Please use this identifier to cite or link to this item: http://localhost/handle/Hannan/2879
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dc.contributor.authorFerreira, Diogo R., ; author ;en_US
dc.date.accessioned2013en_US
dc.date.accessioned2020-05-17T08:41:40Z-
dc.date.available2020-05-17T08:41:40Z-
dc.date.issued2017en_US
dc.identifier.isbn9783319564272 ;en_US
dc.identifier.isbn3319564277 ;en_US
dc.identifier.urihttp://localhost/handle/Hannan/2879-
dc.descriptionen_US
dc.descriptionAvailable to OhioLINK libraries ;en_US
dc.descriptionOhio Library and Information Network ;en_US
dc.descriptionen_US
dc.descriptionen_US
dc.descriptionOriginal ; 9783319564265 ; 3319564269 ; (OCoLC)975368111 ;en_US
dc.descriptionen_US
dc.descriptionen_US
dc.description.abstract"The main goal of this book is to explain the core ideas of process mining, and to demonstrate how they can be implemented using just some basic tools that are available to any computer scientist or data scientist. It describes how to analyze event logs in order to discover the behavior of real-world business processes. The end result can often be visualized as a graph, and the book explains how to use Python and Graphviz to render these graphs intuitively. Overall, it enables the reader to implement process mining techniques on his or her own, independently of any specific process mining tool. An introduction to two popular process mining tools, namely Disco and ProM, is also provided. The book will be especially valuable for self-study or as a precursor to a more advanced text. Practitioners and students will be able to follow along on their own, even if they have no prior knowledge of the topic."-- ; Provided by publisher ;en_US
dc.description.statementofresponsibilityDiogo R. Ferreiraen_US
dc.description.tableofcontentsPreface; Contents; 1 Event Logs; 1.1 Process Model vs. Process Instances; 1.2 Task Allocation; 1.3 Identifying the Process Instances; 1.4 Recording Events in an Event Log; 1.5 Event Logs in CSV Format; 1.6 Reading an Event Log with Python; 1.7 Sorting an Event Log with Python; 1.8 Reading the Event Log as a Dictionary; 1.9 Summary; 2 Control-Flow Perspective; 2.1 The Transition Matrix; 2.2 The Control-Flow Algorithm; 2.3 Implementation in Python; 2.4 Introducing Graphviz; 2.5 Using PyGraphviz; 2.6 Edge Thickness; 2.7 Activity Counts; 2.8 Node Coloring; 2.9 Summary ;en_US
dc.description.tableofcontents3 Organizational Perspective3.1 Handover of Work; 3.2 Implementing Handover of Work; 3.3 Working Together; 3.4 Implementing Working Together; 3.5 Undirected Graphs; 3.6 Edge Thickness; 3.7 Users and Activities; 3.8 Work Distribution; 3.9 Summary; 4 Performance Perspective; 4.1 Dates and Times in Python; 4.2 Parsing the Timestamps; 4.3 Average Timestamp Difference; 4.4 Drawing the Graph; 4.5 Analyzing the Timeline of Events; 4.6 Plotting the Dotted Chart; 4.7 Using Relative Time; 4.8 Activity Duration; 4.9 Summary; 5 Process Mining in Practice; 5.1 The BPI Challenge 2012 ;en_US
dc.description.tableofcontents5.2 Understanding the XES Format5.3 Reading XES with Python; 5.4 Analyzing the Control-Flow Perspective; 5.5 Analyzing the Organizational Perspective; 5.6 Analyzing the Performance Perspective; 5.7 Process Mining with Disco; 5.8 Process Mining with ProM; 5.9 Conclusion; References ;en_US
dc.format.extent1 online resource ;en_US
dc.format.extentIncludes bibliographical references ;en_US
dc.publisherSpringer,en_US
dc.relation.haspart9783319564272.pdfen_US
dc.subjectBusiness ; Data processing ;en_US
dc.subjectPython (Computer program language) ;en_US
dc.subjectGraphic methods ; Data processing ;en_US
dc.subjectComputer graphics ;en_US
dc.titleA primer on process mining :en_US
dc.title.alternativepractical skills with Python and Graphviz /en_US
dc.typeBooken_US
dc.publisher.placeCham, Switzerland :en_US
dc.classification.lcHF5548.2 ;en_US
dc.classification.dc658.4/0380151135 ; 23 ;en_US
Appears in Collections:تمامی گرایش های مدیریت شامل مدیریت بازرگانی و صنعتی

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9783319564272.pdf2.59 MBAdobe PDFThumbnail
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Full metadata record
DC FieldValueLanguage
dc.contributor.authorFerreira, Diogo R., ; author ;en_US
dc.date.accessioned2013en_US
dc.date.accessioned2020-05-17T08:41:40Z-
dc.date.available2020-05-17T08:41:40Z-
dc.date.issued2017en_US
dc.identifier.isbn9783319564272 ;en_US
dc.identifier.isbn3319564277 ;en_US
dc.identifier.urihttp://localhost/handle/Hannan/2879-
dc.descriptionen_US
dc.descriptionAvailable to OhioLINK libraries ;en_US
dc.descriptionOhio Library and Information Network ;en_US
dc.descriptionen_US
dc.descriptionen_US
dc.descriptionOriginal ; 9783319564265 ; 3319564269 ; (OCoLC)975368111 ;en_US
dc.descriptionen_US
dc.descriptionen_US
dc.description.abstract"The main goal of this book is to explain the core ideas of process mining, and to demonstrate how they can be implemented using just some basic tools that are available to any computer scientist or data scientist. It describes how to analyze event logs in order to discover the behavior of real-world business processes. The end result can often be visualized as a graph, and the book explains how to use Python and Graphviz to render these graphs intuitively. Overall, it enables the reader to implement process mining techniques on his or her own, independently of any specific process mining tool. An introduction to two popular process mining tools, namely Disco and ProM, is also provided. The book will be especially valuable for self-study or as a precursor to a more advanced text. Practitioners and students will be able to follow along on their own, even if they have no prior knowledge of the topic."-- ; Provided by publisher ;en_US
dc.description.statementofresponsibilityDiogo R. Ferreiraen_US
dc.description.tableofcontentsPreface; Contents; 1 Event Logs; 1.1 Process Model vs. Process Instances; 1.2 Task Allocation; 1.3 Identifying the Process Instances; 1.4 Recording Events in an Event Log; 1.5 Event Logs in CSV Format; 1.6 Reading an Event Log with Python; 1.7 Sorting an Event Log with Python; 1.8 Reading the Event Log as a Dictionary; 1.9 Summary; 2 Control-Flow Perspective; 2.1 The Transition Matrix; 2.2 The Control-Flow Algorithm; 2.3 Implementation in Python; 2.4 Introducing Graphviz; 2.5 Using PyGraphviz; 2.6 Edge Thickness; 2.7 Activity Counts; 2.8 Node Coloring; 2.9 Summary ;en_US
dc.description.tableofcontents3 Organizational Perspective3.1 Handover of Work; 3.2 Implementing Handover of Work; 3.3 Working Together; 3.4 Implementing Working Together; 3.5 Undirected Graphs; 3.6 Edge Thickness; 3.7 Users and Activities; 3.8 Work Distribution; 3.9 Summary; 4 Performance Perspective; 4.1 Dates and Times in Python; 4.2 Parsing the Timestamps; 4.3 Average Timestamp Difference; 4.4 Drawing the Graph; 4.5 Analyzing the Timeline of Events; 4.6 Plotting the Dotted Chart; 4.7 Using Relative Time; 4.8 Activity Duration; 4.9 Summary; 5 Process Mining in Practice; 5.1 The BPI Challenge 2012 ;en_US
dc.description.tableofcontents5.2 Understanding the XES Format5.3 Reading XES with Python; 5.4 Analyzing the Control-Flow Perspective; 5.5 Analyzing the Organizational Perspective; 5.6 Analyzing the Performance Perspective; 5.7 Process Mining with Disco; 5.8 Process Mining with ProM; 5.9 Conclusion; References ;en_US
dc.format.extent1 online resource ;en_US
dc.format.extentIncludes bibliographical references ;en_US
dc.publisherSpringer,en_US
dc.relation.haspart9783319564272.pdfen_US
dc.subjectBusiness ; Data processing ;en_US
dc.subjectPython (Computer program language) ;en_US
dc.subjectGraphic methods ; Data processing ;en_US
dc.subjectComputer graphics ;en_US
dc.titleA primer on process mining :en_US
dc.title.alternativepractical skills with Python and Graphviz /en_US
dc.typeBooken_US
dc.publisher.placeCham, Switzerland :en_US
dc.classification.lcHF5548.2 ;en_US
dc.classification.dc658.4/0380151135 ; 23 ;en_US
Appears in Collections:تمامی گرایش های مدیریت شامل مدیریت بازرگانی و صنعتی

Files in This Item:
File Description SizeFormat 
9783319564272.pdf2.59 MBAdobe PDFThumbnail
Preview File
Full metadata record
DC FieldValueLanguage
dc.contributor.authorFerreira, Diogo R., ; author ;en_US
dc.date.accessioned2013en_US
dc.date.accessioned2020-05-17T08:41:40Z-
dc.date.available2020-05-17T08:41:40Z-
dc.date.issued2017en_US
dc.identifier.isbn9783319564272 ;en_US
dc.identifier.isbn3319564277 ;en_US
dc.identifier.urihttp://localhost/handle/Hannan/2879-
dc.descriptionen_US
dc.descriptionAvailable to OhioLINK libraries ;en_US
dc.descriptionOhio Library and Information Network ;en_US
dc.descriptionen_US
dc.descriptionen_US
dc.descriptionOriginal ; 9783319564265 ; 3319564269 ; (OCoLC)975368111 ;en_US
dc.descriptionen_US
dc.descriptionen_US
dc.description.abstract"The main goal of this book is to explain the core ideas of process mining, and to demonstrate how they can be implemented using just some basic tools that are available to any computer scientist or data scientist. It describes how to analyze event logs in order to discover the behavior of real-world business processes. The end result can often be visualized as a graph, and the book explains how to use Python and Graphviz to render these graphs intuitively. Overall, it enables the reader to implement process mining techniques on his or her own, independently of any specific process mining tool. An introduction to two popular process mining tools, namely Disco and ProM, is also provided. The book will be especially valuable for self-study or as a precursor to a more advanced text. Practitioners and students will be able to follow along on their own, even if they have no prior knowledge of the topic."-- ; Provided by publisher ;en_US
dc.description.statementofresponsibilityDiogo R. Ferreiraen_US
dc.description.tableofcontentsPreface; Contents; 1 Event Logs; 1.1 Process Model vs. Process Instances; 1.2 Task Allocation; 1.3 Identifying the Process Instances; 1.4 Recording Events in an Event Log; 1.5 Event Logs in CSV Format; 1.6 Reading an Event Log with Python; 1.7 Sorting an Event Log with Python; 1.8 Reading the Event Log as a Dictionary; 1.9 Summary; 2 Control-Flow Perspective; 2.1 The Transition Matrix; 2.2 The Control-Flow Algorithm; 2.3 Implementation in Python; 2.4 Introducing Graphviz; 2.5 Using PyGraphviz; 2.6 Edge Thickness; 2.7 Activity Counts; 2.8 Node Coloring; 2.9 Summary ;en_US
dc.description.tableofcontents3 Organizational Perspective3.1 Handover of Work; 3.2 Implementing Handover of Work; 3.3 Working Together; 3.4 Implementing Working Together; 3.5 Undirected Graphs; 3.6 Edge Thickness; 3.7 Users and Activities; 3.8 Work Distribution; 3.9 Summary; 4 Performance Perspective; 4.1 Dates and Times in Python; 4.2 Parsing the Timestamps; 4.3 Average Timestamp Difference; 4.4 Drawing the Graph; 4.5 Analyzing the Timeline of Events; 4.6 Plotting the Dotted Chart; 4.7 Using Relative Time; 4.8 Activity Duration; 4.9 Summary; 5 Process Mining in Practice; 5.1 The BPI Challenge 2012 ;en_US
dc.description.tableofcontents5.2 Understanding the XES Format5.3 Reading XES with Python; 5.4 Analyzing the Control-Flow Perspective; 5.5 Analyzing the Organizational Perspective; 5.6 Analyzing the Performance Perspective; 5.7 Process Mining with Disco; 5.8 Process Mining with ProM; 5.9 Conclusion; References ;en_US
dc.format.extent1 online resource ;en_US
dc.format.extentIncludes bibliographical references ;en_US
dc.publisherSpringer,en_US
dc.relation.haspart9783319564272.pdfen_US
dc.subjectBusiness ; Data processing ;en_US
dc.subjectPython (Computer program language) ;en_US
dc.subjectGraphic methods ; Data processing ;en_US
dc.subjectComputer graphics ;en_US
dc.titleA primer on process mining :en_US
dc.title.alternativepractical skills with Python and Graphviz /en_US
dc.typeBooken_US
dc.publisher.placeCham, Switzerland :en_US
dc.classification.lcHF5548.2 ;en_US
dc.classification.dc658.4/0380151135 ; 23 ;en_US
Appears in Collections:تمامی گرایش های مدیریت شامل مدیریت بازرگانی و صنعتی

Files in This Item:
File Description SizeFormat 
9783319564272.pdf2.59 MBAdobe PDFThumbnail
Preview File