PPT-Dealing With Concept Drifts in Process Mining

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 eg because of seasonal influences or oneofakind eg the effects of new legislation For the process management it is crucial to discover and understand such concept drifts in processes. . . . Lynne Anderson, FACHE, cphrm rhia. May 14, 2015. 1. 2. introduction. . We have all had to deal with some type of complaint (patient, family, staff member). Risk management synonymous with complaint management. Sensitive Processes through Process . Mining. Jorge . Munoz-Gama . and. . Isao . Echizen. Insuring . Sensitive Processes through Process . Mining. Insuring Scenario. Insurance Company. 3. Clients. 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. How to represent a document. Represent by a string?. No semantic meaning. Represent by a list of sentences?. Sentence is just like a short document (recursive definition). CS@UVa. CS 6501: Text Mining. 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. IN LOW TRAFFIC VOLUME ROADS IN KENYA. DESIGN AND . CONSTRUCTION GUIDELINES. KITUI TRAINING. 10TH APRIL 2017. Eng. Benson M. . Masila. KENYA RURAL ROADS AUTHORITY . KENYA. Order of Presentation. Definition. 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. . Marlon Dumas. University of Tartu, Estonia. With contributions from . Luciano. . García-Bañuelos. , . Fabrizio. . Maggi. & . Massimiliano. de . Leoni. Theory Days, . Saka. , 2013. Business 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. 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. Bamshad Mobasher. DePaul University. 2. From Data to Wisdom. Data. The raw material of information. Information. Data organized and presented by someone. Knowledge. Information read, heard or seen and understood and integrated.