CSci 8370
Advanced Database Systems

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

• SIGMOD / SIGMOD Archive , ICDE / ICDE Archive , VLDB / VLDB Archive , ISWC / ISWC Archive , ICWS / ICWS Archive , SCC / SCC Archive , BigData Congress / BigData Congress Archive , BigData Conference / BigData Conference Archive .

Additional Notes

• Conflict Serializability
• View Serializability
• Semantic Web
  • RDF - TURTLE notation
  • RDF Graph
  • Reasoning
  • OWL
  • SPARQL
• The Elements of Statistical Learning
• An Introduction to Statistical Learning

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	.

Homework

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.

• Scala 3
• Simple Build Tool (SBT)

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	.	.	.	.	.	.	.	.

See GDBMS for example papers.

• Tuesday Lecture - on Tutorial/Survey Paper; Assign Homework Problem
• Wednesday Lecture - on Research Paper, which should be readable, closely related to the topic of Term Project.
• Thursday Lecture - on your Research Plan; Homework Presented

Term Projects (by Group)

Group	Topical Area	Research Paper Title	Abstract	Target Conference	Presentation Date
G1	.	.	.	.	.
G2	.	.	.	.	.
G3	.	.	.	.	.
G4	.	.	.	.	.
G5	.	.	.	.	.

Policies

• Late Points - 10 points off per day late.
• Make-Up Tests - requires (i) written pre-approval for travel/time conflicts or (ii) written explanation for illness.
• A Culture of Honesty -- Examples.
• Copyright Issues -- Regents Guide to Understanding Copyright.

Academic Honesty:

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:

• If you or someone you know needs assistance, you are encouraged to contact Student Care and Outreach in the Division of Student Affairs at 706-542-7774 or visit https://sco.uga.edu/. They will help you navigate any difficult circumstances you may be facing by connecting you with the appropriate resources or services.
• UGA has several resources for a student seeking mental health services https://www.uhs.uga.edu/bewelluga/bewelluga or crisis support https://www.uhs.uga.edu/info/emergencies.
• If you need help managing stress anxiety, relationships, etc., please visit BeWellUGA https://www.uhs.uga.edu/bewelluga/bewelluga for a list of FREE workshops, classes, mentoring, and health coaching led by licensed clinicians and health educators in the University Health Center.
• Additional resources can be accessed through the UGA App.

Coronavirus Information for Students