Viktor Sanca

PhD candidate at EPFL.
Interested in systems, databases, and ML. Especially how to make them efficient and fast on modern hardware.

My research is in data management systems, machine learning, and applications. My primary focus is on using approximate query processing with high-performance analytical systems on modern hardware to enable workload-agnostic, timely, interactive, and cost-efficient insights from the growing amounts of data [5,7,8]. Broadly, I am interested in novel hardware architectures, hardware-software co-design, database systems, ML, and algorithms for analytics [2,6]. This also drives my interest in using machine learning with databases, particularly in mixed data format analytics and holistic optimization, from complex logical to physical plans that execute on heterogeneous hardware [2,3,6,8]. Finally, modern hardware requires special consideration to achieve the best performance; thus, analyzing and optimizing algorithms for modern platforms and systems remains a natural goal [1,4,5,6].

Peer-Reviewed Publications

  1. Viktor Sanca and Anastasia Ailamaki. 2023. Post-Moore’s Law Fusion: High-Bandwidth Memory, Accelerators, and Native Half-Precision Processing for CPU-Local Analytics. To appear at ADMS@VLDB’23.

  2. Viktor Sanca and Anastasia Ailamaki. 2023. E-Scan: Consuming Contextual Data with Model Plugins. To appear at CDMS@VLDB’23.

  3. Stefan Igescu, Viktor Sanca, Eleni Zapridou, and Anastasia Ailamaki. 2023. Improving K-means Clustering Using Speculation. To appear at AIDB@VLDB’23.

  4. Hamish Nicholson, Andreea Nica, Aunn Raza, Viktor Sanca, and Anastasia Ailamaki. 2023. Chaosity: Understanding Contemporary NUMA-architectures. To appear at the TPC-TC@VLDB’23.

  5. Viktor Sanca, Periklis Chrysogelos, and Anastasia Ailamaki. 2023. LAQy: Efficient and Reusable Query Approximations via Lazy Sampling. In Proc. ACM Manag. Data 1(2): 174:1-174:26 (2023). Link - Presented at SIGMOD’23

  6. Viktor Sanca and Anastasia Ailamaki. 2023. Analytical Engines With Context-Rich Processing: Towards Efficient Next-Generation Analytics. In 2023 IEEE 39th International Conference on Data Engineering (ICDE). Link - Special track vision paper, presented at ICDE’23

  7. Viktor Sanca and Anastasia Ailamaki. 2022. Sampling-Based AQP in Modern Analytical Engines. In Data Management on New Hardware. Association for Computing Machinery, New York, NY, USA, Article 4, 1–8. Link - Presented at DaMoN@SIGMOD’22

  8. Panagiotis Sioulas, Viktor Sanca, Ioannis Mytilinis, and Anastasia Ailamaki. 2021. Accelerating Complex Analytics using Speculation. In Conference on Innovative Data Systems Research. Link - Presented at CIDR’21