PPT-Overview of Information Retrieval and Organization

CSC 575 Intelligent Information Retrieval 2 Source Intel How much information Google 100 PB a day 3 million servers 15 Exabytes stored Wayback Machine has 9 PB 100 . CSC 575. Intelligent Information Retrieval. Intelligent Information Retrieval. 2. Retrieval Models. Model is an idealization or abstraction of an actual process. in this case, process is matching of documents with queries, i.e., retrieval. Module 20. What is Memory?. Process of . encoding, storage, and retrieval.. Learning that persists over time.. Encoding. Get info in. Storage. Retain the info. Retrieval. Getting the information out. Hui Fang , Tao . Tao. , . ChengXiang. . Zhai. University of Illinois at Urbana Champaign. SIGIR 2004 Best Paper. Presented by Lingjie Zhang. Outline. Formal Definitions of Heuristic Retrieval Constraints. Introduction. Ling 573. NLP Systems and Applications. March 29, 2011. Roadmap. Motivation. 573 Structure. Question-Answering. Shared Tasks. Motivation. Information retrieval is very powerful. Search engines index and search enormous doc sets. and Re-ranking. Ling573. NLP Systems and Applications. May 3, 2011. Upcoming Talks. Edith Law. Friday: 3:30; CSE 303. Human Computation: Core Research Questions and Opportunities . Games with a purpose, . Introduction. Ling 573. NLP Systems and Applications. April 1, 2013. Roadmap. Motivation. 573 Structure. Question-Answering. Shared Tasks. Motivation. Information retrieval is very powerful. Search engines index and search enormous doc sets. (for MODIS). Andy Harris. Jonathan . Mittaz. Prabhat. . Koner. (Chris Merchant, Pierre . LeBorgne. ). Satellite data – pros and cons. Main advantages of satellite data. Frequent and regular global coverage (cloud cover permitting for IR). ChengXiang. (“Cheng”) . . Zhai. Department of Computer Science. University of Illinois at Urbana-Champaign. http://www.cs.uiuc.edu/homes/czhai. . Email: czhai@illinois.edu. 1. Yahoo!-DAIS Seminar, UIUC. Hongning. Wang. CS@UVa. What is information retrieval?. CS6501: Information Retrieval. CS@UVa. 2. Why information retrieval . Information overload. “. It refers to the . difficulty. a person can have understanding an issue and making decisions that can be caused by the presence of . CSC 575. Intelligent Information Retrieval. Intelligent Information Retrieval. 2. Web Mining. Today. Overview of Web Data Mining. Web Content Mining / Text Mining. Web Usage Mining. Web Personalization. BY. DR. ADNAN ABID. Lecture # . Introduction. Library Management System. Structured Data Storage / Tables. Semi-Structured and Unstructured . Employee Department Salary. Library Digitization. Information Retrieval Models. Class Activity. Action: . Write. down the . 4 or 5 points. that most impact you from this presentation.. You will need this for the group activity at the end of this presentation.. Key to Learning. Rosalia F. Tungaraza. Advisor: Prof. Linda G. Shapiro. Ph.D. Defense. Computer Science & Engineering. University of Washington. 1. Functional Brain Imaging. Study how the brain works . Imaging while subject performs a task . Retrieval Practice: Lesson 3. 1. What is an . autobiography. ?.  .  2. Does Roald Dahl consider . Boy . to be an . autobiography. ? Why or why not?.  .  3. What is an . anecdote. ?.  . 4. Describe one .