignments
163(10)
16.1 Introduction
164(2)
16.2 Data-aware Processes
166(6)
16.2.1 Petri nets with Data
166(3)
16.2.2 Event Logs and Relating Models to Event Logs
169(1)
16.2.3 Data Alignments
170(2)
16.3 Summary
172(1)
17 Decomposing Data-aware Conformance
173(8)
17.1 Introduction
174(1)
17.2 Valid Decomposition of Data-aware Models
174(2)
17.3 SESE-based Strategy for a Valid Decomposition
176(1)
17.4 Implementation and Experimental Results
177(2)
17.5 Summary
179(2)
18 Event-based Real-time Decomposed Conformance Checking
181(10)
18.1 Introduction
182(1)
18.2 Event-based Real-time Decomposed Conformance
182(4)
18.2.1 Model and Log Decomposition
182(2)
18.2.2 Event-based Heuristic Replay
184(2)
18.3 Experimental Results
186(2)
18.4 Summary
188(3)
Part IV Conclusions and Future Work
19 Conclusions
191(6)
19.1 Conclusion and Reflection
191(1)
19.2 Summary of Contributions
192(1)
19.3 Challenges and Directions for Future Work
193(4)
References 197