IS353 - Social Networks's Project

Components Architecture

Contents

I - Implemented content:

1. Learn big data deployment technologies.

• Select technology to learn: Google Cloud Platform.
• Overview of GCP.
• Evaluate advantages and disadvantages and applications.
• How to deploy components according to big data architecture, demo examples.

2. Supported big data architecture: Lakehouse analytics selection team

• How to deploy components according to big data architecture, demo example: Reference document links.
• Prepare work assignment tables and plans.
• Implementation support documents (link): data warehouse, data lake, lakehouse.

3. Big data architecture components, demo examples

• Data sources.
• Ingest.
• Process.
• Enrich.

4. Implement the topic

• Select dataset as Stack Overflow data to deploy.
• Run components one after another.
• There are scripts and code included.
• Demo examples, reference documents.

II - Organize DAMH (Project) folder:

1. Document

Project report (including doc files, slides and their pdf versions).

2. Technology

• ReadMe.txt.
• Demo videos. (with subtitles and some videos uploaded to Youtube).
• Scripts, codes corresponding to the running components.

3. Project

• Information describing the selected Dataset.
• Related books have sample code to run to make videos.

4. Final-Video

(All participating members report in the video, open the camera).

5. Plan

• General demo scenario.

There is also a job assignment sheet for each member.

Languages and Tools

REFERENCES

• Google Cloud Platform (GCP) Guide

Study and Reference Materials

1. "Social and Economic Networks: Models and Analysis," Stanford University (Coursera), by Matthew O. Jackson.
2. "Giáo trình Phân tích Mạng Xã Hội và ứng dụng," by PGS.TS. Đỗ Phúc.
3. "Social Network Analysis," by John Scott, SAGE Publications Ltd, 2012.
4. "Social Networks. Development, Evaluation and Influence," Nova Science Publishers, Inc. New York, 2008.
5. "Social Network Analysis: Methods and Applications," Cambridge University Press, Cambridge, New York, 1994.

Software or Tools for Practice

1. Main software: R
2. Other software: Python, Pajek, Gephi, NetLogo.