'Specification file' diaporamas de présentation

Product Ads Merchant Integration Guide

639 views • 54 slides

Data Structures and Algorithms Week 3 Data Abstraction Part I: Modules and Information Hiding

Data Structures and Algorithms Week 3 Data Abstraction Part I: Modules and Information Hiding. A Module. The use of modularization is part of the process of decomposing a single, large program. Modularization happens naturally when using stepwise refinement

559 views • 45 slides

Ideas for Test Improvement in PostgreSQL

Galy Lee galylee@gmail.com. Ideas for Test Improvement in PostgreSQL. Contents. Introduction Replication Test Framework Kernel Test Framework Concurrency Test Framework Feedback and suggestions. Introduction.

542 views • 44 slides

Software Tools, Part 2

Software Tools, Part 2. Fall, 2002. Overview. Toolkits Prototyping Languages Frameworks Groupware Frameworks GroupKit THYME Wrap Up. Toolkits. Ease development by providing fully-fleshed out UI widgets A UI component has two major parts The UI presentation (a button)

436 views • 34 slides

Compiler Principle and Technology

Compiler Principle and Technology. Prof. Dongming LU Mar. 26th, 2014. 5. Bottom-Up Parsing. PART THREE. Contents. PART ONE 5.1 Overview of Bottom-Up Parsing 5.2 Finite Automata of LR(0) Items and LR(0) Parsing PART TWO 5.3 SLR(1) Parsing 5.4 General LR(1) and LALR(1) Parsing

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CASA Parallelization and High Performance Computing

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CS 111 - Introduction to Data Structures

CS 111 - Introduction to Data Structures. Spring Term 2004 Franz Hiergeist. CS 111 - A continuation of CS 110 C++ is the vehicle to implement our ideas Data Structures Algorithms Recursion Prerequisite: Successful completion of CS 110

319 views • 19 slides

CS 111 - Introduction to Data Structures

CS 111 - Introduction to Data Structures. Spring Term 2004 Franz Hiergeist. CS 111 - A continuation of CS 110 C++ is the vehicle to implement our ideas Data Structures Algorithms Recursion Prerequisite: Successful completion of CS 110

298 views • 19 slides

Extracting Semistructured Information from the Web

Extracting Semistructured Information from the Web J. Hammer, H. Garcia-Molina, J. Cho, R. Aranha, A. Crespo from Stanford University Presented by: Wei Mao. Introduction: Background Fast growing of WWW Semistructured data in web pages Difficulty with manipulating web data

226 views • 11 slides