PPT-Advanced Query Processing

Dr Susan Gauch Query Term Weights The vector space model matches queries to documents with the inner productcosine similarity measure Query vector Document vector inner product Normalizedqvector. Dr. Bjarne Berg . 3. What We’ll Cover …. . Introduction. Performance Issues & Tips. MultiProviders and Partitioning. Aggregates. Query Design & Caching. Hardware & Servers. Designing for Performance. 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 . Embedded CPU-GPU Architectures. Xuntao Cheng. , Bingsheng He, Chiew Tong Lau. Nanyang Technological University, Singapore. 1. Outline. Motivations. System Design. Evaluations. Conclusion. 2. Query Processing in the Era of IoT. for the Novice User. Davide Mottin, PhD Defense. April 28th, 2015. Advisors. Yannis. . Velegrakis. , University of Trento. Themis . Palpanas. , . Paris Descartes University. Department of. Information Engineering and Computer Science. 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.. Query Formulation. Ling573. NLP Systems and Applications. April 14, 2011. Roadmap. Motivation:. Retrieval gaps. Query F. ormulation:. Question Series. Query reformulation:. AskMSR. patterns. MULDER parse-based formulation. A query is stated using SQL or other languages, ideally natural languages. After query parsing, each query is essentially treated as a . relational algebra expression. Query optimizer . Enumerates . the possible plans to evaluate expression,. Dr. Bjarne Berg . 3. What We’ll Cover …. . Introduction. Performance Issues & Tips. MultiProviders and Partitioning. Aggregates. Query Design & Caching. Hardware & Servers. Designing for Performance. IDOL Under . the . Hood. Techniques and algorithms. #. SeizeTheData. Read the presenter notes before presenting.. Most of the content of this presentation is contained therein.. IDOL knowledge flow. Acquisition. SpatialHadoop. Type: Research Paper (Experimental evaluation). Authors: Ahmed . Eldawy. , . Louai. . Alarabi. , Mohamed F. . Mokbel. Presented by: Siddhant Kulkarni. Term: Fall 2015. Motivation. in a Data Stream Management . System. Introduction. Two . fundamental . differences between DSMS and DBMS. In . addition to managing traditional stored data . such as . relations, a DSMS must handle multiple . A query is stated using SQL or other languages, ideally natural languages. After query parsing, each query is essentially treated as a . relational algebra expression. Query optimizer . Enumerates . the possible plans to evaluate expression,. on OpenCL-based FPGAs. Zeke Wang. , Johns Paul, Hui Yan Cheah . (. NTU,. . Singapore), . Bingsheng He (. NUS,. Singapore), . Wei Zhang (HKUST, Hong Kong). 1. Outline. Background and Problem. Challenges. 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:.