Non-Volatile Memory (NVM) offers nanosecond latency and data persistence, enabling new index designs for large-scale key-value stores. However, its write asymmetry (high write latency, write amplification) and limited bandwidth challenge existing structures. The Adaptive Radix Tree (ART) excels in memory/NVM with prefix compression but suffers in NVM: its deeper tree than B+ trees causes long-key queries to traverse more NVM layers, degrading efficiency. Traditional Skip Lists shorten paths via hierarchy but fail to locate ART’s prefix branches due to random levels and full-key comparisons. This paper proposes HIACS, a hybrid NVM index integrating ART and Skip Lists. It uses a Prefix Hash Adjacency List (replacing Skip Lists) as H-Skip List to map key prefixes/suffixes to ART nodes for cross-hierarchical jumping and accurate branch localization; an Asynchronous Weak Consistency Strategy decouples leaf updates from SMOs with versioning to ensure eventual consistency and avoid blocking; and a Dynamic Monitoring Module tracks ART node states to optimize H-Skip List query parameters and reduce overhead. Experimental results under YCSB mixed workloads show HIACS outperforms mainstream NVM indexes (PACTree, LITS) by 23
Open data plays a critical role in advancing social good, yet access to this open data resource remains fragmented, with different publishers hosting data across various decentralized portals. This decentralization poses significant challenges for building unified search systems, primarily due to heterogeneity and, often, the low quality of metadata across these portals. In this paper, we address these challenges within the context of public dataset search by exploring the application of large language models (LLMs) in a zero-shot or few-shot manner to consolidate and enrich heterogeneous and incomplete metadata. We also investigate how LLMs can be utilized to rerank retrieved datasets, thereby enhancing search accuracy, and to provide relevance explanations to help users make more informed decisions. Using these techniques, we develop the China Public Data Search system (CN-PDS), a unified dataset search system that integrates datasets from public data portals of various provinces and cities in China. Through extensive experiments on China’s public data and other standard test collections for dataset search, we validate the effectiveness of our approach. Our empirical evaluation, coupled with the insights gained from this work, offers valuable lessons in improving real-world dataset search systems using LLMs.
Sustained emerging spatio-temporal co-occurrence patterns (SECOPs) represent subsets of spatial features, whose instances consistently appear in close proximity across both space and time. Discovering SECOPs is valuable for many applied fields. Existing studies of SECOP mining neglect the emergence of new instances over time in dynamic spatial databases, which may lead to complex changes in neighbor relationships of instances and heterogeneity of the change in the number of feature instances. Therefore, the deviation may be caused in the SECOP measure for dynamic spatial databases with new instances. To solve this problem, we propose the weighted emerging participation index (WEPI) that incorporates the effects of the change of neighbor relationships and the heterogeneity on the sustained emerging measure of SECOPs. Because WEPI does not possess the antimonotone property, we prove an upper bound of the weighted emerging participation ratio of features and introduce a pruning strategy to prune some candidate patterns. Building upon this pruning strategy, an incremental mining algorithm based on depth-first search is proposed, i.e., IM-DFS. In IM-DFS algorithm, a set of optimization techniques is presented to accelerate WEPI calculation. Extensive experiments on real-world dynamic spatial datasets demonstrate the effectiveness of WEPI and the efficiency of IM-DFS.
Join order selection is one of the key technologies in database query optimizers. Traditional approaches rely heavily on expert experiences, resulting in a prolonged development cycle. Heuristic methods based on predefined rules also fail to leverage past experiences. In recent years, reinforcement learning-based methods have emerged as a promising approach for query optimization. However, different segments of the join plan in SQL queries carry varying degrees of importance for execution latency, which can cause high variability in reward values. Such discrepancies pose challenges in generating robust and efficient query plans. This paper proposes LIGHT, a learned join order selection network that leverages a dynamic graph attention mechanism. This mechanism propagates and fuses table node vectors, allowing each node to focus on the features of its most relevant neighbors, thereby enhancing the query’s vector representation. Moreover, a beam-random search method with a weighted list is employed to retain more possibilities for exploring the best join order in reinforcement learning action selection. Finally, to mitigate reward volatility, the model introduces a logarithmic transformation to compress reward values. This accelerates the training process and promotes the generation of robust query plans. Extensive comparative experiments on the Join Order Benchmark and TPC-DS demonstrate that LIGHT significantly outperforms existing learned optimizers in terms of query execution performance.
Incomplete multi-view clustering (IMVC) is an important research topic in data mining due to the prevalence of incomplete multi-view data, where samples may be missing in particular views. Although many methods for IMVC have been proposed and achieved encouraging results, these methods ignore the neighbor information for samples with missing views and the global structure of multi-view data, and do not effectively utilize the pseudo labels generated during label allocation. To address these issues, we propose a multiple self-supervised IMVC method based on neighbor contrastive learning (NCMS). Firstly, the missing samples are completed through the transfer of neighbor relationships between views. Then, a two-level node representation learning strategy including view-specific and global-specific contrastive learning is designed to learn the sample features. Next, a multiple self-supervised strategy including intra-view and inter-view self-supervised learning is developed to learn the global representation suitable for clustering tasks. The experimental results on four incomplete multi-view datasets verify the effectiveness of the proposed method, especially on the 100Leaves dataset, when the missing rate is 50
