Verfügbarkeit: Process mining | Technische Universität München

Process mining: data science in action

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Bibliographische Detailangaben
Beteilige Person:	Aalst, Wil van der 1966- (VerfasserIn)
Format:	Buch
Sprache:	Englisch
Veröffentlicht:	Berlin ; Heidelberg Springer [2016]
Ausgabe:	Second edition
Schlagwörter:	Daten Datenmanagement Big Data Software Engineering Data Mining Prozessmanagement Prozessmodell Betriebliches Informationssystem Informatik JPP KJQ UM Business Information Systems Business Intelligence Business Process Management Data Science Workflow Management
Links:	http://deposit.dnb.de/cgi-bin/dokserv?id=a755893269c84c0583616dc17589acbd&prov=M&dok_var=1&dok_ext=htm http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=029050562&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA
Umfang:	xix, 467 Seiten Illustrationen, Diagramme (teilweise farbig)
ISBN:	9783662498507 9783662570418

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Datensatz im Suchindex

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adam_text	Titel: Process Mining Autor: Aalst, Wil van der Jahr: 2016 Contents Part I Introduction 1 Data Science in Action......................... 3 1.1 Internet of Events......................... 3 1.2 Data Scientist........................... 10 1.3 Bridging the Gap Between Process Science and Data Science . . 15 1.4 Outlook .............................. 20 2 Process Mining: The Missing Link.................. 25 2.1 Limitations ofModeling...................... 25 2.2 Process Mining .......................... 30 2.3 Analyzing an Example Log.................... 35 2.4 Play-In, Play-Out, and Replay................... 41 2.5 Positioning Process Mining.................... 44 2.5.1 How Process Mining Compares to BPM......... 44 2.5.2 How Process Mining Compares to Data Mining..... 46 2.5.3 How Process Mining Compares to Lean Six Sigma .... 46 2.5.4 How Process Mining Compares to BPR.......... 49 2.5.5 How Process Mining Compares to Business Intelligence . 49 2.5.6 How Process Mining Compares to CEP.......... 50 2.5.7 How Process Mining Compares to GRC ......... 50 2.5.8 How Process Mining Compares to ABPD, BPI, WM,.... 51 2.5.9 How Process Mining Compares to Big Data....... 52 Part II Preliminaries 3 Process Modeling and Analysis.................... 55 3.1 The Art ofModeling........................ 55 3.2 Process Models .......................... 57 3.2.1 Transition Systems..................... 58 3.2.2 PetriNets ......................... 59 3.2.3 Workflow Nets....................... 65 3.2.4 YAWL........................... 66 xvi Contents 3.2.5 Business Process Modeling Notation (BPMN)...... 68 3.2.6 Event-Driven Process Chains (EPCs)........... 70 3.2.7 CausalNets ........................ 72 3.2.8 Process Trees ....................... 78 3.3 Model-Based Process Analysis.................. 83 3.3.1 Verification......................... 83 3.3.2 Performance Analysis................... 85 3.3.3 Limitations of Model-Based Analysis........... 88 4 Data Mining............................... 89 4.1 Classification of Data Mining Techniques............. 89 4.1.1 Data Sets: Instances and Variables ............ 90 4.1.2 Supervised Learning: Classification and Regression ... 92 4.1.3 Unsupervised Learning: Clustering and Pattern Discovery 94 4.2 Decision Tree Learning...................... 94 4.3 /t-Means Clustering........................ 100 4.4 Association Rule Learning..................... 104 4.5 Sequence and Episode Mining................... 107 4.5.1 Sequence Mining..................... 107 4.5.2 Episode Mining...................... 109 4.5.3 Other Approaches..................... 111 4.6 Quality of Resulting Models.................... 112 4.6.1 Measuring the Performance of a Classifier........ 113 4.6.2 Cross-Validation...................... 115 4.6.3 Occam sRazor....................... 118 Part III From Event Logs to Process Models 5 Getting the Data ............................ 125 5.1 DataSources............................ 125 5.2 Event Logs............................. 128 5.3 XES................................ 138 5.4 Data Quality............................ 144 5.4.1 Conceptualizing Event Logs................ 145 5.4.2 Classification of Data Quality Issues ........... 148 5.4.3 Guidelines for Logging.................. 151 5.5 Flattening Reality into Event Logs ................ 153 6 Process Discovery: An Introduction.................. 163 6.1 Problem Statement......................... 163 6.2 A Simple Algorithm for Process Discovery............ 167 6.2.1 Basic Idea......................... 167 6.2.2 Algorithm......................... 171 6.2.3 Limitations of the a-Algorithm.............. 174 6.2.4 Taking the Transactional Life-Cycle into Account .... 177 6.3 Rediscovering Process Models .................. 178 6.4 Challenges............................. 182 Contents xvii 6.4.1 Representational Bias................... 183 6.4.2 Noise and Incompleteness................. 185 6.4.3 Four Competing Quality Criteria............. 188 6.4.4 Taking the Right 2-D Slice of a 3-D Reality ....... 192 7 Advanced Process Discovery Techniques............... 195 7.1 Overview.............................. 195 7.1.1 Characteristic 1: Representational Bias.......... 197 7.1.2 Characteristic 2: Ability to Deal With Noise....... 198 7.1.3 Characteristic 3: Completeness Notion Assumed..... 199 7.1.4 Characteristic 4: Approach Used ............. 199 7.2 Heuristic Mining.......................... 201 7.2.1 Causal Nets Revisited................... 201 7.2.2 Learning the Dependency Graph ............. 202 7.2.3 Learning Splits and Joins................. 205 7.3 Genetic Process Mining...................... 207 7.4 Region-Based Mining....................... 212 7.4.1 Learning Transition Systems ............... 212 7.4.2 Process Discovery Using State-Based Regions...... 216 7.4.3 Process Discovery Using Language-Based Regions . . . 218 7.5 Inductive Mining.......................... 222 7.5.1 Inductive Miner Based on Event Log Splitting...... 222 7.5.2 Characteristics of the Inductive Miner........... 229 7.5.3 Extensions and Scalability................. 233 7.6 Historical Perspective....................... 236 Part IV Beyond Process Discovery 8 Conformance Checking ........................ 243 8.1 Business Alignment and Auditing................. 243 8.2 TokenReplay ........................... 246 8.3 Alignments............................. 256 8.4 Comparing Footprints....................... 263 8.5 Other Applications of Conformance Checking.......... 268 8.5.1 Repairing Models..................... 268 8.5.2 Evaluating Process Discovery Algorithms ........ 269 8.5.3 Connecting Event Log and Process Model........ 272 9 Mining Additional Perspectives.................... 275 9.1 Perspectives............................ 275 9.2 Attributes: A Helicopter View................... 277 9.3 Organizational Mining....................... 281 9.3.1 Social Network Analysis ................. 282 9.3.2 Discovering Organizational Structures .......... 287 9.3.3 Analyzing Resource Behavior............... 288 9.4 Time and Probabilities....................... 290 9.5 Decision Mining.......................... 294 9.6 Bringing It All Together...................... 297 xviii Contents 10 Operational Support.......................... 301 10.1 Refined Process Mining Framework................ 301 10.1.1 Cartography........................ 303 10.1.2 Auditing.......................... 304 10.1.3 Navigation......................... 305 10.2 Online Process Mining....................... 305 10.3 Detect............................... 307 10.4 Predict............................... 311 10.5 Recommend............................ 316 10.6 Processes Are Not in Steady State!................ 318 10.6.1 Daily, Weekly and Seasonal Patterns in Processes .... 318 10.6.2 Contextual Factors..................... 318 10.6.3 Concept Drift in Processes................. 320 10.7 Process Mining Spectrum..................... 321 Part V Putting Process Mining to Work 11 Process Mining Software........................ 325 11.1 Process Mining Not Included!................... 325 11.2 Different Types of Process Mining Tools............. 327 11.3 ProM: An Open-Source Process Mining Platform ........ 331 11.3.1 Historical Context..................... 331 11.3.2 Example ProM Plug-ins.................. 333 11.3.3 Other Non-commercial Tools............... 337 11.4 Commercial Software....................... 339 11.4.1 Available Products..................... 339 11.4.2 Strengths and Weaknesses................. 345 11.5 Outlook .............................. 352 12 Process Mining in the Large...................... 353 12.1 Big Event Data........................... 353 12.1.1 iV = All .......................... 354 12.1.2 Hardware and Software Developments.......... 356 12.1.3 Characterizing Event Logs ................ 364 12.2 Case-Based Decomposition.................... 368 12.2.1 Conformance Checking Using Case-Based Decomposition....................... 369 12.2.2 Process Discovery Using Case-Based Decomposition . . 370 12.3 Activity-Based Decomposition.................. 373 12.3.1 Conformance Checking Using Activity-Based Decomposition....................... 374 12.3.2 Process Discovery Using Activity-Based Decomposition . 376 12.4 Process Cubes........................... 378 12.5 Streaming Process Mining..................... 381 12.6 Beyond the Hype.......................... 384 Contents xix 13 Analyzing Lasagna Processes .................... 387 13.1 Characterization of Lasagna Processes ............. 387 13.2 UseCases............................. 391 13.3 Approach.............................. 392 13.3.1 Stage 0: Plan and Justify.................. 393 13.3.2 Stage LExtract...................... 395 13.3.3 Stage 2: Create Control-Flow Model and Connect Event Log............................. 395 13.3.4 Stage 3: Create Integrated Process Model......... 396 13.3.5 Stage 4: Operational Support............... 396 13.4 Applications............................ 397 13.4.1 Process Mining Opportunities per Functional Area . . . . 397 13.4.2 Process Mining Opportunities per Sector......... 398 13.4.3 Two Lasagna Processes.................. 402 14 Analyzing Spaghetti Processes ................... 411 14.1 Characterization of Spaghetti Processes ............. 411 14.2 Approach.............................. 415 14.3 Applications............................ 418 14.3.1 Process Mining Opportunities for Spaghetti Processes . . 418 14.3.2 Examples of Spaghetti Processes............. 420 Part VI Reflection 15 Cartography and Navigation ..................... 431 15.1 Business Process Maps ...................... 431 15.1.1 Map Quality........................ 432 15.1.2 Aggregation and Abstraction............... 432 15.1.3 SeamlessZoom...................... 434 15.1.4 Size, Color, and Layout.................. 438 15.1.5 Customization....................... 440 15.2 Process Mining: TomTom for Business Processes?........ 441 15.2.1 Projecting Dynamic Information on Business Process Maps............................ 441 15.2.2 Arrival Time Prediction.................. 444 15.2.3 Guidance Rather than Control............... 444 16 Epilogue................................. 447 16.1 Process Mining as a Bridge Between Data Mining and Business Process Management ....................... 447 16.2 Challenges............................. 449 16.3 Start Today!............................ 451 References .................................. 453 Index..................................... 463
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id	DE-604.BV043636649
illustrated	Illustrated
indexdate	2024-12-20T17:41:14Z
institution	BVB
isbn	9783662498507 9783662570418
language	English
oai_aleph_id	oai:aleph.bib-bvb.de:BVB01-029050562
oclc_num	945132117
open_access_boolean
owner	DE-523 DE-862 DE-BY-FWS DE-703 DE-83 DE-11 DE-2070s DE-861 DE-N2 DE-473 DE-BY-UBG DE-1051 DE-91G DE-BY-TUM DE-860 DE-355 DE-BY-UBR DE-20
owner_facet	DE-523 DE-862 DE-BY-FWS DE-703 DE-83 DE-11 DE-2070s DE-861 DE-N2 DE-473 DE-BY-UBG DE-1051 DE-91G DE-BY-TUM DE-860 DE-355 DE-BY-UBR DE-20
physical	xix, 467 Seiten Illustrationen, Diagramme (teilweise farbig)
publishDate	2016
publishDateSearch	2016
publishDateSort	2016
publisher	Springer
record_format	marc
spellingShingle	Aalst, Wil van der 1966- Process mining data science in action Daten (DE-588)4135391-2 gnd Datenmanagement (DE-588)4213132-7 gnd Big Data (DE-588)4802620-7 gnd Software Engineering (DE-588)4116521-4 gnd Data Mining (DE-588)4428654-5 gnd Prozessmanagement (DE-588)4353072-2 gnd Prozessmodell (DE-588)4237203-3 gnd Betriebliches Informationssystem (DE-588)4069386-7 gnd Informatik (DE-588)4026894-9 gnd
subject_GND	(DE-588)4135391-2 (DE-588)4213132-7 (DE-588)4802620-7 (DE-588)4116521-4 (DE-588)4428654-5 (DE-588)4353072-2 (DE-588)4237203-3 (DE-588)4069386-7 (DE-588)4026894-9
title	Process mining data science in action
title_auth	Process mining data science in action
title_exact_search	Process mining data science in action
title_full	Process mining data science in action Wil van der Aalst
title_fullStr	Process mining data science in action Wil van der Aalst
title_full_unstemmed	Process mining data science in action Wil van der Aalst
title_short	Process mining
title_sort	process mining data science in action
title_sub	data science in action
topic	Daten (DE-588)4135391-2 gnd Datenmanagement (DE-588)4213132-7 gnd Big Data (DE-588)4802620-7 gnd Software Engineering (DE-588)4116521-4 gnd Data Mining (DE-588)4428654-5 gnd Prozessmanagement (DE-588)4353072-2 gnd Prozessmodell (DE-588)4237203-3 gnd Betriebliches Informationssystem (DE-588)4069386-7 gnd Informatik (DE-588)4026894-9 gnd
topic_facet	Daten Datenmanagement Big Data Software Engineering Data Mining Prozessmanagement Prozessmodell Betriebliches Informationssystem Informatik
url	http://deposit.dnb.de/cgi-bin/dokserv?id=a755893269c84c0583616dc17589acbd&prov=M&dok_var=1&dok_ext=htm http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=029050562&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA
work_keys_str_mv	AT aalstwilvander processminingdatascienceinaction

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