Big Data最新文献

Recommender systems play a crucial role in enhancing user engagement across domains such as e-commerce, social media, entertainment, and education. Recently, they have also been used in marketing to identify high-value customers and personalize campaigns. However, small businesses often struggle with the high cost per action (CPA) and low conversion rates (CR) associated with online marketing platforms. To address this challenge, we propose a novel recommender system that leverages offline interaction data to identify customers likely to use discount coupons, thereby increasing CRs and reducing marketing costs. We address technical challenges such as cold-start problems and data sparsity by introducing tailored data augmentation techniques. The effectiveness of our approach is validated through experiments using store-level coupon and point log data, evaluated with metrics including CPA, CR, and root mean squared error. Results show that our system significantly outperforms conventional online marketing platforms, emphasizing the value of incorporating offline data with proper augmentation for cost-effective marketing.

Enhancing brand value is critical for new energy vehicle (NEV) enterprises amid fierce competition. This study leverages online consumer reviews as core big data to drive brand equity improvement via advanced big data analytics. A large-scale dataset of 5564 reviews for top five best-selling NEVs was collected from "Dongche Di" via web scraping, followed by a big data processing pipeline (data cleaning, Jieba segmentation, and stop-word filtering). To mine unstructured text big data, we used word cloud visualization, semantic network analysis, and an Latent Dirichlet Allocation (LDA)-Long Short-Term Memory (LSTM) fusion model: LDA identified key consumer concern dimensions, while LSTM enabled deep sentiment classification. Big data analysis revealed five core NEV brand perception dimensions (range, driving experience, interior space, price, and high-speed performance) and quantified emotions-prominent negativity in driving experience, minimal negativity in interior space, and overall dominant negativity. Guided by the Consumer-Based Brand Equity model, we proposed brand enhancement strategies. This study showcases big data analytics' power in scaling consumer perception understanding, offering a data-centric framework for NEV firms to optimize branding.

In the context of a global risk society, emergency law has become a critical field for balancing the expansion of state power with the protection of civil rights during crises. Despite its growing importance, a systematic, quantitative comparison of the knowledge landscapes of international and Chinese emergency law scholarship has been notably absent. This study employs bibliometric and knowledge mapping analysis, utilizing CiteSpace software. A total of 274 publications were retrieved from the Web of Science Core Collection and 391 from the China National Knowledge Infrastructure database. These data were used to systematically map and compare the research status, collaborative networks, and core themes of the two academic communities. The findings indicate that while both international and Chinese research are crisis-driven, with publication surges corresponding to major events such as the 9/11 attacks, SARS, and the COVID-19 pandemic, they function as two academically isolated communities with no author-level collaboration. A fundamental divergence in research paradigms was identified. International scholarship follows a "limitation-oriented" paradigm, rooted in liberal constitutionalism, focusing on the tension between emergency powers and human rights, and the risks of a state of exception. In contrast, Chinese research adopts a "construction-oriented" paradigm aimed at building an efficient, state-centric crisis response system, dominated by concepts such as emergency management and the "one plan and three sub-systems" framework. This study concludes that there are two worlds of emergency law. The international paradigm primarily treats emergency law as a mechanism to constrain state authority and protect individual rights from government overreach. In contrast, the Chinese paradigm views law as an instrument to enhance state capacity and ensure effective crisis management. This fundamental divergence in normative goals and theoretical foundations identified in this study presents significant theoretical and practical challenges for global emergency governance and offers a clear direction for future comparative legal studies.

With the rapid development of social media and online platforms, the speed and influence of emergency dissemination in cyberspace have significantly increased. The swift changes in public opinion, especially the phenomenon of opinion reversals, exert profound impacts on social stability and government credibility. The hypernetwork structure, characterized by its multilayered and multidimensional complexity, offers a new theoretical framework for analyzing multiagents and their interactions in the evolution of public opinion. Based on hypernetwork theory, this study constructs a four-layer subnet model encompassing user interaction network, event evolution network, semantic association network, and emotional conduction network. By extracting network structural features and conducting cross-layer linkage analysis, an identification system for public opinion reversals in emergencies is established. Taking the donation incident involving Hongxing Erke during the Henan rainstorm in 2021 as a case study, an empirical analysis of the public opinion reversal process is conducted. The research results indicate that the proposed hypernetwork model can effectively identify key nodes in public opinion reversals. The multi-indicator collaborative identification system for public opinion reversals aids in rapidly and effectively detecting signals of such reversals. This study not only provides new methodological support for the dynamic identification of public opinion reversals but also offers theoretical references and practical guidance for public opinion monitoring and emergency response decision-making in emergencies.

Data in power grid digital operation exhibit multisource heterogeneous characteristics, resulting in low integration efficiency and slow anomaly detection response. To address this, this paper proposes a method for power grid digital operation data integration based on K-medoids clustering. The basic service layer utilizes an Field Programmable Gate Array parallel architecture. This enables millisecond-level synchronous acquisition and dynamic preprocessing of multisource data, such as mechanical vibration, partial discharge signals, and temperature. The implementation is based on the analysis of the power grid digital operation structure. The data are then fed back to the cloud service layer, which, through business integration services, data analysis, and data access services, performs data filtering and analysis. Subsequently, the data are input to the application layer via the database server. The application layer employs a K-medoids clustering method that introduces a density-weighted Euclidean distance metric and an adaptive centroid selection strategy, significantly enhancing the clustering performance of multisource data. In particular, the proposed architecture supports real-time data processing and can be extended to cross-modal scenarios, including integration with speech-to-text systems in power grid monitoring. By aligning with low-latency neural network principles, this method facilitates timely decision-making in intelligent operation environments. Experiments confirm the method's efficacy. It acquires and integrates multisource heterogeneous power grid digital operation data effectively. The data throughput of different power grid digital operation data sources all exceed 110 MB/s. The silhouette coefficient of the integrated data sets is greater than 0.91, indicating that the integration of power grid digital operation data using this method exhibits good separability and reliability, enabling rapid detection of data anomalies within the power grid, thus laying a solid foundation for the operation and maintenance management of power grid digital operation.

Soybeans are a high-quality vegetable protein resource and a fundamental strategic material integral to the national economy and public livelihood. To investigate the research status of soybean quality evaluation, this study analyzes relevant literature from Web of Science and China Knowledge Network (2000-2024). Using bibliometric methods with Excel and VOSviewer, we examined publication years, keywords, authors, sources, countries/regions, and institutions, generating visualizations to intuitively illustrate the field's developmental status. Results indicate that over the past 25 years, soybean quality evaluation research has emerged as a focal point in crop science, with institutions predominantly located in China and the United States. Key journals in this domain include Food Chemistry, Frontiers in Plant Science, and Soybean Science, among others. Research primarily focuses on soybean physical characteristics and the component-quality relationship. Interdisciplinary advancements have positioned spectral analysis, intelligent systems, and multitechnology fusion as innovative frontiers in this field. These findings enhance researchers' understanding of current trends and support evidence-based decision-making in soybean quality evaluation.

This study aims to enhance the prediction precision of aircraft engine remaining useful life (RUL) by overcoming common challenges in current models, such as ineffective feature extraction and insufficient modeling of long-term temporal dependencies. We propose a novel multilayer hybrid architecture that combines bidirectional long short-term memory (BiLSTM) and gated recurrent unit (GRU) networks, augmented with an attention mechanism to enhance the model's focus on informative temporal patterns. In this framework, raw time series data are initially processed by the BiLSTM to extract bidirectional features associated with engine health conditions. The GRU network is subsequently used to effectively model long-range dependencies, thereby enriching the temporal representation. An adaptive attention module is included to assign varying importance to different features, allowing the model to focus on key indicators of engine condition. Evaluation results on the FD001 and FD003 datasets show that the model achieves root mean squared error reductions ranging from 8.81% to 30.60% and from 7.48% to 37.96%, validating its performance and robustness in RUL forecasting. In comparison with conventional BiLSTM and GRU models, the proposed BiLSTM-GRU-Attention architecture integrates attention-based feature weighting with a hybrid recurrent framework, thereby offering a concise and effective approach to RUL prediction for aircraft engines.

Human activities that generate greenhouse gas emissions pose a significant threat to urban green and sustainable development. Production activities in key industrial sectors are a primary contributor to high urban carbon emissions. Therefore, effectively reducing carbon emissions in these sectors is crucial for achieving urban carbon peak and neutrality goals. Carbon emission monitoring is a critical approach that aids governmental bodies in understanding changes in industrial carbon emissions, thereby supporting decision-making and carbon reduction efforts. However, current industry-oriented carbon monitoring methods suffer from issues such as low frequency, poor accuracy, and inadequate privacy security. To address these challenges, this article proposes a novel privacy-protected "electricity-carbon'' nexus model, long short-term memory with the vertical federated framework (VF-LSTM), to monitor carbon emissions in key urban industries. The vertical federated framework ensures "usable but invisible" privacy protection for multisource data from various participants. The embedded long short-term memory model accurately captures industry-specific carbon emissions. Using data from key industries (steel, petrochemical, chemical, and nonferrous industries), this article constructs and validates the performance of the proposed industry-level carbon emission monitoring model. The results demonstrate that the model has high accuracy and robustness, effectively monitoring industry carbon emissions while protecting data privacy.

Decision sciences (DSC) involves studying complex dynamic systems and processes to aid informed choices subject to constraints in uncertain conditions. It integrates multidisciplinary methods and strategies to evaluate decision engineering processes, identifying alternatives and providing insights toward enhancing prudent decision-making. This study analyzes the evolutionary trends and innovation in DSC education and research trends over the past 25 years. Using metadata from bibliographic records and employing the science mapping method and text analytics, we map and evaluate the thematic, intellectual, and social structures of DSC research. The results identify "knowledge management," "decision support systems," "data envelopment analysis," "simulation," and "artificial intelligence" (AI) as some of the prominent critical skills and knowledge requirements for problem-solving in DSC before and during the period (2000-2024). However, these technologies are evolving significantly in the recent wave of digital transformation, with data analytics frameworks (including techniques such as big data analytics, machine learning, business intelligence, data mining, and information visualization) becoming crucial. DSC education and research continue to mirror the development in practice, with sustainable education through virtual/online learning becoming prominent. Innovative pedagogical approaches/strategies also include computer simulation and games ("play and learn" or "role-playing"). The current era witnesses AI adoption in different forms as conversational Chatbot agent and generative AI (GenAI), such as chat generative pretrained transformer in teaching, learning, and scholarly activities amidst challenges (academic integrity, plagiarism, intellectual property violations, and other ethical and legal issues). Future DSC education must innovatively integrate GenAI into DSC education and address the resulting challenges.

Our online lives generate a wealth of behavioral records-digital footprints-which are stored and leveraged by technology platforms. These data can be used to create value for users by personalizing services. At the same time, however, it also poses a threat to people's privacy by offering a highly intimate window into their private traits (e.g., their personality, political ideology, sexual orientation). We explore the concept of cloaking: allowing users to hide parts of their digital footprints from predictive algorithms, to prevent unwanted inferences. This article addresses two open questions: (i) can cloaking be effective in the longer term, as users continue to generate new digital footprints? And (ii) what is the potential impact of cloaking on the accuracy of desirable inferences? We introduce a novel strategy focused on cloaking "metafeatures" and compare its efficacy against just cloaking the raw footprints. The main findings are (i) while cloaking effectiveness does indeed diminish over time, using metafeatures slows the degradation; (ii) there is a tradeoff between privacy and personalization: cloaking undesired inferences also can inhibit desirable inferences. Furthermore, the metafeature strategy-which yields more stable cloaking-also incurs a larger reduction in desirable inferences.