CS 6530-001 Fall 2025 Adv. Database Systems

CS 6530: Advanced Database Systems (Subject to Minor Changes)

Tue/Thu, 10:45-12:05pm; WEB L112

  • Instructor: Anna Fariha
    Office Location: WEB 2851
    Office hours: Tue/Thu: 12:05 - 1:00 PM
  • TA:
    • TBD

TA Office Hours & Location

Day Time TA Location
Mon
Tue
Wed 
Thu
Fri

 

Class Schedule and Important Dates

Holidays:  Follow the academic calendar.

Exam, Quiz, and Homework Dates

Event Note Due/Event Date
(all due dates are 11:59 PM MT)		 Weight
Quiz 1 In class, Duration: 20 mins Sep 4, Thu 4%
Quiz 2 In class, Duration: 20 mins Sep 25, Thu 4%
Homework 1

Topic: TBD

Release: Sep 1

 Oct 3, Fri 15%
Quiz 3 In class, Duration: 20 mins Oct 23, Thu 4%
Homework 2

Topic: TBD

Release: Oct 1

 Oct 31, Fri 15%
Quiz 4 In class, Duration: 20 mins Nov 13, Thu 4%
Late Mid Term In class, Duration: 70 minutes Nov 20, Thu 30%
Quiz 5 In class, Duration: 20 mins Dec 2, Tue 4%
Presentation
  • Present a topic in a group of 3 members.
  • 15 minutes per group [12 min presentation + 2 min Q&A + 1 min transition]
  • Select topic here.
 10%
Project TBD
  • Proposal due: Oct 1 (Wed)
  • Final project due: Dec 5 (Fri)
 10%
Class participation for Bonus Points

In-class quiz
  • 16 classes will have an in-class quiz
  • Each worth 0.25 bonus points
 4%

 

Lectures

Event Date Topic Additional Materials

Lecture 1

Slides

Scribe

Video

 Aug 19
  • Introduction
  • AI & DBMS: Automation and Self-Optimization

Lecture 2

Slides

Scribe

Video

 Aug 21
  • Data Modeling (ER)
  • Relational DB

Lecture 3

Slides

Scribe

Video

 

 Aug 26
  • Relational Algebra (SQL)
  • Datalog

Lecture 4

Slides

Scribe

Video

 Aug 28
  • Indexing
  • In-memory indexing
  • Learned Index

Lecture 5

Slides

Scribe

Video

 Sep 2
  • Storage

Lecture 6

Slides

Scribe

Video

 Sep 4
  • Quiz 1
  • Disks and Buffers

Lecture 7

Slides

Scribe

Video

 Sep 9
  • Row vs Column Stores
  • Query Processing & Optimization
  • Join Algorithms

Lecture 8

Slides

Scribe

Video

Sep 11
  • Cardinality Estimation
  • AI for Query Optimization

Lecture 9

Slides

Scribe

Video

 Sep 16
  • Transactions

Lecture 10

Slides

Scribe

Video

 Sep 18
  • Concurrency Control
  • Logging and Recovery

Lecture 11

Slides

Scribe

Video

 Sep 23
  • Theory Problems in Databases
  • Expressiveness & Complexity of Language

Lecture 11

Slides

Scribe

Video

 Sep 25
  • Quiz 2
  • Query Equivalence
  • Views

Lecture 12

Slides

Scribe

Video

 Sep 30
  • Diverse Query Results
  • Reverse Data Management

Lecture 13

Slides

Scribe

Video

 Oct 2
  • Natural Language Processing (NLP) for Database Queries
  • Structured queries over unstructured data: images, video, natural language, etc.

Lecture 14

Slides

Scribe

Video

 Oct 14
  • Data Profiles (Functional Dependencies, Denial Constraints)
  • Data Cleaning

Lecture 15

Slides

Scribe

Video

 Oct 16
  • Database Usability: Human-centric/Human-in-the loop  Data Management

Oct 21
  • Database Tuning

Lecture 13

Slides

Scribe

Video

 Oct 23
  • Quiz 3
  • Self-driving/Autonomous Database (self-provisioning, self-optimizing, self-repairing)

Lecture 14

Slides

Scribe

Video

 Oct 28
  • Differential Privacy and Database Security
  • AI in Database Security

 

 Oct 30
  • Presentations

Lecture 15

Slides

Scribe

Video

 Nov 4
  • Probabilistic Databases

Lecture 16

Slides

Scribe

Video

 Nov 6
  • Approximate Query Processing

Lecture 17

Slides

Scribe

Video

 Nov 11
  • Mid-Term

Lecture 18

Slides

Scribe

Video

 Nov 13
  • Causality and Data Debugging
  • Simpson's Paradox

Lecture 19

Slides

Scribe

Video

 Nov 18
  • Retrieval Augmented Generation (RAG)

Lecture 20

Slides

Scribe

Video

 Nov 20
  • Quiz 4
  • Data Provenance

Lecture 21

Slides

Scribe

Video

 Nov 25
  • Data Augmentation and Data Fusion
  • Data Lakes & Data Discovery
  • Bloom Filters, MinHash

Lecture 22

Slides

Scribe

Video

 Dec 2
  • Quiz 5
  • Vector Databases

Lecture 23

Slides

Scribe

Video

 Dec 4
  • Ethics and Fairness in Data Management
  • Responsible Data Management

Course Description

Course Type: In Person
Description: This graduate-level course covers the design and implementation of relational database system kernels and advanced data management techniques. Topics include relational models, SQL, indexing (in-memory, learned), storage (row vs. column stores), and query processing. It also explores AI-driven database optimization, automatic database tuning (self-driving databases), transactions, concurrency control, logging, and recovery. The course discusses modern application of AI in DBMS, focusing on self-optimization, security, NLP for database queries, and human-centric data management. Additional topics include differential privacy, probabilistic databases, data provenance, retrieval-augmented generation (RAG), vector databases, data lakes, and query result diversification. Ethical considerations in data management are also discussed. Students will engage in hands-on projects, implementing core database modules and exploring modern large-scale data management techniques. Please note that this is NOT a course on building database applications and introduction to database systems, i.e., we will not cover in this course how to build a database application (e.g., ER design, schema refinement, functional dependency, and database application development). Such topics will be covered in CS 5530. Also, this course will have almost no overlap with the special topics course Human-centered Data Management (CS 3960/6959).

Recommended Prerequisites

  • CS 2420 (Introduction to Algorithm & Data Structure)
  • CS 3500 (Software Practice)
  • CS 3960/6959 (Human-centered data management)
  • CS 4150 (Algorithms)
  • CS 5530 (Database Systems)
  • SQL and NoSQL
  • Python
  • Basic probability and statistics (e.g. MATH 1040, review)

Course Materials

  • All course materials will be available on Canvas. All materials for this course are copyrighted. Do not distribute or share course resources without instructor permission.
  • Slides used in lectures will be made available on Canvas.
  • Additional reading materials will also be made available on Canvas.
  • There is no required textbook.
  • Course lectures will be video recorded and posted on Canvas.

Academic Honesty Statement

Since the integrity of the academic enterprise of any institution of higher education requires honesty in scholarship and research, academic honesty is required of all students at the University of Utah. Academic dishonesty is prohibited in all programs of the University. Academic dishonesty includes but is not limited to: cheating, fabrication, plagiarism, and facilitating dishonesty. Appropriate sanctions may be imposed on any student who has committed an act of academic dishonesty. Instructors should take reasonable steps to address academic misconduct. Any person who has reason to believe that a student has committed academic dishonesty should bring such information to the attention of the appropriate course instructor as soon as possible. Instances of academic dishonesty not related to a specific course should be brought to the attention of the appropriate department Head or Chair. Since students are expected to be familiar with this policy and the commonly accepted standards of academic integrity, ignorance of such standards is not normally sufficient evidence of lack of intent.

Check out the following policies for more information:

1. Academic Misconduct

2. PRICE COLLEGE OF ENGINEERING GUIDELINES

Any sort of academic dishonesty will result into:

  • 0 for that assignment (homework assignment, quiz, exams)
  • An additional grade reduction for the class.
  • A filing with the Academic Dishonesty board.

Examples of academic dishonesty

  • Searching for homework solutions in the Internet and using it.
  • Searching for any part of the homework solutions in the Internet.
  • Collaborating/copying with any other current/past student of the class, or anyone else, while doing homework assignments.
  • Using any AI tool (such as ChatGPT) to generate (or validate) solutions of homework assignments or project proposals.

Course Policies

  • All course materials, such as lecture slides, homework assignments, grades, etc. will be posted on Canvas. Class announcements will be done via  Piazza + in-class.
  • Regularly check your UMail, CANVAS, and Piazza.
  • Electronic or equipment failure: It is your responsibility to maintain your computer and related equipment in order to participate in the online portion of the course. Equipment failures will not be an acceptable excuse for late or absent assignments.
  • Online submissions: You are responsible for submitting the assignment with the required naming convention, correct file extension, and using the software type and version required for the assignment.
  • You are responsible to own/borrow a computer with sufficient admin privilege to install the necessary software for this course. Not having access to install necessary software is NOT an excuse for related assignments.
  • Homework:  Two homework assignments will be distributed. You can use up to a total of 5 late days for all the homework assignments. No credits will be given for any further late submission after you have used up all your 5 late days. For example, you can use 2 days for the first one and 3 days for the second one.
  • Quizzes: There will be 5 quizzes. The lowest one will be dropped.
  • Exam: There will be one (late) mid-term. There will be no final.
  • Project:  All projects are individual.
  • Presentation: Form a group of up to 3 members for a class presentation. You can choose a topic from the list given here. Put your names next to the topic of your choosing. Make sure all members are available during the presentation day. Failure to present on your chosen date will result in zero for presentation.
  • Regrades: All regrade requests must be submitted within 3 days of the grades being released. No late requests for regrades will be entertained. All regrade requests must be submitted via Gradescope. No oral regrade requests will be accepted.
  • Grading: The following chart denotes the minimum grade you will get for each number ranges. For example, if your total score is 87, you will receive no less than a grade of B+.

 

Score GPA

A

93-100

4.0

A-

90-92.99

3.7

B+

87-89.99

3.3

B

83-86.99

3.0

B-

80-82.99

2.7

C+

77-79.99

2.3

C

73-76.99

2.0

C-

70-72.99

1.7

D+

67-69.99

1.3

D

63-66.99

1.0

D-

60-62.99

0.7

E

0-59.99

0.0

 

Acknowledgements

Some of the course materials are generously provided by Prof. Alexandra Meliou, UMass Amherst.

University Policies