PPT-Mining High-Speed Data Streams

Presented by William Kniffin Pedro Domingos Geoff Hulten Sixth ACM SIGKDD International Conference 2000 Outline Introduction Hoeffding Trees The VFDT System Performance Study. 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 NOV. 12. th. . , 2013 : New Delhi. LARSEN & TOUBRO. REENERGISING INDIAN COAL SECTOR. Trends. Mining Equipment Size. Equipment Operating Systems. Equipment Tracking Systems. Equipment Management Systems.. 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?”. Presented by: Tyler J. Sawyer. UVM Spring 2014 - CS 332 Data Mining. Pedro Domingos. Geoff Hulten. Sixth ACM SIGKDD International Conference - 2000. Outline. → Introduction. → Hoeffding Trees. → The VFDT System. mindbogglingly. big they are.. Massive data streams. Douglas Adams – Hitchhiker’s Guide to Big Data. Massive Data Streams . SDO (The devil we know!). 650 TB of data a year. Storable / Distributable. NOV. 12. th. . , 2013 : New Delhi. LARSEN & TOUBRO. REENERGISING INDIAN COAL SECTOR. Trends. Mining Equipment Size. Equipment Operating Systems. Equipment Tracking Systems. Equipment Management Systems.. 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. Desmond O’Connor. spatial data analyst. What is it about?. its about …. exploiting existing spatial data sources. utilise GPS data & patterns within the data. relating these patterns to road geometry. 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. Project Description . Design and develop a camera system that can capture, package, and transmit high-speed still image and video data in a compact ruggedized housing which may be subjected to extreme cold, heat, and moisture-laden environments..