PPT-Query Decomposition and Data Localization

Bhavana ganne 20 Srilekha vuyurru 21 Prasanna Kumar reddy kukkala 22 Krishna teja talasila 19 GROUP 5 226 235 Agenda Query Decomposition Data Localization . Dr. Bjarne Berg . 3. What We’ll Cover …. . Introduction. Performance Issues & Tips. MultiProviders and Partitioning. Aggregates. Query Design & Caching. Hardware & Servers. Designing for Performance. Partitioning and Mapping to Processes. Shantanu. . Dutt. ECE Dept., UIC. Acknowledgements. Adapted from:. Chapter 3 slides, by A. . Grama. , of the text: . Principles of Parallel Algorithm . Design by . - . or. -. How to Spoof Your Location with a Tin Can. Kevin Bauer. , Damon McCoy, Eric Anderson, Markus Breitenbach, . Greg Grudic, Dirk Grunwald, and Douglas Sicker. University of Colorado. . IEEE GLOBECOM 2009 December 3, 2009 . WELCOME. to the inaugural. Game Localization Summit @ GDC. Co-Advisors of the Game Localization Summit and chairs of the . IGDA Localization SIG:. Miguel Á. Bernal-Merino . (Roehampton University), SIG Vice-Chair. 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. Learning Objectives. Understand why WSNs need localization protocols. Understand localization protocols in WSNs. Understand secure localization protocols. Prerequisites. Module 7. Basic concepts of network security. Two Problems, . One Open Standards Based Solution. Phil Ritchie, CTO, . VistaTEC. Current Approach. Reviewer is often instructed on how much time to spend or amount of content to review.. This amounts to a random, human judgement on what to look at.. Dr. Bjarne Berg . 3. What We’ll Cover …. . Introduction. Performance Issues & Tips. MultiProviders and Partitioning. Aggregates. Query Design & Caching. Hardware & Servers. Designing for Performance. Parallel Processing (CS453) . . How . does one decompose a task into various subtasks? . . While there is no single recipe that works for all problems, we present a set of commonly used techniques that apply to broad classes of problems. These include: . David Johnson. cs6370. Basic Problem. Go from this to this. [Thrun, Burgard & Fox (2005)]. . Kalman . Filter. [Thrun, Burgard & Fox (2005)]. . Kalman Limitations. Need initial state and confidence. Matching on Large Graphs in Cloud. Zhao Chang*, Lei . Zou. †, . Feifei. Li*. *University of Utah, USA . †. Peking University, China. Outline. Background. K-. Automorphism. Subgraph. Matching. Steven L. . Brunton1, . Joshua L. Proctor2, J. Nathan . Kutz1. ，. Journal . of Computational Dynamics, Submitted . in. DEC,2013. .. Dynamic Mode Decomposition. Fluid simulation is very important in engineering. in that most fluid mechanical systems of interest are described by the same governing equations: the . Fola Olagbemi. Outline. What is the sieve of Eratosthenes?. Algorithm used. Parallelizing the algorithm . Data decomposition options for the parallel algorithm . Block decomposition (to minimize inter-process communication). 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:.