Conformance Checking and Diagnosis in Process Mining Comparing Observed and Modeled Processes /

• Κύριος συγγραφέας: Munoz-Gama, Jorge (Συγγραφέας)
• Συγγραφή απο Οργανισμό/Αρχή: SpringerLink (Online service)
• Μορφή: Ηλεκτρονική πηγή Ηλ. βιβλίο
• Γλώσσα: English
• Έκδοση: Cham : Springer International Publishing : Imprint: Springer, 2016.
• Σειρά: Lecture Notes in Business Information Processing, 270
• Θέματα: Computer science.; Management information systems.; Industrial management.; Data mining.; Application software.; Computer Science.; Computer Appl. in Administrative Data Processing.; Business Process Management.; Data Mining and Knowledge Discovery.

 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. 

 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.