Approximate Nearest Neighbor Search (ANNS) is a critical problem in vector databases. Cluster-based index is utilized to narrow the search scope of ANNS, thereby accelerating the search process. Due to its scalability, it is widely employed in real-world vector search systems. However, existing cluster-based indexes often suffer from coarse granularity, requiring query vectors to compute distances with vectors of varying quality, thus increasing query complexity. Existing work aim to represent vectors with minimal cost, such as using product quantization (PQ) or linear transformations, to speed up ANNS. However, these approaches do not address the coarse granularity inherent in cluster-based index. In this paper, we present an efficient vector data query engine to enhance the granularity of cluster-based index by carefully subdividing clusters using diverse distance metrics. Building on this refined index, we introduce techniques that leverage triangle inequalities to develop highly optimized and distinct search strategies for clusters and vectors of varying qualities, thereby reducing the overhead of ANNS. Extensive experiments demonstrate that our method significantly outperforms existing in-memory cluster-based indexing algorithms, achieving up to an impressive 10× speedup and a pruning ratio exceeding 99.4%.

Recent studies have shown the promise of directly processing compressed graphs. However, its benefits have been limited by high peak-memory usage and unbearably long compression time. In this paper, we introduce Laconic, a novel rule-based graph processing solution that overcomes the challenges of restricted memory and impractical compression time faced by existing approaches. Laconic, for the first time, ensures minimal memory overhead during compression and significantly reduces graph sizes, thus reducing peak memory demand during computations. By employing an efficient parallel compression algorithm, Laconic achieves a remarkable reduction in compression time. In our experiments, we compare Laconic with state-of-the-art solutions. The results demonstrate that Laconic outperforms other methods, reducing peak memory consumption by an average of 70% during compression and 66% during computation. Additionally, Laconic reduces rule compression time by an average of 93% compared to traditional rule-based compression, achieving a 2.47× higher compression ratio, and providing a 2.12× performance speedup.