Xinyi Ye's paper "AJOSC: Adaptive Join Order Selection for Continuous Queries" on join order selection for continuous multiway join queries has been accepted by SIGMOD 2025.

Multi-way join, which refers to the join operation among multiple tables, is widely used in database systems. With the development of the Internet and social networks, a new variant of the multi-way join query has emerged, requiring continuous monitoring of the query results as the database is updated in a streaming manner. This variant is called continuous multi-way join. The join order of continuous multi-way join significantly impacts the operation's cost. However, existing methods for continuous multi-way join order selection are heuristic, which may fail to select the most efficient orders. On the other hand, the high-cost order computation will become a system bottleneck if we directly transfer join order selection algorithms for static multi-way join to the stream setting. In this paper, we propose a new Adaptive Join Order Selection algorithm for the Continuous multi-way join queries named AJOSC. It uses dynamic programming to find the optimal join order with a new cost model specifically designed for continuous multi-way join. We further propose a lower-bound-based incremental re-optimization algorithm to restrict the search space and recompute the join order with low cost when data distribution changes. Experimental results show that AJOSC is up to two orders of magnitude faster than the state-of-the-art methods.