PPT-Query Evaluation

Chapter 11 External Sorting Why Sort A classic problem in computer science Data requested in sorted order eg find students in increasing gpa order Sorting is first step in bulk loading . Group 4: . Nipun . Garg, Surabhi . Mithal. http://www-users.cs.umn.edu/~smithal/. Chapter Organization. 2. OLD Organization. 5.1 . Evaluation of Spatial Operations . 5.2 . Query Optimization . 5.3 . Paraschos. . Koutris. Prasang. . Upadhyaya. Magdalena . Balazinska. Bill Howe. Dan . Suciu. University of Washington. PODS 2012. Motivation. Data is increasingly . sold. and . bought. on the web. Nicholas Del . Rio. University of Texas at El Paso Computer Science. Workshop Objectives. U. nderstand . notion . and purpose of visualization . queries. Understand why writing queries may be easier that writing visualization programs. Michael . Armbrust. , Kristal Curtis, Tim . Kraska. Armando Fox, Michael J. Franklin, David A. Patterson. AMP Lab, EECS, UC Berkeley. Introduction. Large-scale websites are increasingly moving from relational databases to distributed key-value stores.. CSE 8330. Instructor: . Dr.Margaret. H. Dunham. Presenter: . Akshaya. . Aradhya. Introduction. Query optimization in XML databases. Query optimization in Parallel databases. Comparison. Conclusion and Future work. Matt Masson. Tessa Palmer. DBI-B410. Agenda. Overview of Power . Query. Why you would use the Power Query Formula Language (“M. ”). Introduction to the . Language. Types, values, and . functions. Davide Mottin, University of Trento. Francesco . Bonchi. , Yahoo Labs - Francesco . Gullo. , Yahoo Labs. Issues. with Pattern . S. earch. O. OH. O. S. Query. 510 . matches. T. oo. . many. . matches. 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. Jianfeng Gao, MSR. (Joint work with . Jian. Huang, . Jiangbo. Miao, Xiaolong Li, Kuansan Wang and Fritz Behr). Outline. N-gram language model (LM) ABC. N-gram LM at Microsoft. Bing-It-On-. Ngram. Building Web scale N-gram LM. Ruirui. Li, Ben Kao, Bin Bi, . Reynold. Cheng, Eric Lo. Speaker:. . Ruirui. Li. 1. The University of Hong Kong. Outline. Motivation. Problem Statement. DQR Model. Experiments & Evaluation. 2. Date. . :. . 2013/08/20. Source. . :. . SIGIR’13. Authors : . Weize. Kong and James . Allan. Advisor . : . Dr.Jia. -ling, . Koh. Speaker : . Wei, Chang. 1. Outline. Introduction. Approach. Experiment. Jie Tang. *. , Limin Yao. #. , and Dewei Chen. *. *. Dept. of Computer Science and Technology. Tsinghua University. #. Dept. of Computer Science, University of Massachusetts Amherst. April, 2009. ?. What are the major topics in the returned docs?. of Massive Trajectory Data based on MapReduce. Qiang. Ma, Bin Yang (. Fudan. University). Weining. . Qian. , . Aoying. Zhou (ECNU). Presented By: . Xin. Cao (Aalborg University). Outline. Introduction . 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:.