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001 978-3-319-49451-7
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008 161124s2016 gw | s |||| 0|eng d
020 _a9783319494517
_9978-3-319-49451-7
024 7 _a10.1007/978-3-319-49451-7
_2doi
050 4 _aQA76.76.A65
050 4 _aTA345-345.5
072 7 _aJPP
_2bicssc
072 7 _aUB
_2bicssc
072 7 _aCOM018000
_2bisacsh
072 7 _aPOL017000
_2bisacsh
082 0 4 _a004
_223
100 1 _aMunoz-Gama, Jorge.
_eauthor.
245 1 0 _aConformance Checking and Diagnosis in Process Mining
_h[electronic resource] :
_bComparing Observed and Modeled Processes /
_cby Jorge Munoz-Gama.
264 1 _aCham :
_bSpringer International Publishing :
_bImprint: Springer,
_c2016.
300 _aXIV, 202 p. 90 illus.
_bonline resource.
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
347 _atext file
_bPDF
_2rda
490 1 _aLecture Notes in Business Information Processing,
_x1865-1348 ;
_v270
505 0 _aIntroduction -- 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.
520 _aProcess 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.
650 0 _aComputer science.
650 0 _aManagement information systems.
650 0 _aIndustrial management.
650 0 _aData mining.
650 0 _aApplication software.
650 1 4 _aComputer Science.
650 2 4 _aComputer Appl. in Administrative Data Processing.
650 2 4 _aBusiness Process Management.
650 2 4 _aData Mining and Knowledge Discovery.
710 2 _aSpringerLink (Online service)
773 0 _tSpringer eBooks
776 0 8 _iPrinted edition:
_z9783319494500
830 0 _aLecture Notes in Business Information Processing,
_x1865-1348 ;
_v270
856 4 0 _uhttp://dx.doi.org/10.1007/978-3-319-49451-7
912 _aZDB-2-SCS
942 _cEBK
999 _c57128
_d57128