Philip A. Bernstein
In this short paper, I recount some early history of transaction research (including some of my own), explain why transaction research continues to this day (even though it seems to be a solved problem), and speculate about its future. This is an extended version of the paper that appeared in the Companion of the 2025 International Conference on Management of Data (SIGMOD-Companion '25).
Jiongli Zhu, Yue Wang, Bailu Ding et al.
Vector search plays a crucial role in many real-world applications. In addition to single-vector search, multi-vector search becomes important for multi-modal and multi-feature scenarios today. In a multi-vector database, each row is an item, each column represents a feature of items, and each cell is a high-dimensional vector. In multi-vector databases, the choice of indexes can have a significant impact on performance. Although index tuning for relational databases has been extensively studied, index tuning for multi-vector search remains unclear and challenging. In this paper, we define multi-vector search index tuning and propose a framework to solve it. Specifically, given a multi-vector search workload, we develop algorithms to find indexes that minimize latency and meet storage and recall constraints. Compared to the baseline, our latency achieves 2.1X to 8.3X speedup.