コンテンツに飛ぶ | ナビゲーションに飛ぶ

  • 日本語
  • English
 
現在位置: ホーム ja シラバス(2020年度) 工学研究科 デザイン学分野 分散情報システム

分散情報システム

JA | EN

科目ナンバリング
  • G-ENG76 63217 LE11
  • G-ENG76 63217 LE13
  • G-ENG76 63217 LE10
開講年度・開講期 2020・後期
単位数 2 単位
授業形態 講義
配当学年 博士
対象学生 大学院生
使用言語 英語
曜時限 水3
教員
  • 吉川 正俊(情報学研究科 教授)
  • 馬 強(情報学研究科 准教授)
授業の概要・目的 This course gives an overview of major topics on distributed information systems. The course starts with a topic on complex data. Unlike flat tables employed by relational databases, modern information systems manage complex data. Students will learn data models which have rich expressive power to model complex data, and declarative languages to retrieve and update complex data. The course also covers highly-scalable distributed file systems and databases. The systems covered in lectures include HDFS, MapReduce, and Dremel. Column store technologies are also covered as an important storage model for handling OLAP tasks on high-volume data. Blockchain, an emerging technology, is also introduced. The last topic is Web mining and knowledge discovery. The fundamental technologies and application systems will be introduced. Some other contemporary topics are lectured if time allows.
到達目標 Our goal is to introduce students to principles and techniques of distributed information systems. Students are expected to obtain fundamental knowledge on representation, management, processing and mining of large amount of distributed data.
授業計画と内容 Distributed and Parallel Information Systems (8 Lectures by Yoshikawa)
Complex Data
. Nested Data, Complex Value, Semi-Structured Data, XML
Highly-Scalable Distributed File Systems and Databases
. Column Store
. Dremel
. HDFS (Hadoop Distributed File System) and MapReduce
Blockchain
Foundation of Semantic Web

Knowledge Discovery (Web Mining) (7 Lectures by Ma)
. Content Mining: Information Extraction, Information Integration (Schema Matching)
. Structure Mining: Link analysis, Social Network Analysis
. Usage Mining: log analysis, personalization, user behavior analysis, HCI
. Sentiment Analysis and Opinion Mining
. Application Systems
成績評価の方法・観点 Grading method: Grade is evaluated by writing examination and reports.
履修要件 Basic knowledge of database systems and data mining.
授業外学習(予習・復習)等 In some lectures, homework is assigned. Course review is highly recommended.
教科書
  • Lecture notes and related documents will be distributed in lectures
参考書等
  • Several related documents will be introduced in lectures