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008			161124s2016 gw | s |||| 0|eng d
020			_a9783319494517
024	7		_a10.1007/978-3-319-49451-7 _2doi
040			_aES-MaUEC
050		4	_aQA76.9.D343 _bM866 2016 EB
100	1		_aMunoz-Gama, Jorge. _9101842 _0Local
245	1	0	_aConformance Checking and Diagnosis in Process Mining : _bComparing Observed and Modeled Processes _cby Jorge Munoz-Gama.
260			_aCham, Switzerland _bSpringer _c2016
300			_a1 recurso en línea (XIV, 202 p.) _b90 ilustraciones
336			_aTexto (visual) _btxt _2rdacontent
337			_aelectrónico _bc _2rdamedia
338			_arecurso electrónico _bcr _2rdacarrier
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		7	_aGestión de empresas _2embne _9413157
650		7	_aData mining _2embne _9162648
650		7	_aSistemas de información en la gestión _2embne _9145258
650		7	_aInformática _2embne _9139268
830		0	_aLecture Notes in Business Information Processing _x1865-1348 _v270 _0http://id.loc.gov/authorities/names/no2007152362 _9134211
856	4	0	_uhttps://go.openathens.net/redirector/universidadeuropea.es?url=https://link.springer.com/book/10.1007/978-3-319-49451-7 _zAcceso a este recurso digital (usuarios Universidad Europea de Madrid)
901			_ai9783319494517
907			_a.b12982544 _b10-10-17 _c08-03-17
942			_2lcc _cLE
945			_aQA76.9.D343 M866 2016 EB _g1 _ieBOOK _j0 _lmae _o- _pEUR0.00 _q- _r- _sb _t15 _u0 _v0 _w0 _x0 _y.i11604384 _z06-04-17
988			_aEBOOK, EBSPRINGER
998			_am _a_alco _a_vill _b - - _cm _dz _e- _feng _ggw _h0