PPT-Beyond Process Mining: Discovering Business Rules From Event Logs

Marlon Dumas University of Tartu Estonia With contributions from Luciano GarcíaBañuelos Fabrizio Maggi amp Massimiliano de Leoni Theory Days Saka 2013 Business Process Mining. An Approach Based on . ILP. Massimiliano de . Leoni. Wil M. P. van der Aalst. Conformance . Checking. PAGE . 1. model. conformance. . /. . A. B. event log. event . stream. DB. extract. solved for control-flow. 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.. 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.). 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. 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.). 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.). 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. Katanosh Morovat. This concept is a formal approach for identifying the rules that encapsulate the structure, constraint, and control of the operation as a one package. Business Rules declare . business structure or behavior of the business. 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. . Global . and Local Association Rules. Abhishek Mukherji*. . Elke . A. . . Rundensteiner Matthew . O. . Ward. Department of Computer Science, Worcester Polytechnic Institute, MA, USA. Overview of Functionality. Event Business Rules Engine . Overview. The Event Business Rules Engine enables admins to build custom business rules to assist event organizers when planning an event. This helps to minimize planning mistakes and to ensure compliance. For example:. interesting . and . useful. information from Web . content. and . usage . data. What is Web Mining?. Web mining is . a data . mining . technique . to extract knowledge from . web data. . . Web data includes : .