PPT-Online Techniques for dealing with Concept Drift in Process mining

J Carmona R Gavaldà UPC Barcelona Spain 1 Outline The Advent of Process Mining PM T he challenge of Concept Drift CD Key ingredients Online strategy for CD in PM Experiments Work in progress. Zhimin. He. iTechs. – ISCAS. 2013-03-21. Agenda. What’s Concept Drift. Causes of a Concept Drift. Types of Concept Drift. Detecting and Handling Concept Drift. Implications for Software Engineering Research. 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.. Data Mining. Data Triangle. Quotes. “Drowning in data but starving for knowledge”. “Computers . have promised us a fountain of wisdom but delivered a flood of data”. Definition. According to Encyclopedia Britannica…. 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. . Chapter. 12-1. Pan-. gaea. . “. all. - earth. ” . Note: Our world today is actually a 2’nd or 3’rd generation “super continent” or “. pangea. ” !!!. . Alfred Wegener . SVD & CUR. 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. Part 1. Learning Objectives. 1. Describe how variation within groups is maintained and how variation among groups is maintained.. 2. . Describe modern human biological diversity and articulate an informed position on the question of biological races of humans. . 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. When there is a single program running in the CPU, it leads to the degradation of the CPU utilization.. Example: When a running program initiates an I/O operation, the CPU remain idle until the I/O operation is completed.. Tiffany. . Chiu,. . Yunsen. Wang. . and. . Miklos. . Vasarhelyi. Rutgers 18th Fraud Seminar, December 7. th. This paper aims at providing a framework on how process mining can be applied to identify fraud schemes and assessing the riskiness of business processes. . 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. M., Fiore . G., . Gorini. E., . Mazzotta. P., . Miccoli. A., Innocente A., Rocco R., . S. ekhniaidze. G.. 24/06/2013. 1. SUMMARY. TARGET. CONCEPT DESIGN. COMPONENTS CONSTRUCTION. NEXT STEP. 2. TARGET. Eva García-. Martín. . eva.garcia.martin@bth.se. Thesis. Make Machine Learning algorithms more energy efficient. Machine. . learning. Algorithms that automatically learn with experience. How can one learn from data and process evolving data in only one pass?.