PPT-Query Suggestions Debapriyo Majumdar

Information Retrieval Spring 2015 Indian Statistical Institute Kolkata Search engines 2 User needs some information Assumption the required information is present somewhere A search engine tries to bridge this gap. 1) Name(In Capital): DR. HEMEN CHANDRA MAJUMDAR 2) Designation: A ssistant Professor 3) Contact No. &Email id: 9864056909, majumdar_h@rediffmail.com 4) Educational Qualifications: M Sc., B. Ed., Ph. Dr. Bjarne Berg . 3. What We’ll Cover …. . Introduction. Performance Issues & Tips. MultiProviders and Partitioning. Aggregates. Query Design & Caching. Hardware & Servers. Designing for Performance. Gu Xu. Microsoft Research Asia. Mismatching Problem. Mismatching is Fundamental Problem in Search. Examples:. NY ↔ New York, game cheats ↔ game . cheatcodes. Search Engine Challenges. Head or frequent queries. Query Optimization Process. (simplified a bit). Parse the SQL query into a logical tree:. identify distinct blocks (corresponding to nested sub-queries or views). . Query rewrite phase:. apply . algebraic transformations. Alicia Wood. What is the . problem. to be solved?. Problem. I. mperfect description of need. Search engine not able to retrieve documents matching query . N. eed accurate and related query substitutions. Dr. Bjarne Berg . 3. What We’ll Cover …. . Introduction. Performance Issues & Tips. MultiProviders and Partitioning. Aggregates. Query Design & Caching. Hardware & Servers. Designing for Performance. Jennifer Rexford. Princeton University. http://. www.cs.princeton.edu. /~. jrex. Joint work with . Srinivas. . Narayana. , Mina . Tahmasbi. , and David Walker. To appear in NSDI’16: . http://. www.cs.princeton.edu. Henry Feild. Center for Intelligent Information Retrieval. University of Massachusetts Amherst. November 28, 2012. Things . we . like to do in . IR. Observe and model user . behavior. Compare search algorithms / interfaces. Yifei Lu. 1. ,. Wei Wang. 1. ,. . Jianxin. Li. 2. . and. . Chengfei. Liu. 2. 1 . University of New South . Wales. 2 . Swinburne University of Technology. XML Keyword Search. 2. Query: . jiawi. . in the . EoR. 21-cm signal using . Bispectrum. Suman Majumdar. Imperial College London. Non-. bispectrum. presentations on non-. Gaussianity. Poster by . Sambit. . Giri. --- “Bubble size statistics from 21-cm . Data Mining – Fall 2014. Indian Statistical Institute Kolkata. November . 20, . 2014. Examples of Streaming Data. Ocean behavior at a point. Temperature (once every half an hour). Surface height (once or more / second). Data Mining – Fall 2014. Indian Statistical Institute Kolkata. August 4 and 7, 2014. Transaction id. Items. 1. Bread, Ham, Juice,. Cheese, Salami, Lettuce. 2. Rice, . Dal, Coconut, Curry leaves, Coffee, Milk, Pickle. Course Goal. The goal of this course is to teach end users of ctcLink PeopleSoft to find and retrieve Queries and Reports in the most effective manner.. Course Learning Objectives. At the end of this course users will:. Chapters 12.4-12.6 and 15.2-15.3 of Cow Book. Arun Kumar. CSE 132C. Database System Implementation. Slide ACKs: Jignesh Patel. Query. Query Result. Database Server. Select R.text from . Report R, Weather W.