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. Dora Cai David Clutter Greg Pape Jiawei Han Michael Welge Loretta Auvil Automated Learning Group NCSA University of Illinois at UrbanaChampaign USA Department of Computer Science University of Illinois at UrbanaChampaign USA 1 INTRODUCT Email and news articles are natural examples of suc streams eac haracterized topics that app ear gro in in tensit for erio of time and then fade The published literature in particular researc 57356eld can seen to exhibit similar phenomena er uc long Anthony T. Iannacchione, . engineer. (mining). Stephen J. Tonsor, . biologist. (ecology). Associate Professors, University of Pittsburgh.  . The Question “Can We Conduct Underground Coal Mining and Protect PA Streams?”. 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.). Lesson 1. Bernhard Pfahringer. University of Waikato, New Zealand. 2. Or:. Why . YOU. should care about Stream Mining. Overview. 3. Why is stream mining important?. How is it different from batch ML?. (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. COMP3211 . Advanced Databases. Dr. Nicholas Gibbins – . nmg@ecs.soton.ac.uk. 2014-2015. From Databases to Data Streams. 2. Traditional DBMS makes several assumptions:. persistent data storage. relatively static records. (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. 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.). in Robotics Engineering. Blink . Sakulkueakulsuk. D. . Wilking. , and T. . Rofer. , . Realtime. Object Recognition . Using Decision . Tree . Learning, 2005. . http. ://. www.informatik.uni-bremen.de/kogrob/papers/rc05-objectrecognition.pd. PENTAHO/ WEKAYannis AngelisChannels Information Exploitation DivisionApplication Delivery Sector EFG Eurobank1AgendaBI in Financial EnvironmentsPentahoCommunity PlatformWekaPlatformIntegration with P 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. 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.