PPT-Pattern mining in system logs: opportunities for process im

Author : trish-goza | Published Date : 2016-10-10

Dolev Mezebovsky Pnina Soffer and Ilan Shimshoni BPMDS Amsterdam June 2009 The implementation of enterprise systems is often a driver for business process change

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Pattern mining in system logs: opportunities for process im: Transcript

Dolev Mezebovsky Pnina Soffer and Ilan Shimshoni BPMDS Amsterdam June 2009 The implementation of enterprise systems is often a driver for business process change System implementation as an opportunity for redesigning business processes. Mining Frequent Patterns. Ali Javed. CS:332, April 20. th. , 2015. Slides by . Afsoon. . Yousefi. Jiawei. Han, . Jian. Pei and . Yiwen. Yin. . School of Computer Science. Simon Fraser University. Abstract. Although most business processes change over time, contemporary process mining techniques tend to . analyze. these processes as if they are in a steady state. Processes may change suddenly or gradually. The drift may be periodic (e.g., because of seasonal influences) or one-of-a-kind (e.g., the effects of new legislation). For the process management, it is crucial to discover and understand such concept drifts in processes.. Prajwal Shrestha. Department of Computer Science. The . University . of Vermont. Spring 201. 5. Original Authors. This presentation is based on the paper. Zaki. MJ (2002). Efficiently mining frequent trees in a forest. . Inductive Miner. Sander . Leemans. D. . Fahland. W.M.P van der Aalst. Process discovery. S.J.J. Leemans. 2. system. log. process model. Quality. Complete log. =. Sound. Simple. S.J.J. Leemans. 3. System behaviour. Mining Frequent Patterns. Afsoon. . Yousefi. CS:332, March 24. th. , 2014. Inspired by Song Wang slides. Jiawei. Han, . Jian. Pei and . Yiwen. Yin. . School of Computer Science. Simon Fraser University. Discovering Business Rules From Event Logs. Marlon Dumas. University of Tartu, Estonia. With contributions from . Luciano. . García-Bañuelos. , . Fabrizio. . Maggi. & . Massimiliano. de . Leoni. Discovering Business Rules From Event Logs. Marlon Dumas. University of Tartu, Estonia. With contributions from . Luciano. . García-Bañuelos. , . Fabrizio. . Maggi. & . Massimiliano. de . Leoni. Oman. Hilal al-. Busaidi. Public Authority for Mining. Outlines. Mining History in Oman. Presence of Minerals . in Oman. Mining . Sector in Om. an. Mining . Investment . Opportunities. . Mining History in Oman. Chapter 7 : Advanced Frequent Pattern Mining. Jiawei Han, Computer Science, Univ. Illinois at Urbana-Champaign. , 2017. 1. October 28, 2017. Data Mining: Concepts and Techniques. 2. Chapter 7 : Advanced Frequent Pattern Mining. My Administrator Home Page: an administrator has access to a high-level view of his or her staff members’ PD participation. Users click to search for professional development opportunities by framework standards. www.pwc.com. TU/e, . . September 17. th. , 2015. Zbigniew ‘Zibi’ Paszkiewicz, Ph.D.. Manager. System and Process Assurance. Data Assurance Group. zbigniew.paszkiewicz@be.pwc.com. Purpose. Process mining @ PwC. Chapter 6. . Mining Frequent Patterns, Association and Correlations: Basic Concepts and Methods. Jiawei Han, Computer Science, Univ. Illinois at Urbana-Champaign. , . 2017. 1. Chapter 6: Mining Frequent Patterns, Association and Correlations: Basic Concepts and Methods. Instructor: . Yizhou. Sun. yzsun@ccs.neu.edu. January 6, 2013. Chapter 1. : Introduction. Course Information. Class . homepage: . http://. www.ccs.neu.edu/home/yzsun/classes/2013Spring_CS6220/index.htm. Marlon Dumas. University of Tartu, Estonia. With contributions from . Luciano. . García-Bañuelos. , . Fabrizio. . Maggi. & . Massimiliano. de . Leoni. Theory Days, . Saka. , 2013. Business Process Mining.

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