PPT-The use of an intelligent forum crawler for data retrieval

Miloš Pavković and J elica P roti ć University of Belgrade School of Electrical Engineering Belgrade Serbia 6th International Conference on Education and New Learning . 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. Undergraduate Researchers:. Graduate Student Mentor:. Faculty Mentor:. Jordan Cowart, Katie Allmeroth. Krist. . Culmer. Dr. . Wenjun. (Kevin) Zeng. A centrality measure to quantify social capital in a network. ASFPM 2012. Decision, Information and Knowledge. We make decisions everyday. Decisions have consequences. Cereal or waffle for breakfast. Take IH-35 or IH-410. Buy a house on the lake front or on the hills. 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. Indexing. Indexing is the process of transforming items (documents) into a searchable data structure. creation of document surrogates to represent each document. Matthew Hurst. Alexey Maykov. Live Labs, Microsoft. Requirements. Timeliness. Coverage. Scale. Data Quality. Crawling Options. Firehose. Six Apart. Word Press $$$. Feeds. Feeds. Ping servers. Static List of Feeds. (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). and Indexing Models. CSC 575. Intelligent Information Retrieval. Information. need. Index. Pre-process. Parse. Collections. Rank. Query. text input. Lexical analysis and stop words. Result. Sets. How is. 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. copyrights@SporTech. B.I.. 1. (. By Team 02. ). Team members. 12/2/2014. copyrights@SporTech B.I.. 2. Trupti Sardesai. . - . Program Manager. . Zhitao. Zhou . - . Feasibility Analyst. . Subessware. Tianjun. Fu-Department of MIS, University of Arizona Tucson. Ahmed . Abbasi. -Department of MIS Wisconsin-Milwaukee. Hsinchun. Chen-Department of MIS ,University of Arizona . Tuscon. By: Brian Goodwin. web.  spider or . web.  robot) is a program or automated script which browses the World Wide Web in a methodical, automated manner. . Search engines such as Google, Bing etc. uses . web crawlers . Miloš Pavković. . and. . J. elica. . P. roti. ć. , University of Belgrade. School of . Electrical . Engineering, Belgrade, Serbia. 6th International Conference on Education and New . Learning . 15-08-2019. Speaker: . khansa Amrouni. S. upervisors: . Pablo Panero. ,. . Andreas Wagner. Department: . IT-CDA-WF. . Plan. to remove from a surface by usually repeated strokes of an edged instrument..