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Paper Review Reading List

The following are the approved papers for the paper review assignment.

Students should select two papers from different topic areas.

A. Core DBMS, Storage, and Query Processing

  • P. Selinger, M. Astrahan, D. Chamberlin, R. Lorie, and T. Price. Link Access Path Selection in a Relational Database Management System.
    Proceedings of the ACM SIGMOD International Conference on Management of Data, pp. 23–34, 1979.

  • P. Boncz, M. Zukowski, and N. Nes. Link MonetDB/X100: Hyper-Pipelining Query Execution.
    Proceedings of the 2nd Biennial Conference on Innovative Data Systems Research (CIDR), pp. 225–237, 2005.

  • M. Stonebraker, D. J. Abadi, A. Batkin, et al. Link C-Store: A Column-Oriented DBMS.
    Proceedings of the 31st International Conference on Very Large Data Bases (VLDB), pp. 553–564, 2005.

  • D. J. Abadi, S. Madden, and N. Hachem. Link Column-Stores vs. Row-Stores: How Different Are They Really?
    Proceedings of the ACM SIGMOD International Conference on Management of Data, pp. 967–980, 2008.

B. OLTP, OLAP, and HTAP Systems

  • S. Harizopoulos, D. J. Abadi, S. Madden, and M. Stonebraker. Link OLTP Through the Looking Glass, and What We Found There.
    Proceedings of the ACM SIGMOD International Conference on Management of Data, pp. 981–992, 2008.

  • A. Kemper and T. Neumann. Link HyPer: A Hybrid OLTP & OLAP Main-Memory Database System Based on Virtual Memory Snapshots.
    Proceedings of the IEEE International Conference on Data Engineering (ICDE), pp. 195–206, 2011.

  • C. Diaconu, C. Freedman, E. Ismert, et al. Link Hekaton: SQL Server’s Memory-Optimized OLTP Engine.
    Proceedings of the ACM SIGMOD International Conference on Management of Data, pp. 1243–1254, 2013.

  • J. DeBrabant, A. Pavlo, S. Tu, M. Stonebraker, and S. B. Zdonik. Link Anti-Caching: A New Approach to Database Management System Architecture.
    Proceedings of the VLDB Endowment (PVLDB), 6(14):1942–1953, 2013.

C. Big Data Platforms and Cloud-Native Systems

  • B. Dageville, T. Cruanes, M. Zukowski, et al. Link The Snowflake Elastic Data Warehouse.
    Proceedings of the ACM SIGMOD International Conference on Management of Data, pp. 215–226, 2016.

  • M. Elhemali, N. Gallagher, N. Gordon, et al. Link Amazon DynamoDB: A Scalable, Predictably Performant, and Fully Managed NoSQL Database Service.
    Proceedings of the USENIX Annual Technical Conference, pp. 1037–1048, 2022.

  • M. Armbrust, A. Ghodsi, R. Xin, M. Zaharia Link Lakehouse: A New Generation of Open Platforms that Unify Data Warehousing and Advanced Analytics. Proceedings of the 11th Biennial Conference on Innovative Data Systems Research (CIDR), 2021

D. Data Streaming and Velocity

  • P. Carbone, A. Katsifodimos, S. Ewen, V. Markl, S. Haridi, K. Tzoumas. Link Apache Flink : Stream and Batch Processing in a Single Engine.
    IEEE Data Engineering Bulletin, 36(28-33), 2015.

  • T. Akidau, A. Balikov, K. Bekiroglu, et al. Link MillWheel: Fault-Tolerant Stream Processing at Internet Scale.
    Proceedings of the VLDB Endowment (PVLDB), 6(11):1033–1044, 2013.

E. Data Integration and Preparation

  • A. Y. Halevy, A. Rajaraman, and J. Ordille. Link Data Integration: The Teenage Years.
    Proceedings of the International Conference on Very Large Data Bases (VLDB), pp. 9–16, 2006.

  • H. Do and E. Rahm. Link COMA: A System for Flexible Combination of Schema Matching Approaches.
    Proceedings of the International Conference on Very Large Data Bases (VLDB), pp. 610–621, 2002.

  • R. Dhamankar, Y. Lee, A. Doan, A. Y. Halevy, and P. Domingos. Link iMAP: Discovering Complex Mappings Between Database Schemas.
    Proceedings of the ACM SIGMOD International Conference on Management of Data, pp. 383–394, 2004.

F. Data Quality and Provenance

  • B. Rekatsinas, X. Chu, I. F. Ilyas, and C. Ré. Link HoloClean: Holistic Data Repairs with Probabilistic Inference.
    Proceedings of the ACM SIGMOD International Conference on Management of Data, pp. 119–134, 2017.

  • M. Yakout, A. K. Elmagarmid, J. Neville, M. Ouzzani, and I. F. Ilyas. Link Guided Data Repair.
    Proceedings of the VLDB Endowment (PVLDB), 4(5):279–289, 2011.

  • H. Yang, Z. Xu, S. Yudin, and A. Davidson. Link Unlocking the power of ci/cd for data pipelines in distributed data warehouses. Proceedings of the VLDB Endowment (PVLDB) 18(12):4887–4895, 2025.

G. LLMs and Modern Data Engineering

  • D. Gao, H. Wang, Y. Li, X. Sun, Y. Qian, B. Ding, and J. Zhou. Link Text-to-SQL Empowered by Large Language Models: A Benchmark Evaluation.
    Proceedings of the VLDB Endowment (PVLDB), 17(5):1132–1145, 2024.
    DOI: 10.14778/3641204.3641221

  • M. Parciak, B. Vandevoort, F. Neven, L. M. Peeters, and S. Vansummeren. Link Schema Matching with Large Language Models: An Experimental Study. VLDB 2024 Workshop: Tabular Data Analysis Workshop (TaDA), 2024.

  • J. Tan, K. Zhao, R. Li, J. Xu Yu, C. Piao, H. Cheng, H. Meng, D. Zhao, and Y. Rong. Link Can Large Language Models Be Query Optimizers for Relational Databases? arXiv preprint, arXiv:2502.05562, 2025.

H. Vector Databases and Similarity Search

  • J. Johnson, M. Douze, and H. Jégou. Link Billion-Scale Similarity Search with GPUs. Proceedings of the IEEE International Conference on Big Data, pages 535–544, 2017.

  • Z. Wang, Y. Cai, S. Zhang, et al. Link Milvus: A Purpose-Built Vector Data Management System. Proceedings of the ACM SIGMOD International Conference on Management of Data, pages 2619–2632, 2021.

  • I. Azizi, K. Echihabi, and T. Palpanas. Link Graph-Based Vector Search: An Experimental Evaluation of the State-of-the-Art. Proceedings of the ACM on Management of Data (PACMMOD), 3(1): Article 43, 2025