CMP653 DATABASE MANAGEMENT SYSTEMS

SPRING 2026

INSTRUCTOR: Engin Demir

COMMUNICATION:  Communications will be carried out through Piazza. Please enroll it by following the links http://www.piazza.com/hacettepe.edu.tr/spring2026/cmp635

COURSE DESCRIPTION: This is an advanced graduate course on database systems intended for students in computer science and engineering. The course provides a comprehensive coverage of database system models, starting with relational, object-relational, and NoSQL systems, extending into parallel and distributed architectures. The second half of the course focuses on modern database systems integrated with AI applications, covering hybrid workloads (HTAP), autonomous databases, vector stores and next generation DBMS. Students are expected to conduct a research or applied project, aiming for a submission-quality conference or journal paper.

TEXTBOOKS:  

 Avi Silberschatz, Henry F. Korth, S. Sudarshan. Database System Concepts , Seventh Edition, 2019. McGraw-Hill.
J. A. Hoffer, R. Venkataraman, and H. Topi. Modern Database Management , 13th Edition, Pearson Education.

GRADING POLICY:

Research/Applied Project 60
Final Exam 40

CLASS SCHEDULE

Week Topic Materials
1 Introduction to Modern DBMS  
2 Relational Model & SQL Review Silberschatz Ch 1, 2, 3-5; Hoffer Ch 1-4
3 Object-Based Databases & Complex Data Types Silberschatz Ch 5 (Sec 5.3-5.4); Hoffer Ch 13
4 NoSQL Systems I: Key-Value & Document Stores Silberschatz Ch 10 (Big Data); Hoffer Ch 13 (Big Data)
5 NoSQL Systems II: Column-Family & Graph Databases Silberschatz Ch 10 (Big Data); Hoffer Ch 13
6 Parallel Databases: Architecture & Storage Silberschatz Ch 20, 21
7 Distributed Query Processing Silberschatz Ch 22
8 Distributed Transactions & Consistency (CAP/PACELC) Silberschatz Ch 23
9 Cloud Databases & Modern Architectures Silberschatz Ch 25 (Advanced Topics)
10 Hybrid Transactional/Analytical Processing (HTAP) Reading list
11 AI for Database Management (Self-Driving/ Autronomous Databases) Reading list
12 Databases for AI & Emerging Technologies Reading list
13 Final Project Presentations & Demonstrations  
14 Final Project Presentations & Demonstrations  

Reading list  
Hybrid Transactional/Analytical Processing (HTAP)
AI for Database Management (Self-Driving/Autonomous Databases)
  • Tim Kraska, Alex Beutel, Ed H. Chi, Jeffrey Dean, and Neoklis Polyzotis. 2018. The Case for Learned Index Structures. In Proceedings of the 2018 International Conference on Management of Data (SIGMOD '18). Association for Computing Machinery, New York, NY, USA, 489–504. https://doi.org/10.1145/3183713.3196909
  • Maryam Mozaffari, Anton Dignös, Johann Gamper, and Uta Störl. 2024. Self-tuning Database Systems: A Systematic Literature Review of Automatic Database Schema Design and Tuning. ACM Comput. Surv. 56, 11, Article 277 (November 2024), 37 pages. https://doi.org/10.1145/3665323
  • Samuel Madden, Michael Cafarella, Michael Franklin, and Tim Kraska. 2024. Databases Unbound: Querying All of the World's Bytes with AI. Proc. VLDB Endow. 17, 12 (August 2024), 4546–4554. https://doi.org/10.14778/3685800.3685916
Databases for AI: Vector Databases & Embeddings
Next-Gen Databases: Emerging technologies in DBMS
  • Andrew Pavlo and Matthew Aslett. 2016. What's Really New with NewSQL? SIGMOD Rec. 45, 2 (June 2016), 45–55. https://doi.org/10.1145/3003665.3003674
  • Qian Wei, Bingzhe Li, Wanli Chang, Zhiping Jia, Zhaoyan Shen, and Zili Shao. 2022. A Survey of Blockchain Data Management Systems. ACM Trans. Embed. Comput. Syst. 21, 3, Article 25 (May 2022), 28 pages. https://doi.org/10.1145/3502741
  • Liu, C., Russo, M., Cafarella, M., Cao, L., Chen, P. B., Chen, Z., ... & Vitagliano, G. (2025). Palimpzest: Optimizing ai-powered analytics with declarative query processing. In Proceedings of the Conference on Innovative Database Research (CIDR) (p. 2).  https://palimpzest.org/
  • Liana Patel, Siddharth Jha, Melissa Pan, Harshit Gupta, Parth Asawa, Carlos Guestrin, and Matei Zaharia. 2025. Semantic Operators and Their Optimization: Enabling LLM-Based Data Processing with Accuracy Guarantees in LOTUS. Proc. VLDB Endow. 18, 11 (July 2025), 4171–4184. https://doi.org/10.14778/3749646.3749685