Course Code

045102715

Course Title

Big Data Technology

Course Category

Specialty-related Course

Course Nature

Elective Course

Class Hours

Total: 40  laboratorialpractice: 12  experiments: 0  field practice: 0

Credits

2.5

Semester

Seventhterm

Institute

School of Computer Scienceand Technology

Program Oriented

Computer Science andEngineering, Network Engineering, Information Science

Teaching Language

Chinese

Prerequisites

“Computer Network”,“Operation System”, “Program designing” , “DatabaseSystem”

Student Outcomes (SpecialTraining Ability)

Thiscourse contributes to the students’ ability from the aspects asfollows:

1.Engineering knowledge: students will learn the fundamentalknowledge, basic professional principles, methodologies andtechniques. Students will be trained to solve the problems in bigdata management and process by applying mathematics and theirprofessional knowledge in the scope of computer science. Thecourse enhances students’ ability to develop big dataapplications.

2.Problem analysis: students will learn to define, express andanalyze the comprehensive problems in big data engineering bydoing survey and applying mathematics, engineering techniques andtheir professional knowledge in the scope of computer science.

3.Problem solving: students will learn how to find the comprehensivesolutions to the problems in big data engineering including thedesign of big data system, selection of critical techniques,implementation of workflows and planning. Students are promoted ininnovative awareness through considering multiple factors (e.g.,society, environment and security) in their designs.

4.Research ability: students will learn to do research on theproblems in big data engineering by adopting scientificmethodologies including experiments, data analysis and conclusionmaking.

5. Utilizing moderntechniques: students will learn to select, utilize and developtools and techniques available to anticipate and simulate problemsin big data engineering.

Teaching Objectives

Afterfinishing the course:

(1)Students should master the basic knowledge of distributedcomputing techniques, big data processing models, storageplatforms, programming techniques and be trained in problemdiscovering and resolving. [I, II]

(2)Students should master the basic methods and techniques forstoring, processing and analyzing big data. [II, III, IV]

(3) Students should masterwidely-used big data programming and be trained in designing andprogramming simple big data systems. [III, V]

Course Description

This course isprepared for upperclassmen who have a good mastery of the basicsof computer network, operating system, program design and databaseas well as have capability to develop an application. Theobjective of this course is to introduce the basic principles anddevelopment technology of traditional distributed computing, thestorage and management of big data, platform for big data, themodel of big data computing, principles of algorithm to analyzebig data and how to design a framework for big data system as wellas the application development technology. Students in this courseshould to read a lot of relevant literature about big data, inorder to form a perception of the technology. Besides, studentsneed to do some experiment which is necessary to master how to usetools to analyze and program for big data. We hope student candiscover, solve and apply the technology of big data during thereal work instead of just knowing the basic principles of managingbig data platforms or the way to analyze. The knowledge modules ofthe course include basic knowledge of distributed computing,technology of distributed computing programming, technology of bigdata storage platform, computational model for big data, big dataanalysis and processing technology, technology of big dataprograming development, and technology of big data applicationdevelopment.

Instruments and Equipments

Equipment:PC server

Software: JavaDevelopment Kit、HadoopDevelopment Environment

Experiment Report

The method,procedure, process and conclusion of experiment are required

Assessment

ExperimentReport: 50%

ExperimentalOperation: 50%

Teaching Materials andReference Books

SuggestedTextbooks:

林伟伟，刘波编著《分布式计算、云计算与大数据》，机械工业出版社，2017年，第二版次。

MainReferences:

[1]杨正洪著，《大数据技术入门》，清华大学出版社，2016

[2]林子雨编著，《大数据技术原理与应用（第2版）》，人民邮电出版社出版，2017.

[3]张良均等著，《Hadoop大数据分析与挖掘实战》，机械工业出版社，2015

[4]M.L. Liu著，《分布式计算原理和应用》，清华大学出版社，2004

[5]孙宇熙著，《云计算与大数据》，人民邮电出版社，2017

[6]刘鹏著，《大数据》，电子工业出版社，2017

Prepared by Whomand When

Lin Weiwei, 6 July 2017.

No.

ExperimentItem

Class Hours

ContentSummary

Category

Requirements

Number ofStudentsEach Group

Instruments,Equipments and Software

1

Distributed ComputingProgram Design

4

Preparing Client/Server’scommunication program with Socket API or Java RMI, and realize thesimple function of information inquiry (e.g. query the informationof files on the server)

Design

Compulsory

1

PC\Java DevelopmentEnvironment

2

Basic Operation of Big Data

4

Master the basic operationof distributed file system HDFS, be familiar with how the programof MapReduce run, and master the basic operation of HBase databaseand how to use Hive data warehouse, as well as be able to design asimple program for big data storage (e.g. the program to read orstore data from HDFS or HBase)

Demonstration

Compulsory

1-2

PC Server\ HadoopDevelopment Environment

3

The Analysis and Computingof Massive Log Data

4

Query and analyze the logdata by using the tools of MapReduce or Hive which are designedfor this (e.g. discover the preference of users when their surfingthe Internet by analyzing the TOP URL in the log data)

Comprehensive

Compulsory

1-2

PC Server\ HadoopDevelopment Environment

……

……