CSci 8370
Advanced Database Systems

John A. Miller
Fall 2022


Textbooks

M: Introduction to Computational Data Science Using ScalaTion
John A. Miller, 2020 (see the August 16, 2022 version).

H: Knowledge Graphs, Hogan et al., 2021.

B: Demystifying Graph Databases: Analysis and Taxonomy of Data Organization, System Designs, and Graph Queries, Besta et al. (revised 2021).

LVB: Principles of Database Management: The Practical Guide to Storing, Managing and Analyzing Big and Small Data,
By Lemahieu, Wilfried; Vanden Broucke, Seppe; Baesens, Bart, 2018.

EN: Fundamentals of Database Systems, 7th Edition,
Ramez Elmasri and Shamkant B. Navathe, 2016.

Kutner: Applied Regression Analysis, Chapter 13: Introduction to Nonlinear Regression and Neural Networks,
Kutner and Nachtsheim and Neter, 2016.


Class Time

Day Period 8 Period 76
  4:10 am-5:00 pm 3:55 am-5:10 pm
Tuesday
no
yes
Wednesday
yes
no
Thursday
no
yes
Room Boyd 306 Boyd 306


Course Description

This is an advanced course on database systems and related information technology. Topics vary year to year.

Current Focus: Graph Databases, Knowledge Graphs, Machine Learning Related to Graphs


12 Topics for Final Exam

  1. Graph Database Model - Labeled Property Graph (LPG)
  2. Graph Database Query Languages
  3. Graph Algebra
  4. Graph Database Query Processing
  5. Knowledge Graphs from RDF
  6. Knowledge Graphs from LPG
  7. Graph Algorithms - e.g., Neo4j's Graph Data Science Library
  8. Neural Networks
  9. Convolutional Networks
  10. Knowledge Graph Completion
  11. Graph Embedding
  12. Graph Neural Networks


Course Topics

Topic Text URL
NoSQL M: Ch. 4, EN: Ch. 24 NoSQL
-> Columnar Databases M: Ch. 4, EN: 24.6 C-Store
-> Graph Databases M: Ch. 5, EN: 24.5 Neo4j
--> Graph Database Literatures GDBMS
-> Document Databases EN: 24.3 MongoDB
Parallel and Distributed Databases EN: Ch. 23 Massively Parallel Databases and MapReduce Systems
Big Data EN: Ch. 25 Big Data: A Survey
-> Hadoop EN: 25.2-5 Apache Hadoop
-> Spark . Apache Spark
-> ScalaTion . ScalaTion Project
Analytics/Data Mining M: Chs: 8-13, EN: Ch. 28 .
-> Nonlinear Regression and Neural Networks M: Ch. 11, EN: 28.5, Kutner Chapter 13 analytics
-> Forecasting M: Ch. 12, EN: 28.5 forecaster
-> Classification M: Chs. 8, 9, EN: 28.3 classifier
-> Clustering M: Ch. 13, EN: 28.3 clusterer

Potential Topics from Top-Tier Research Conferences


Additional Notes


Grading

Weight Item Due Date
20% Final 12/?
10% Homework see below
20% Group Programs see below
10% Group Lecture see below
40% Group Project see below
-- 10% -- 25 min. Presentation .
-- 10% -- 8 min. Demo .
-- 20% -- Research Paper .
Research Paper: format and target for a particular research conference.


Homework

(Subject to Change)

Number Name Description Due Date
1 HW-1 M: section 4.5 Exercises - all Thur 8/27
2 HW-2 M: section 5.5 Exercises - all Thur 9/3
3 HW-3 . .
4 HW-4 . .
5 HW-5 . .
6 HW-6 . .
7 HW-7 . .
8 HW-8 . .
9 HW-9 . .

Each student should present one homework solution to the class.


Programs (by Group)

Program Description Restrictions Due Date
PG1 Finish Coding of (1) Table, (2) LTable, (3) VTable, (4) GTable or (5) PGraph. See Appendix C.5 pages 679-680 in M: Textbook. . .
PG2 Test Efficiency of Query Processing for your software chosen in PG1. Compare with Neo4j and MySQL. . .

Each group must demo and submit each programming assignment (e-mail zip file to jam@cs.uga.edu).

Coded in Scala 3.2.0-RC3+, requires Java 17+ or an approved JVM-based language.

Simulation, Optimization and Analytics Using ScalaTion 2.0

See Code Samples


Student Lectures (by Group)

Group Topical Area Topic Example Tutorial Paper (pdf) Research Paper (pdf) Lecture Notes (pdf) HW Problem Lecture Dates
G1 . . . . . . . .
G2 . . . . . . . .
G3 . . . . . . . .
G4 . . . . . . . .
G5 . . . . . . . .
Each group should provide lecture material (ideally via a Web page). Each group will give three lectures with all members participating. Two goals: (i) teach the class about an important research area and (ii) provide background information for your term project. Each group must develop one homework problem on the material they teach that will help the students study for the Final. URLs for lecture notes, tutorial paper, research paper and homework problem should be ready before the group's first lecture.

See GDBMS for example papers.


Term Projects (by Group)

Group Topical Area Research Paper Title Abstract Target Conference Presentation Date
G1 . . . . .
G2 . . . . .
G3 . . . . .
G4 . . . . .
G5 . . . . .
Research paper title, abstract and target conference due before group lecture. Research paper due 11/?.


Policies


Academic Honesty:

All students are responsible for maintaining the highest standards of honesty and integrity in every phase of their academic careers. The penalties for academic dishonesty are severe and ignorance is not an acceptable defense. For more detail, please see the UGA policy at: https://honesty.uga.edu/Academic-Honesty-Policy/

School of Computing Policy Statement on Academic Honesty

The Computer Science Department recognizes honesty and integrity as necessary to the academic function of the University. Therefore all students are reminded that the CS faculty requires compliance with the conduct regulations found in the University of Georgia Student Handbook. Academic honesty means that any work you submit is your own work.

Common forms of academic dishonesty against which students should guard are:

1. Copying from another student's test paper or laboratory report, or allowing another student to copy from you;

2. Fabricating data (computer, statistical) for an assignment;

3. Helping another student to write a laboratory report or computer software code that the student will present as his own work, or accepting such help and presenting the work as your own;

4. Turning in material from a public source such as a book or the Internet as your own work.

Three steps to help prevent academic dishonesty are:

1. Familiarize yourself with the regulations.

2. If you have any doubt about what constitutes academic dishonesty, ask your instructor or a staff member at the Office of Judicial Programs.

3. Refuse to assist students who want to cheat.

All faculty, staff and students are encouraged to report all suspected cases of academic dishonesty. All cases of suspected academic dishonesty (cheating) will be referred to the Office of the Vice President for Instruction. Penalties imposed by the Office of Judicial Programs may include a failing grade in the course and a notation on the student's transcript. Repeated violations are punishable by expulsion from the University. For further information please refer to the UGA Code of Conduct, available at the URL below.

https://honesty.uga.edu/Academic-Honesty-Policy/

Mental Health and Wellness Resources:


Coronavirus Information for Students

UGA Coronavirus (COVID-19) Information and Resources