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Process Mining Techniques in Business Environments [electronic resource] : Theoretical Aspects, Algorithms, Techniques and Open Challenges in Process Mining / by Andrea Burattin.

By: Burattin, Andrea [author.].
Contributor(s): SpringerLink (Online service).
Material type: materialTypeLabelBookSeries: Lecture Notes in Business Information Processing: 207Publisher: Cham : Springer International Publishing : Imprint: Springer, 2015Description: XII, 220 p. 101 illus. online resource.Content type: text Media type: computer Carrier type: online resourceISBN: 9783319174822.Subject(s): Computer science | Management information systems | Industrial management | Data mining | Pattern recognition | Application software | Computer Science | Data Mining and Knowledge Discovery | Business Process Management | Computer Appl. in Administrative Data Processing | Pattern RecognitionAdditional physical formats: Printed edition:: No titleDDC classification: 006.312 Online resources: Click here to access online
Contents:
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.
In: Springer eBooksSummary: 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.
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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.

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.

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