After a brief presentation of the state of the art of process-mining techniques, Andrea Burratin proposes different scenarios for the deployment of process-mining projects, and in particular a characterization of companies in terms of their process awareness. The approaches proposed in this book belong to two different computational paradigms: first to classic "batch process mining," and second to more recent "online process mining." The book encompasses a revised version of the author's PhD thesis, which won the "Best Process Mining Dissertation Award" in 2014, awarded by the IEEE Task Force on Process Mining.…mehr
After a brief presentation of the state of the art of process-mining techniques, Andrea Burratin proposes different scenarios for the deployment of process-mining projects, and in particular a characterization of companies in terms of their process awareness. The approaches proposed in this book belong to two different computational paradigms: first to classic "batch process mining," and second to more recent "online process mining." The book encompasses a revised version of the author's PhD thesis, which won the "Best Process Mining Dissertation Award" in 2014, awarded by the IEEE Task Force on Process Mining.
Produktdetails
Produktdetails
Lecture Notes in Business Information Processing 207
1 Introduction.- Part I: State of the Art: BPM, Data Mining and Process Mining.- 2 Introduction to Business Processes, BPM, and BPM Systems.- 3 Data Generated by Information Systems (and How to Get It).- 4 Data Mining for Information System Data.- 5 Process Mining.- 6 Quality Criteria in Process Mining.- 7 Event Streams.- Part II: Obstacles to Process Mining in Practice.- 8 Obstacles to Applying Process Mining in Practice.- 9 Long-term View Scenario.- Part III: Process Mining as an Emerging Technology.- 10 Data Preparation.- 11 Heuristics Miner for Time Interval.- 12 Automatic Configuration of Mining Algorithm.- 13 User-Guided Discovery of Process Models.- 14 Extensions of Business Processes with Organizational Roles.- 15 Results Interpretation and Evaluation.- 16 Hands-On: Obtaining Test Data.- Part IV: A New Challenge in Process Mining.- 17 Process Mining for Stream Data Sources.- Part V: Conclusions and Future Work.- 18 Conclusions and Future Work.
1 Introduction.- Part I: State of the Art: BPM, Data Mining and Process Mining.- 2 Introduction to Business Processes, BPM, and BPM Systems.- 3 Data Generated by Information Systems (and How to Get It).- 4 Data Mining for Information System Data.- 5 Process Mining.- 6 Quality Criteria in Process Mining.- 7 Event Streams.- Part II: Obstacles to Process Mining in Practice.- 8 Obstacles to Applying Process Mining in Practice.- 9 Long-term View Scenario.- Part III: Process Mining as an Emerging Technology.- 10 Data Preparation.- 11 Heuristics Miner for Time Interval.- 12 Automatic Configuration of Mining Algorithm.- 13 User-Guided Discovery of Process Models.- 14 Extensions of Business Processes with Organizational Roles.- 15 Results Interpretation and Evaluation.- 16 Hands-On: Obtaining Test Data.- Part IV: A New Challenge in Process Mining.- 17 Process Mining for Stream Data Sources.- Part V: Conclusions and Future Work.- 18 Conclusions and Future Work.
1 Introduction.- Part I: State of the Art: BPM, Data Mining and Process Mining.- 2 Introduction to Business Processes, BPM, and BPM Systems.- 3 Data Generated by Information Systems (and How to Get It).- 4 Data Mining for Information System Data.- 5 Process Mining.- 6 Quality Criteria in Process Mining.- 7 Event Streams.- Part II: Obstacles to Process Mining in Practice.- 8 Obstacles to Applying Process Mining in Practice.- 9 Long-term View Scenario.- Part III: Process Mining as an Emerging Technology.- 10 Data Preparation.- 11 Heuristics Miner for Time Interval.- 12 Automatic Configuration of Mining Algorithm.- 13 User-Guided Discovery of Process Models.- 14 Extensions of Business Processes with Organizational Roles.- 15 Results Interpretation and Evaluation.- 16 Hands-On: Obtaining Test Data.- Part IV: A New Challenge in Process Mining.- 17 Process Mining for Stream Data Sources.- Part V: Conclusions and Future Work.- 18 Conclusions and Future Work.
1 Introduction.- Part I: State of the Art: BPM, Data Mining and Process Mining.- 2 Introduction to Business Processes, BPM, and BPM Systems.- 3 Data Generated by Information Systems (and How to Get It).- 4 Data Mining for Information System Data.- 5 Process Mining.- 6 Quality Criteria in Process Mining.- 7 Event Streams.- Part II: Obstacles to Process Mining in Practice.- 8 Obstacles to Applying Process Mining in Practice.- 9 Long-term View Scenario.- Part III: Process Mining as an Emerging Technology.- 10 Data Preparation.- 11 Heuristics Miner for Time Interval.- 12 Automatic Configuration of Mining Algorithm.- 13 User-Guided Discovery of Process Models.- 14 Extensions of Business Processes with Organizational Roles.- 15 Results Interpretation and Evaluation.- 16 Hands-On: Obtaining Test Data.- Part IV: A New Challenge in Process Mining.- 17 Process Mining for Stream Data Sources.- Part V: Conclusions and Future Work.- 18 Conclusions and Future Work.
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