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. Written by Amir Kirsh, Edited by Liron Blecher. Agenda. System Parameters. File Class. I/O Streams. Reading from the standard input. Scanner Class. Binary files. Text files and character encoding. 3. Rafal Lukawiecki. Strategic Consultant, Project Botticelli Ltd. rafal@projectbotticelli.co.uk. Objectives. Overview Data Mining. Introduce typical applications and scenarios. Explain some DM concepts. 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. Susan Price. Department of Biological Sciences, York College. Introduction. . Macroinvertebrates. can play an integral role in aquatic environments. These organisms are . detrivores. , meaning they break down leaf matter in which they also live (Freshwater 2004). Although this leaf material provides ample protection for . 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.. Chen Shapira. http://prodlife.wordpress.com. B.Sc. . in CS and . Statistics. OCP. 10 . years of production IT . experience. Oracle Ace. Five successful Streams implementations last year. .. Goal:. . (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. 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. The Backcountry’s resources made it relatively easy to start a small farm.. Region had many streams and . Forests provided wood that settlers used to build log houses. The Backcountry!!!. The Backcountry’s resources made it relatively easy to start a small farm.. 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.” Abaz . Kryemadhi. , Andreas . Mastronikolis, . Konstantin Zioutas. 17. th. . Patras. workshop on . Axions. , WIMPS, and WISPS, . Aug. 8-12, 2022, Mainz Germany. 1. Profited from Mark Vogelsberger.