PPT-A simple method for multi-relational outlier detection

Sarah Riahi and Oliver Schulte School of Computing Science Simon Fraser University Vancouver Canada With tools that you probably have around the house lab A simple method for multirelational outlier detection. Roddick and David MW Powers School of Informatics and Engineering Flinders University PO Box 2100 Adelaide South Australia 5001 Abstract Outlier or anomaly detection is an important problem for many domains including fraud detec tion risk analysis n Outlier Detection. Ayushi Dalmia. *. , Manish Gupta. *+. , Vasudeva Varma. *. 1. IIIT Hyderabad, India* Microsoft, India. +. Introduction. A. B. B. B. B. A. B. B. B. A. C. C. C. X. 1. Subgraphs from . Network Datasets. Manish . Gupta. UIUC. Microsoft. , India. Arun. . Mallya. , . Subhro. Roy. Jason Cho, Jiawei . Han. Motivation (1). Query based subgraph outlier detection. A security officer may like to find some tiny but . Nothing is so practical as a good theory. Kurt Lewin, 1945. The relational model. Overcame shortcomings of earlier database models. Has a strong theoretical base. Codd was the major developer. Problems with other models. Jonathan Kuck. 1. , . Honglei. Zhuang. 1. , . Xifeng. Yan. 2. , Hasan Cam. 3. , . Jiawei. Han. 1. 1. University of Illinois at Urbana-Champaign. 2. University of California at Santa Barbara. 3. US Army Research Lab. OVERVIEW OF CODD’s RULE. A . relational database management system (RDBMS).  is a database management system (DBMS) that is based on the relational model as introduced by . E. F. . Codd. . . A short definition of an RDBMS may be a DBMS in which data is stored. Analysis. . . Kai-Wei Chang. Joint work with. . Scott Wen-tau . Yih, Chris Meek. Microsoft Research. Natural Language Understanding. Build an intelligent system that can interact with human using natural language. data mining approach . to flag unusual schools. Mayuko Simon. Data Recognition Corporation. May, 2012. 1. Statistical methods for data forensic. Univariate. distributional techniques: e.g., average wrong-to-right erasures.. Outlier Detection. Ayushi Dalmia. *. , Manish Gupta. * . , Vasudeva Varma. *. 1. IIIT Hyderabad, India* Microsoft, India. . Introduction. A. B. B. B. B. A. B. B. B. A. C. C. C. X. 1. 9. Introduction to Data Mining, . 2. nd. Edition. by. Tan. , Steinbach, Karpatne, . Kumar. With additional slides and modifications by Carolina Ruiz, WPI. 11/20/2018. Introduction to Data Mining, 2nd Edition. Analysis. . . Kai-Wei Chang. Joint work with. . Scott Wen-tau . Yih, Chris Meek. Microsoft Research. Natural Language Understanding. Build an intelligent system that can interact with human using natural language. “Anomaly Detection: A Tutorial”. Arindam. . Banerjee. , . Varun. . Chandola. , . Vipin. Kumar, Jaideep . Srivastava. , . University of Minnesota. Aleksandar. . Lazarevic. , . United Technology Research Center. 14. . World-Leading Research with Real-World Impact!. CS 5323. Outline. Anomaly detection. Facts and figures. Application. Challenges. Classification. Anomaly in Wireless.  . 2. Recent News. Hacking of Government Computers Exposed 21.5 Million People. Denis Krompaß. 1. , Maximilian Nickel. 2. and Volker Tresp. 1,3. 1. . Department of Computer Science. Ludwig Maximilian University, . 2. MIT, Cambridge and . Istituto. . Italiano. . di. . Tecnologia.