PPT-Beyond Process Mining:

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Discovering Business Rules From Event Logs Marlon Dumas University of Tartu Estonia With contributions from Luciano GarcíaBañuelos Fabrizio Maggi amp Massimiliano

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Beyond Process Mining:: Transcript

Discovering Business Rules From Event Logs Marlon Dumas University of Tartu Estonia With contributions from Luciano GarcíaBañuelos Fabrizio Maggi amp Massimiliano de Leoni. CS548 Xiufeng . Chen. S. ources. K. . Chitra. , . B.Subashini. , Customer Retention in Banking Sector using . Predictive . Data . Mining Technique. , International Conference on . Information . Technology, . Ian . S. atchwell, Director. Briefing of AusIMM, Perth. 11 . February . 2013. Supporting sustainable resources development. Australian . development assistance . budget is ramping up to OECD standard of. Anthony T. Iannacchione, . engineer. (mining). Stephen J. Tonsor, . biologist. (ecology). Associate Professors, University of Pittsburgh. . The Question “Can We Conduct Underground Coal Mining and Protect PA Streams?”. Chapter 1. Kirk Scott. Iris . virginica. 2. Iris . versicolor. 3. Iris . setosa. 4. 1.1 Data Mining and Machine Learning. 5. Definition of Data Mining. The process of discovering patterns in data.. (The patterns discovered must be meaningful in that they lead to some advantage, usually an economic one.). Chapter 1. Kirk Scott. Iris . virginica. 2. Iris . versicolor. 3. Iris . setosa. 4. 1.1 Data Mining and Machine Learning. 5. Definition of Data Mining. The process of discovering patterns in data.. (The patterns discovered must be meaningful in that they lead to some advantage, usually an economic one.). Do Lord We’ve got a home in glory land… 1. Overview . This presentation is for chapter 16 which discuss :. Chapter . 16: Text Mining for Translational . Bioinformatics. 1- terminologies.. 2- definitions.. 2-uses cases and applications.. 3-evaluation techniques and evaluation metrics.. Rafal Lukawiecki. Strategic Consultant, Project Botticelli Ltd. rafal@projectbotticelli.co.uk. Objectives. Overview Data Mining. Introduce typical applications and scenarios. Explain some DM concepts. Discovering Business Rules From Event Logs. Marlon Dumas. University of Tartu, Estonia. With contributions from . Luciano. . García-Bañuelos. , . Fabrizio. . Maggi. & . Massimiliano. de . Leoni. (Part 1). Mining of Massive Datasets. Jure Leskovec, . Anand. . Rajaraman. , Jeff Ullman . Stanford University. http://www.mmds.org . Note to other teachers and users of these . slides:. We . would be delighted if you found this our material useful in giving your own lectures. Feel free to use these slides verbatim, or to modify them to fit your own needs. Edited . by. . Julia Sagebien. SBA Dalhousie University and EGAE University . of Puerto . Rico. . Nicole Marie . Lindsay. School . of Communication,. Simon . Fraser . University. Governance Ecosystems . 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. Marlon Dumas. University of Tartu, Estonia. With contributions from . Luciano. . García-Bañuelos. , . Fabrizio. . Maggi. & . Massimiliano. de . Leoni. Theory Days, . Saka. , 2013. Business Process Mining. Credit: Gaby . Matalon. What is Data Mining?. The. . process . of analyzing data from different perspectives and summarizing it into useful information. It . uncovers patterns . in a large set of data.

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