PPT-Query Processing Information Retrieval in Practice

Query Processing Documentatatime Calculates complete scores for documents by processing all term lists one document at a time Termatatime Accumulates scores for documents by processing term lists one at a time. 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. 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 . Deliverable #2. Jonggun Park . Haotian. He. Maria . Antoniak. Ron Lockwood. System architecture. Two modules:. Indexing. Querying . query processing. p. assage retrieval. answer processing/ranking. Information Retrieval. Information Retrieval. Konsep. . dasar. . dari. IR . adalah. . pengukuran. . kesamaan. sebuah. . perbandingan. . antara. . dua. . dokumen. , . mengukur. . sebearapa. . 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. All slides ©Addison Wesley, 2008. Information Needs. An . information need. is the underlying cause of the query . that a . person submits to a search . engine. sometimes called . information problem . 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 . All slides ©Addison Wesley, 2008. Retrieval Models. Provide a mathematical framework for defining the search process. includes explanation of assumptions. basis of many ranking algorithms. can be implicit. 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. What is IR?. Sit down before fact as a little child, . be prepared to give up every conceived notion, . follow humbly wherever and whatever abysses nature leads, . or you will learn nothing. . . -- Thomas Huxley --. 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. Fatemeh. Azimzadeh. Books. (Manning et al., 2008). Christopher D. Manning, . Prabhakar. . Raghavan. , and . Hinrich. . Schütze. . Introduction to Information Retrieval. Cambridge University Press, 2008. .