PPT-Mining Data Streams (Part

2 Mining of Massive Datasets Jure Leskovec Anand Rajaraman Jeff Ullman Stanford University httpwwwmmdsorg 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. CS548 Xiufeng . Chen. S. ources. K. . Chitra. , . B.Subashini. , Customer Retention in Banking Sector using . Predictive . Data . Mining Technique. , International Conference on . Information . Technology, . Pedersen, . Kristensen. , . Friberg. , 2014, . PlosOne. , Volume 9, 1-10. Katie Halvorson. Introduction to area. Methods and ideas on project. Results from project. S. ummary. Overview. Catchment areas of Jutland, Denmark. 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.). Emre Eftelioglu. 1. What is Knowledge Discovery in Databases?. Data mining is actually one step of a larger process known as . knowledge discovery in databases. (KDD).. The KDD process model consists of six phases. Streams in karst areas sometimes lose large volumes of surface water through gradual loss into underground caverns. . Environmental Concerns with Karst. More Concerns. Cow manure in streams can lead directly to underground cavern systems.. Ricardo H. Taniwaki, Jeremy J. Piggott, Silvio F. B. Ferraz, Christoph D. Matthaei. Department of Forest Sciences, University of São Paulo, . ESALQ, São Paulo, Brazil.. Department of Zoology, University of Otago, Dunedin, New Zealand.. (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. (Part . 2). 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. with an . Eclipse . Attack. With . Srijan. Kumar, Andrew Miller and Elaine Shi. 1. Kartik . Nayak. 2. Alice. Bob. Charlie. Emily. Blockchain. Bitcoin Mining. Dave. Fairness: If Alice has 1/4. th. computation power, she gets 1/4. Professor Tom . Fomby. Director. Richard B. Johnson Center for Economic Studies. Department of Economics. SMU. May 23, 2013. Big Data:. Many Observations on Many Variables . Data File. OBS No.. Target Var.. Streams Declarative— More concise and readable Composable— Greater flexibility Parallelizable— Better performance What is a stream? A stream is “a sequence of elements from a source that supports data processing operations.” Tao Huang, . Shrideep. . Pallickara. , Geoffrey Fox. Community Grids Lab. Indiana University, Bloomington. . {. taohuang. , . spallick. , . gcf}@indiana.edu. Outline. Analysis of existing Collaboration and Annotation Systems. Skadelik. , CC BY-SA 4.0 <https://creativecommons.org/licenses/by-sa/4.0>, via Wikimedia Commons. Average. major ion. concentrations (mg/L) in North American rivers. Ca. 2+. 21.2. HCO. 3. -. 72.3. http://www.cs.uic.edu/~. liub. CS583, Bing Liu, UIC. 2. General Information. Instructor: Bing Liu . Email: liub@cs.uic.edu . Tel: (312) 355 1318 . Office: SEO 931 . Lecture . times: . 9:30am-10:45am.