The shortest path counting is an important problem for various applications in road networks, such as traffic analysis, reliability assessment, and navigation services. Existing methods, such as 2-hop labeling schemes, precompute shortest-path distances and counts to enable fast query responses, but they face scalability challenges in large and dynamically changing networks. In this work, we propose an efficient solution, Dynamic Count Labeling (DCL), for shortest path count queries on dynamic road networks. DCL consists of three key components: a tree hierarchy, a 2-hop count labeling scheme, and a shortcut count graph scheme. The tree hierarchy organizes the network to guide the 2-hop labeling design and ensures efficient query processing by limiting the search space to a small subset of relevant label entries. The shortcut count graph scheme enables efficient construction and dynamic maintenance of the 2-hop labels under edge weight increases and decreases. We further develop dynamic algorithms to update both the shortcut count graph and the 2-hop labels in response to network changes. Additionally, we introduce a parallel version of our algorithms that leverages the label structure for high parallel efficiency. Experimental results demonstrate that DCL can compete with existing approaches for static graphs in terms of construction time, index size and query time, while enabling efficient index updates for dynamic graphs, at a fraction of reconstruction costs.
Vector databases are essential for large-scale multi-modal data management. Approximate similarity join, which aims to retrieve vector pairs satisfying a user-defined rule, is a fundamental operator in modern vector databases. As vectors are often associated with structured attributes, we define approximate range-filtered similarity join (ARFSJ): only vectors that additionally satisfy an attribute filter are considered. Existing solutions approach ARFSJ from two angles: (i) search-based methods that treat each vector as an independent approximate similarity search, and (ii) join-based methods that process the vectors in a locality-aware order. Both families repeatedly compute the search space, leading to redundant computation, and they overlook batch optimization, resulting in an imbalance between locality and parallelism in concurrent settings. We present ParaJoin, the first framework purposely designed for ARFSJ. ParaJoin introduces an adaptive partitioning algorithm that dynamically reconstructs partitions for arbitrary join ranges, and it develops batch optimization strategies that combine fine-grained task division with neighborhood result reuse. Extensive experiments on five real-world datasets demonstrate that, while maintaining high recall, ParaJoin achieves speedups of up to 17.3× and 218.6× over the state-of-the-art baselines for two representative types of ARFSJ, respectively.
Query optimization is a crucial problem in database systems that has been studied for decades. Learned query optimizers (LQOs) can improve performance over time by incorporating feedback; however, they suffer from cold-start issues and often require retraining when workloads shift or schemas change. We initially proposed SEFRQO, a Self-Evolving Fine-tuned RAG-based Query Optimizer, which leverages fine-tuned LLMs to address these challenges and uses in-context learning to achieve continuous evolution. However, its system design relies on the assumption of repetitive query workloads, requires executing queries statically upon their first encounter (incurring extra overhead), and utilizes only partial feedback from the best historical records (insufficient feedback design). In this paper, we present SEFRQO-Plus, which resolves the above limitations. First, we design a feedback-oriented vector database with query structure-aware embeddings to support effective similarity search, and we incorporate multi-layered histories as references to enrich the feedback. Then, we enhance the prompt optimization workflow by utilizing multi-dimensional historical feedback to drive continuous self-evolution. Moreover, we provide formal theoretical proofs to validate the advantages of our customized query group relative policy optimization. Evaluations show that SEFRQO-Plus outperforms state-of-the-art LQOs, achieving reductions of 66.52
Online social networks have emerged as prominent platforms for individuals to rapidly share ideas and perspectives. However, their swift dissemination capabilities also make them powerful channels for the spread of misinformation. Such dissemination results in substantial economic and societal harm, highlighting the need for effective suppression. To support this practical demand, this paper investigates the influence minimization (IMIN) and blocker minimization (BM) problems. The IMIN problem aims to find a node set of size k (the given budget) to block, such that the reduction in influence spread of a given seed set S is maximized. The BM problem is the dual of IMIN, aiming to find a node set of minimum cardinality to block, such that the threshold condition is satisfied, i.e., the reduction in influence spread of S is not less than the given threshold η . Both problems are NP-hard and involve a non-submodular objective function. Existing IMIN solutions incur high computational costs and offer no approximation guarantees on influence spread reduction. To fill the gap, we build upon the sandwich strategy to develop the first efficient algorithm with data-dependent approximation guarantees for IMIN. In particular, we design non-trivial (1-1/e-ϵ ) -approximation algorithms to optimize the proposed submodular bounding functions. For the BM problem, the existing solution is of limited effectiveness and lacks theoretical guarantees for both node set cardinality and threshold condition satisfaction. In this paper, we design a heuristic and two bicriteria approximation algorithms to address the BM problem. The heuristic is efficient and satisfies the threshold condition, but fails to ensure near-optimal cardinality. The two approximation algorithms both provide guarantees on cardinality: the first attains a tighter approximation ratio at the expense of efficiency, while the second yields a slightly weaker ratio with improved efficiency. Finally, comprehensive experiments on 9 real-world datasets are conducted to validate the efficiency and effectiveness of the proposed techniques.
We study the distinctiveness maximization problem for datasets assemblage: assembling a set of datasets from a repository that satisfies a user-specified target schema and query set under a budget constraint, while maximizing the number of distinct tuples. This problem is NP-hard in two scenarios: (i) when the repository contains datasets conforming to the target schema, and (ii) when no dataset individually satisfies the schema and candidates must be constructed through joins. For the first scenario, a greedy algorithm with exact distinctiveness computation achieves a (1-e^-1)/2 -approximation ratio, but is costly due to exhaustive tuple-wise marginal gain computation. We therefore propose an efficient machine learning (ML)-based method that directly estimates the marginal distinctiveness gain of each candidate dataset, avoiding costly exact computations. For the second scenario, we integrate a column-level merging technique into our ML framework to construct candidate datasets and estimate their distinctiveness without physically materializing join results, supporting efficient assemblage over heterogeneous datasets. Extensive experiments on real-world data pools show that our methods consistently outperform relevant baselines in effectiveness, efficiency, and scalability. A downstream case study on two ML tasks further shows that our approach identifies datasets with more useful tuples and improves ML performance.
