| 000 | 02611nam a2200277 i 4500 | ||
|---|---|---|---|
| 003 | MIUC | ||
| 005 | 20200310144931.0 | ||
| 008 | 170522s2015 sz a|||| |||| 000 | eng d | ||
| 020 | _a9783319174815 | ||
| 040 |
_aMIUC _beng _cMIUC |
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| 082 | 0 | _a658.4 | |
| 100 | 1 |
_93526 _aBurattin, Andrea |
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| 245 | 1 | 0 |
_aProcess mining techniques in business environments : _btheoretical aspects, algorithms, techniques and open challenges in process mining / _cAndrea Burattin. |
| 260 |
_aSwitzerland : _bSpringer, _c2015. |
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| 300 |
_aXII, 220 p. : _bill. b&w ; _c24 cm. |
||
| 336 |
_2rdacontent _atext |
||
| 490 | 1 |
_aLecture Notes in Business Information Processing, _v18651348 ; _x207 |
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| 500 | _aThe 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. | ||
| 504 | _aIncludes bibliographical references. | ||
| 505 | 0 | _a1. Introduction -- Pt. 1. 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 -- Pt. 3. 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 -- Pt. 4. A New Challenge in Process Mining -- 17. Process Mining for Stream Data Sources -- Pt. 5. Conclusions and Future Work -- 18. Conclusions and Future Work. | |
| 520 | _aAfter 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." | ||
| 650 | 0 |
_9794 _aData mining |
|
| 650 | 0 |
_91040 _aProcess control _xData processing |
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| 830 | 0 |
_93527 _aLecture notes in business information processing _v207 |
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| 942 |
_2ddc _cBK |
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