Cargando…
Process Mining Techniques in Business Environments Theoretical Aspects, Algorithms, Techniques and Open Challenges in Process Mining /
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 bel...
| Clasificación: | Libro Electrónico |
|---|---|
| Autor principal: | |
| Autor Corporativo: | |
| Formato: | Electrónico eBook |
| Idioma: | Inglés |
| Publicado: |
Cham :
Springer International Publishing : Imprint: Springer,
2015.
|
| Edición: | 1st ed. 2015. |
| Colección: | Lecture Notes in Business Information Processing,
207 |
| Temas: | |
| Acceso en línea: | Texto Completo |
MARC
| LEADER | 00000nam a22000005i 4500 | ||
|---|---|---|---|
| 001 | 978-3-319-17482-2 | ||
| 003 | DE-He213 | ||
| 005 | 20230810184412.0 | ||
| 007 | cr nn 008mamaa | ||
| 008 | 150506s2015 sz | s |||| 0|eng d | ||
| 020 | |a 9783319174822 |9 978-3-319-17482-2 | ||
| 024 | 7 | |a 10.1007/978-3-319-17482-2 |2 doi | |
| 050 | 4 | |a QA76.9.D343 | |
| 072 | 7 | |a UNF |2 bicssc | |
| 072 | 7 | |a UYQE |2 bicssc | |
| 072 | 7 | |a COM021030 |2 bisacsh | |
| 072 | 7 | |a UNF |2 thema | |
| 072 | 7 | |a UYQE |2 thema | |
| 082 | 0 | 4 | |a 006.312 |2 23 |
| 100 | 1 | |a Burattin, Andrea. |e author. |4 aut |4 http://id.loc.gov/vocabulary/relators/aut | |
| 245 | 1 | 0 | |a Process Mining Techniques in Business Environments |h [electronic resource] : |b Theoretical Aspects, Algorithms, Techniques and Open Challenges in Process Mining / |c by Andrea Burattin. |
| 250 | |a 1st ed. 2015. | ||
| 264 | 1 | |a Cham : |b Springer International Publishing : |b Imprint: Springer, |c 2015. | |
| 300 | |a XII, 220 p. 101 illus. |b online resource. | ||
| 336 | |a text |b txt |2 rdacontent | ||
| 337 | |a computer |b c |2 rdamedia | ||
| 338 | |a online resource |b cr |2 rdacarrier | ||
| 347 | |a text file |b PDF |2 rda | ||
| 490 | 1 | |a Lecture Notes in Business Information Processing, |x 1865-1356 ; |v 207 | |
| 505 | 0 | |a 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. | |
| 520 | |a 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. | ||
| 650 | 0 | |a Data mining. | |
| 650 | 0 | |a Information technology |x Management. | |
| 650 | 0 | |a Pattern recognition systems. | |
| 650 | 1 | 4 | |a Data Mining and Knowledge Discovery. |
| 650 | 2 | 4 | |a Business Process Management. |
| 650 | 2 | 4 | |a Computer Application in Administrative Data Processing. |
| 650 | 2 | 4 | |a Automated Pattern Recognition. |
| 710 | 2 | |a SpringerLink (Online service) | |
| 773 | 0 | |t Springer Nature eBook | |
| 776 | 0 | 8 | |i Printed edition: |z 9783319174839 |
| 776 | 0 | 8 | |i Printed edition: |z 9783319174815 |
| 830 | 0 | |a Lecture Notes in Business Information Processing, |x 1865-1356 ; |v 207 | |
| 856 | 4 | 0 | |u https://doi.uam.elogim.com/10.1007/978-3-319-17482-2 |z Texto Completo |
| 912 | |a ZDB-2-SCS | ||
| 912 | |a ZDB-2-SXCS | ||
| 912 | |a ZDB-2-LNB | ||
| 950 | |a Computer Science (SpringerNature-11645) | ||
| 950 | |a Computer Science (R0) (SpringerNature-43710) | ||