Summary

This course deals with modeling and management of various types of big data and knowledge from the data-driven service life cycle perspective. Students learn modern technologies of ingestion, storage, distribution, and processing of big data in parallel and distributed cloud computing environment. Final project involves a group of students collecting real-world big data and build a data science analysis using relational and graph databases. Students report their own solutions on data science problems and demonstrate feasibility of the solutions.

• Historical development of data models and data management systems

• Structured data management and relational data model

• Relational data storage and meta data management, and query processing

• Transaction management and database recovery management

• Spatio-temporal data, graph data, semi-structured and unstructured data, and knowledge structures

• Parallel and distributed big data systems and complex analytics processing and machine learning on cloud infrastructure

• Big data and model life cycle management

Grading scheme

Attendance 5%

Assignment 30%

Midterm exam 25%

Final exam 25%

Project 15%

Content

1. Introduction

   • 1. 1. 1. 1. • Real-world data and knowledges structures, and their life cycle

                 • Data management of Structured and Unstructured data

                 • Query, Complex Analytics, and Machine Learning

                 • Transactions, Concurrency Control, Logging, and Recovery

                 • HW development and Real-world demands driving Technology Paradigm Changes: Relational DBMS, In-Memory Platform, Ambient AI

2. First Order Logic, PROLOG

   • 1. 1. 1. 1. • First Order Logic Fundamentals, Translating Knowledge into Logic

                 • Resolution Algorithm, Unification

                 • PROLOG Syntax, PROLOG Tutorial (Hanoi Tower, RDBMS, Graph)

3. Natural Language Processing in PROLOG

   • 1. 1. 1. 1. • Debugging and Tracing in Prolog

                 • Natural Language Analysis in Prolog

4. Frequent Graph Structures and Applications, Graph Storage and Operators -Neo4j

5. Neo4j in Depth

6. Spatial/Temporal Type in Neo4j

7. Relational DBMS

   • 1. 1. 1. 1. • Relational Algebra and Query Language

                 • Physical Tables: Row vs Column Stores, Indexes

                 • Why we need Virtual Table(View)

8. SQL: Data Definition Language, Data Manipulation Language, Data Control Language, Transactions: ACID Properties, Isolation Levels

9. Index as Redundant Structures for Fast Access: B+-Tree

10. Logical and Physical Query Plan, Relational Query Optimization

11. In-Memory Data Management Architecture, Column Store

12. Dictionary Encoding for Compression in Column Store

13. In-Memory Complex Analytical Query Processing

14. Transaction Processing, Concurrency Control - Locking and Latching, Multi-Version Concurrency Control(MVCC), Index Concurrency Control (with OLFIT)

15. Logging & Recovery, and Replication for High- Availability

16. Distributed Systems for Scalability: Shared-Nothing Partitioning, Distributed Transaction and Query Processing, Distributed Computing for Cloud and Edge System

17. Data Cleansing, ETL(Extract-Transform-Load)

18. Distributed Workflow Management and Long Transactions