Unstructured data, such as images and texts, are increasingly represented as high-dimensional vectors for emerging AI applications like retrieval-augmented generation. A key operation in these applications is querying for vectors that are both semantically similar and temporally relevant. This operation can be formulated as Timestamp Approximate Nearest Neighbor Search (TANNS), where both the vectors and the query incorporate temporal attributes, aiming to retrieve the approximate nearest neighbors valid at the given timestamp. A naive solution is to create separate indexes for each timestamp, which enables accurate and fast searches but incurs high update latency and excessive storage demands. In this paper, we introduce the timestamp graph, a novel structure that supports rapid index updates while minimizing storage costs. Exploiting the temporal locality of changes in valid vectors, our timestamp graph effectively manages a unified index across all historical timestamps, thereby substantially reducing storage overhead. Moreover, we design the historic neighbor tree, which further compresses the space complexity to that of a single-timestamp index. Extensive evaluations on four standard datasets show that our method achieves over 99% accuracy while improving the query efficiency by 4.4× to 138.1× than existing solutions.

Data federation has emerged as a novel database system enabling collaborative queries across mutually distrusted data owners. Federated equi-join, a commonly used operation in data federation, combines relations from distinct data owners while preserving their data privacy. Due to the wide applications of this query, many solutions to federated equi-joins have been proposed. However, it is still challenging for practitioners to choose the most appropriate algorithm due to various reasons, including incomplete evaluation protocols (e.g., lack of evaluating multi-way equi-joins), under-explored performance metric (main memory usage), and absence of a standardized comparison. Motivated by this reason, this paper conducts a comprehensive experimental study and builds a new benchmark, called${\sf FEJ-Bench}$, for federated equi-joins. The experimental study and the benchmark consist of eight state-of-the-art algorithms and five datasets. Our evaluation reveals the query efficiency ranking, its impact factors, and potential research opportunities. Finally, we open-source${\sf FEJ-Bench}$on GitHub, which is the first benchmark for federated equi-joins. Our findings aim to guide researchers and practitioners in deploying federated equi-joins in practice.