最新刊期
卷 12 ， 期 4 ， 2026
- 摘要：In complex systems represented by megacities, large-scale cross-domain data circulation exhibits distinct characteristics such as dispersed data sources, heterogeneous data, cross-scenario application, and dynamic rules, which pose systematic challenges to the collaborative governance of multi-source data. To address these challenges, this paper firstly elaborates on the evolution of data governance paradigms from multiple perspectives, analyzing the conceptual foundations, technological approaches, and application characteristics of each stage. It subsequently summarizes the technical challenges and scientific questions encountered in the entire spatial scope, innovatively proposing the Data Governance 5.0 paradigm characterized by data symbiosis along with its corresponding technical framework. Finally, the application of this proposed paradigm is demonstrated through a case study of an Intelligent Data Operating System.关键词：entire spatial scope;distributed data governance;data governance paradigm;cross-domain circulation;supply-demand matching116|51|0更新时间：2026-07-15
- 摘要：Focusing on the field of government data sharing, this paper analyzes the core problems such as"dare not share" and "not deeply utilized" that currently exist. The research holds that building a trusted government data space is the key path to breaking the bottleneck of government data circulation. As an important part of the national trusted data space system, this space can provide triple guarantees covering rules, technology, and governance for the circulation of data elements. In response to the new requirements for data security and system support put forward by artificial intelligence applications such as large model training and industry model fine-tuning, a construction path based on data hierarchical governance, integrating privacy computing and blockchain technology, and oriented towards AI tasks is proposed, aiming to promote the compliant and efficient circulation of government data and provide a reference basis for the construction of a national integrated data element market and the improvement of intelligent governance capabilities.关键词：Trusted Data Space;government data sharing;privacy computing;blockchain;artificial intelligence92|31|0更新时间：2026-07-15
- 摘要：Facing the challenges of large-scale circulation of data factors, from the perspective of systems theory, a comprehensive solution of “the trusted data space (TDS) based on reality-data integration” is introduced. This solution is comprised primarily of: the design of institutional rules for the data factors market, the design of the technical architecture of the TDS system, and an operation-oriented, central-local collaboratively constructed path for the nationwide TDS system. The innovation of the solution is summarized as a “1+1+3” framework, namely “one trusted governance methodology (for data factors circulation)” “one framework of reality-data integration” and building upon this, “one trusted data space” “one infrastructure platform (multi-node)” and “one operational entity” for the nationwide data factors market.关键词：Trusted Data Space;Reality-Data Integration;data circulation;data market;Data Infrastructure;data factor71|58|0更新时间：2026-07-15
- 摘要：With the advancement of national policies on data factors, efficient discovery and convenient utilization of data resources across data spaces scattered in various industries and fields have become a key for the effective circulation of data elements. A data space search system oriented to scenario-based data usage was designed and implemented. In response to the security requirements that data shall not leave its domain or be aggregated in each, a distributed search framework based on the digital object architecture (DOA) was constructed. Based on the pragmatic data relationships abstracted from the scenario-based data usage, a digital object ranking algorithm, named DO-Rank was proposed. It considered both query similarity and data pragmatic relationships. It provided upper-layer applications with the capability to search and discover data resources across different data spaces. Experiments results demonstrated that without data aggregation, the system achieved performance indicators including precision, recall and normalized discounted cumulative gain (nDCG) comparable to those of centralized data retrieval methods.关键词：data space;data circulation;Digital Object Architecture;search system;search ranking47|32|0更新时间：2026-07-15
TOPIC
- 摘要：In industrial internet of things (IIoT) scenarios, device monitoring data are typically stored in the form of time-series data and modeled using hierarchical path structures to facilitate point management and access; however, this modeling approach does not directly support multidimensional analysis based on relational models and often requires complex ETL transformations. To address the model-level conflict between time-series data management and analysis in industrial settings, this paper proposes a unified time-series data model. The model leverages a hierarchical tree structure for flexible data ingestion and storage, while offering multidimensional analytical capabilities through a classical relational model view. We implement this approach systematically in the industrial time-series database Apache IoTDB, enabling a single dataset to support both monitoring and analytical requirements.关键词：industrial internet of things;timeseries database;data model;schema management;hierarchical data;data model transformation91|53|0更新时间：2026-07-15
- 摘要：Due to the widely available missing data in data, missing value imputation has become an extremely important issue in data processing. Equipment health management, such as fault diagnosis, life prediction, and early detection of weak faults, relies on sensor data, which can seriously affect the analysis results of such methods when missing values are present in the sensor data. Most existing missing value imputation methods focus on overall trends while ignoring data details, but the details often reflect the true situation of the device, especially in weak fault detection. To address these issues, a wavelet transform-based missing value imputation method (WTMI) is proposed. The method first decomposes the data by wavelet to obtain multi-scale data, then reconstructs the multi-scale data separately using an auto-encoder, and finally obtains the final prediction value by using the inverse wavelet transformation. Considering the correlation between different scales and the problem of scale selection, we further proposed a multi-scale missing value imputation (MSMI). MSMI uses a stacked neural network to link data at different scales, so that the information at different scales can be fully utilized and more accurate predictions can be obtained. The experimental results of imputation and fault diagnosis on artificial and practical datasets show that the imputation accuracy of this method is similar to existing methods, and it improves the fault diagnosis accuracy by 2% compared with existing filling methods, especially by 5% for weak faults.关键词：missing value;multi-scale analysis;wavelet transform;sensor data;fault diagnosis77|56|0更新时间：2026-07-15
- 摘要：To address the core bottlenecks of high latency and low utilization in Hadoop ecosystem resource scheduling for the Zettabyte (ZB) era, this study proposes a cross-layer collaborative optimization method driven by a dynamic trade-off model. A SDAPC collaborative optimization framework integrating quantum and classical algorithms is constructed. This framework first enhances data locality through storage-computation preprocessing to reduce problem complexity; its core innovation lies in mapping the YARN task allocation problem to an Ising model Hamiltonian minimization problem. Global optimization is achieved via D-Wave quantum annealing, while millisecond-level fine-grained adjustments are enabled by a graph attention network-driven deep reinforcement learning strategy. In TPC-DS benchmarks on a 256-node heterogeneous cluster, the method achieves ultra-low scheduling latency of 12.0
- 摘要：Educational knowledge graph is an important tool for representing relationships between knowledge and concept in the field of education. Understanding and evaluating the importance of node in educational knowledge graph are crucial for tasks like educational resource management and learning path recommendation. However, traditional node importance evaluation methods often treat all nodes equally, considering only certain topological structures, and fail to capture the comprehensive characteristics of node. To address this, we proposed TSFM driven by key node. TSFM evaluated node importance by integrating topological structure with semantic and structural similarity information driven by key node. Specifically, TSFM utilized graph neural networks to embed nodes in knowledge graph, and optimized the embedding representation by considering topological and semantic similarity between nodes. Experimental results demonstrate that TSFM outperforms traditional algorithms and recent graph neural network models across multiple evaluation metrics.关键词：Educational Knowledge Graph;Node Importance Evaluation;topological structure;graph attention network72|25|0更新时间：2026-07-15
- 摘要：With the widespread application of speech technology, voice privacy protection faces severe challenges. A speech anonymization method (kLCAnoy++) based on latent space splicing and contrastive learning was proposed, aiming to effectively remove speaker identity information while preserving speech content intelligibility and naturalness. The method first employed the pretrained WavLM model to extract self-supervised speech features. Subsequently, it matched target features in a diverse reference audio feature library using the k-nearest neighbors algorithm, achieving latent space feature splicing to generate preliminary anonymized speech. Finally, a contrastive learning mechanism was introduced, treating natural splicing as positive samples and k-nearest neighbor splicing as negative samples, to optimize the vocoder decoding process and enhance the coherence and naturalness of anonymized speech. Experiments on the VCTK dataset demonstrated that the proposed method significantly outperformed baseline approaches, with visualization analysis further confirming its effectiveness in disentangling speaker identity information.关键词：Speech anonymization;Speaker de-identification;Self-supervised speech representations;latent space splicing;contrastive learning5|0|0更新时间：2026-07-15
STUDY
- 摘要：Under the accelerated construction of a unified national data market, the factorization of municipal public transportation data still faces persistent challenges, including an underdeveloped supply system, insufficient cross-departmental integration and sharing, and a lack of productized data offerings. To explore replicable solutions, this study takes Fuzhou's practice as a case and examines its innovative model, which strengthens factor supply through a transportation data infrastructure and an intelligent annotation system, and enables scenario implementation and data circulation via the data-sharing platform, city-level large model applications and the data-interconnection network mechanism.The findings indicate that this model integrates data governance, data engineering, and application deployment into a coordinated framework, forming a closed loop between data-factor supply and scenario-based value realization. It has effectively addressed the above shortcomings and achieved tangible progress , providing valuable experience and reference for other cities seeking to advance the factorization and market-oriented utilization of public transportation data.关键词：unified national data market;transportation data factorization;intelligent transportation;TOCC;city-level large model;data-interconnection network44|18|0更新时间：2026-07-15
APPLICATION
- 摘要：As data elements have been deeply integrated into economic activities, the construction of production and circulation systems for data products has been accelerated, among which the development of data supply chains has been particularly critical. First, the differences between data supply chains and physical supply chains were analyzed, and three distinctive features of data supply chains were summarized, namely "networked supply and low latency", "'0/1' inventory", and "demand-driven structural flexibiliky". Second, to address the two major challenges of rapid participant matching and coordinated pricing across the entire chain, a flexible data supply chain construction and pricing framework, termed FlexChain, was proposed. Dominated by an architect, FlexChain employed a multi-dimensional fitness scoring mechanism to realize dynamic real-time reconfiguration of the ecological topology on the one hand, and introduced a nonlinear quadratic cost function to construct a two-stage Stackelberg game pricing model on the other hand, based on the superlinear growth characteristics of computational and compliance costs in data processing. Finally, the existence and uniqueness of the game equilibrium were proved through theoretical analysis, by which the profit maximization of all parties involved in the data supply chain and the transmission optimization of the end-user bidding price were effectively achieved. This framework has provided a feasible pathway for the construction of production and circulation systems for data products under dynamic and high-frequency scenarios.关键词：data supply chain;flexible manufacturing;Stackelberg game;data supply chain pricing25|4|0更新时间：2026-07-15
- 摘要：As the demand for data continues to soar, enterprises often resort to data crawling to enhance efficiency. Data crawling gives rise to multiple risks, including data privacy security , data contamination that endangers data governance security, and unfair competition that undermines market order. The existing legal framework faces challenges such as ambiguous legal boundaries for crawling behaviors, the uncertain legal validity of the Robots Exclusion Protocol, and deficiencies in data compliance governance mechanisms. The underlying causes lie in the imbalance between two pairs of core rights and interests: "data efficiency and data security",as well as "the autonomous management rights of data platforms and the obligation of tolerance for data access". Therefore, from the perspective of balancing rights and interests, this paper established a multi-party collaborative, classification-based, and tiered data governance system, improved data compliance governance mechanisms, optimized the classified application of the triple authorization principle, clarified the Robots Exclusion Protocol's legal validity as a contract, and safeguarded the right to data deletion. These five measures aim to address the dilemmas faced by data crawling and facilitate enterprises' digital transformation.关键词：Data crawling;data governance;compliance;balance of right and interest64|57|0更新时间：2026-07-15
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