웹기술 및 응용:

Course Syllabus

2019년도 2학기

Instructor: Prof. Young-guk HaDept. of Computer Science & Engineering

Contents

• Introduction

• Major Topics

• Term Project

• Course Material

• Grading Policy

• Class Schedule

• Contact Information

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Course Overview

• Course title– “웹기술 및 응용”

• Objective– 웹 기반 컴퓨팅의 개념 및 기초 지식을 학습

• XML, Web Services, REST

– 웹 기반 컴퓨팅 시스템 개발에 대한 기초 학습 및 실습– 웹 기반 컴퓨팅 최신 기술 동향 및 이슈에 대한 이해

• 머신 러닝(Machine Learning) 기반의 지능형 웹 기술에 대한 지식 습득

• Lecture time– 수요일 오후 2:30 ~ 오후 4:30 / 목요일 오후 1:30 ~ 오후 3:30

• Lecture room– 신공학관 1213호

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Introduction toWeb-Based Computing (1)

• What is Web-Based Computing?– Web Technology를 기반으로 하는 분산 컴퓨팅 기술– For Human-to-Machine interaction (traditionally)

• Essential technologies– W3C standards: URL, HTML, HTML5

HTTP, CGI, …– Web Servers & Web Browsers

(e.g., IE, Safari, Chrome, FireFox, …)– Java: JavaScript,

JSON, JSP, JQuery,AJAX, …

– Web UI Frameworks– ASP, Flash, …

Human ReadableWeb Pages in HTML

HTML

Web-based computing forHuman-to-Machine interaction

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HTTP

• Today’s Web-Based Computing– 다양한 Mobile Smart Device, Cloud Service 및 IoT의 등장– 보다 Intelligent, Interactive 및 Dynamic한 특성을 가짐– Increasing requirements for Machine-to-Machine interaction

à HTML & CGI are not enough

• Essential technologies– XML (eXtensible Markup

Language)– Web Services: WSDL, SOAP,

REST, …– Open APIs: Facebook,

Google, Naver, …– Semantic Web: RDF, OWL, …– And so on

Introduction toWeb-Based Computing (2)

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HTML

Web Agents HTML

Hard to parsesemantics of Web pages

Hard to adapt tochanges of Web pages

MobileApps

Cloud

Today’s Web-Based Computing

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CloudServices

Social MediaServices

Web-Based

Open

API

InformationServices

RESTful

Interfaces

Smart & IoT

Devices

XML

XML

XML

XML

XML

XML

What’s Next?

• “Intelligent Web = AI + Web-Based Computing”

– Traditional approach: Semantic Web• Web 자체를 하나의 거대한 지식베이스화 함 (웹 온톨로지)• 의미추론 기반 웹 검색 (Retrieval), 웹서비스 자동실행 (Execution)

및 웹서비스 자동조합(Composition) 등이 가능해 짐• 아직 실현되기에는 극복해야 할 한계가 많으며, 이론 연구 및 소규

모 실증 수준에서 머물고 있음

– Todays: Machine Learning• 최근 기계학습(i.e., Deep Learning) 기술의 발전 및 실용화에 힘입어

웹과 기계학습 기술을 접목하는 연구가 활발해짐• 웹 기반 서비스에 빅데이터 및 기계학습 기술을 접목하여 다양한 지

능형(사용자 맞춤형) 서비스 및 클라우드 기반 지능형 서비스를 제공하고 있음 (Google Assistant, Amazon Alexa, Apple Siri, MS Cortana, SKT Nugu 등)

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Intelligent Web-Based Services:Echo(Alexa) and Lynx

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What’s behind the Echo?

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EchoDevice

Amazon Cloud

Internet

Open

API

Ultimate Goal of Web Intelligence:Semantic Web & Semantic Web Services

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Semantic Web(Ontology,

RDF, RDFS, OWL)

Java, REST,Web Services

(XML, WSDL, SOAP)

Intelligence

Interaction“Bringing the web to its full potential”

Semantic Web Services(OWL-S)

Web(URL, HTML, HTTP,Browser, Web Server)

“Web as a Global Scale Knowledge Base”

Technology Stack forSemantic Web Services

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OWL-S (Web Ontology Language for Services)

Service Profile Service Model Service Grounding

Semantic Web Services

HTTP/HTML

Unicode and URI/URL (Uniform Resource Identifier/Locator)

OWL (Web Ontology Language)

RDF (Resource Description

Framework) and RDF Schema

Semantic Web

WSDL(Web Services Description Language)

SOAP (Simple Object Access Protocol)

REST(Representational State Transfer)

Web Services

XML (eXtensible Markup Language) and Namespaces

Major Topics (1)

• XML (eXtensible Markup Language)– XML basics

• Introduction• Document structure• Basic syntax

– XML document models• DTD (Document Type Definition)• XML Schema

– XML document processing• DOM (Document Object Model)• SAX (Simple API for XML)• XML Path language (XPath)

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Major Topics (2)

• Web Interfaces and Open API– Web Services

• Basic architecture (SOA)• Core technologies (WSDL, SOAP, UDDI)

– REST (Representational State Transfer)• REST architecture• RESTful API design

– Open APIs• Google, Facebook, Naver, … • 공공데이터포털, 기상청, 한국도로공사, …

– Etc• JSON (JavaScript Object Notation)• Flask

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Major Topics (3)

• Machine Learning– Introduction to machine learning

• What is ANN (Artificial Neural Network)?• What is deep learning?• Deep learning models: CNN, RNN, YOLO

– Python• Basics on Python language• Programming practice with Python

– PyTorch• Open source machine learning library for Python• Deep learning practice with PyTorch

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Term Project• 지능형 웹 서비스 시스템 구현

– Base technologies: XML, HTML, REST, JSON– Programming frameworks: Java, JavaScript, Python– Open APIs: Google, Facebook, Naver 등– Operating systems: Android, iOS, Linux, Windowsv 주 1: 관련 Open source를 적극 활용v 주 2: C++, C#, .NET, ActiveX, ASP, Flash 등은 사용 불가

• 텀 프로젝트 진행 절차1) Project proposal2) Progress report3) Final report 및 demonstration

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Course Material

• 강의 자료– PPT를 이용하여 강의 진행– 강의 자료는 수업시간전에

과목홈페이지에서 다운로드– URL:

http://sclab.konkuk.ac.kr/lecture/4

• References– 각종 Web 표준 및 Spec.:

http://www.w3c.org– Web에서 다운로드할 수

있는 관련 Open 소스 및Document를 활용

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Grading Policy• Midterm exam: 30%

– Programming exam

• Final exam (Project): 60%– Proposal: 15%– Progress report: 15%– Final report and demonstration: 30%

• Class attendance: 10%– 2회 지각 = 1회 결석– 5회 결석 = 출석점수 0점

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Class ScheduleWeek Major Topics

Week 1 Course Syllabus

Week 2 Introduction to Web-Based Computing / XML Basics

Week 3 XML DTD & Schema 1

Week 4 XML DTD & Schema 2

Week 5 XML DOM & SAX 1

Week 6 XML DOM & SAX 2

Week 7 XPath

Week 8 Midterm exam

Week 9 Project proposal

Week 10 Web Services

Week 11 REST & Open API

Week 12 Progress report

Week 13 Python 1

Week 14 Python 2

Week 15 Machine Learning & PyTorch

Week 16 Final report & demonstration

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Contact Information

• Instructor: 하영국 교수– Office: 공학관 C동 291-2호– Phone: 02-450-3273 (내선 3273)– Email: ygha@konkuk.ac.kr– Office hour: 수업 후 1시간 (또는 사전 연락 후 상담)

• Teaching assistant: 이명재– Office: 신공학관 1216호 (대학원 SCLab 연구실)– Email: dualespresso@naver.com

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