PPT-Web Archive Information Retrieval

Miguel Costa Daniel Gomes speaker Portuguese Web Archive Information Retrieval is the activity of obtaining information resources relevant to an information need from a collection. John . Halquist. Development and Operations Officer. National Operational Hydrologic Remote Sensing Center (NOHRSC). HIC Meeting – Feb 2010. Major categories for Archive use. General Archive requirements. 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. 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. INST 734. Module 3. Doug . Oard. Agenda. Ranked retrieval. Similarity-based ranking. Probability-based ranking. Boolean Retrieval. Strong points. Accurate, . if you know the right strategies. Efficient for the computer. 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 . 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 . Hongning. Wang. CS@UVa. Recap: Core IR concepts. Information need. “. an individual or group's desire to locate and obtain information to satisfy a conscious or unconscious need. ” – wiki. An IR system is to satisfy users’ information need. 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. All slides ©Addison Wesley, 2008. How Much Data is Created Every . Minute?. Source: . https. ://www.domo.com/blog/2012/06/how-much-data-is-created-every-minute/. The Search Problem. Search and Information Retrieval. 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. Web Science Course. 2. What to Expect. Information Retrieval Basics. IR Systems. History of IR. Retrieval Models. Vector Space Model. Information Retrieval on the Web. Differences to traditional IR. Selected Papers. Universität des Saarlandes, Saarbrücken. Winter Semester 2011/12. The Course. Lecturers. Teaching Assistants. October 18, 2011. I.. 2. IR&DM, WS'11/12. D5: Databases & Information Systems Group . Cal Poly Pomona. Today. Who I am. CS 599 educational objectives (and why). Overview of the course, and logistics. Quick overview of IR and why we study it. 2. Who am I?. Instructor : . Sampath . Jayarathna.