Big Data Research最新文献_第9页

Scholar's Career Switch from Academia to Industry: Mining and Analysis from AMiner 学者从学术界到工业界的职业转换：来自 AMiner 的挖掘和分析

IF 3.3 3区计算机科学

Big Data Research Pub Date : 2024-02-19 DOI: 10.1016/j.bdr.2024.100441

Zhou Shao , Sha Yuan , Yinyu Jin , Yongli Wang

{"title":"Scholar's Career Switch from Academia to Industry: Mining and Analysis from AMiner","authors":"Zhou Shao , Sha Yuan , Yinyu Jin , Yongli Wang","doi":"10.1016/j.bdr.2024.100441","DOIUrl":"10.1016/j.bdr.2024.100441","url":null,"abstract":"<div><p>The phenomenon of scholars switching their careers from academia to industry has become more prevalent nowadays. This paper proposes a combination approach of bibliometrics analysis and data mining to study the phenomenon from the perspective of Science of Science (SciSci). Based on the proposed methods, this paper first provides an overview of frequent companies and frequent universities as well as the exponentially increasing number of scholars under the scenario. And then, this study uncovers the excessively single patterns in South Korean scholars switches using frequent pattern mining from their papers. This paper studies the knowledge and technology transfer (KTT) and the research change of scholars by using the language model, the result of which illustrates that the research difference between industry and academia gradually decreases and reaches a steady state in recent years. In exploring the driving factors of the phenomenon, deep preliminary cooperation may be an essential reason, and the career switches will not promote the published amounts of papers but may benefit its academic influence. This study should, therefore, be of value to researchers wishing to study the academia-industry career switches more intensely.</p></div>","PeriodicalId":56017,"journal":{"name":"Big Data Research","volume":"36 ","pages":"Article 100441"},"PeriodicalIF":3.3,"publicationDate":"2024-02-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139922786","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Interactive big data visualization and analytics 交互式大数据可视化和分析

IF 3.3 3区计算机科学

Big Data Research Pub Date : 2024-02-14 DOI: 10.1016/j.bdr.2024.100445

David Auber , Nikos Bikakis , Panos K. Chrysanthis , George Papastefanatos , Mohamed Sharaf

引用次数: 0

A big data driven vegetation disease and pest region identification method based on self supervised convolutional neural networks and parallel extreme learning machines 基于自监督卷积神经网络和并行极限学习机的大数据驱动型植被病虫害区域识别方法

IF 3.3 3区计算机科学

Big Data Research Pub Date : 2024-02-13 DOI: 10.1016/j.bdr.2024.100444

Bo Jiang , Hao Wang , Hanxu Ma

{"title":"A big data driven vegetation disease and pest region identification method based on self supervised convolutional neural networks and parallel extreme learning machines","authors":"Bo Jiang , Hao Wang , Hanxu Ma","doi":"10.1016/j.bdr.2024.100444","DOIUrl":"10.1016/j.bdr.2024.100444","url":null,"abstract":"<div><p>A self supervised convolutional neural network-parallel extreme learning machine classification model based on big data is proposed to address the subjectivity and inaccuracy of traditional methods for identifying vegetation pests and diseases that rely on manual observation and empirical judgment. This model is constructed using convolutional neural networks and parallel extreme learning machines, and integrates feature extraction networks with dual attention mechanisms to improve the accuracy of identifying pests and diseases. The model utilized a large amount of big data for training, achieving a recall rate of 98.42 % on multispectral datasets, and an overall classification accuracy of 99.04 %. After optimizing the residual network, the overall accuracy of identifying vegetation pest and disease areas has been further improved to 99.77 %, and the recall rate has also reached 98.91 %. These results indicate that the method proposed in this study has high accuracy and efficiency in the application of big data, can meet the needs of disease and pest identification, and provides effective technical support for the monitoring and prevention of crop diseases and pests, which has important practical significance.</p></div>","PeriodicalId":56017,"journal":{"name":"Big Data Research","volume":"36 ","pages":"Article 100444"},"PeriodicalIF":3.3,"publicationDate":"2024-02-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139887525","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Knowledge Distillation via Token-Level Relationship Graph Based on the Big Data Technologies 基于大数据技术的令牌级关系图知识提炼

IF 3.3 3区计算机科学

Big Data Research Pub Date : 2024-02-12 DOI: 10.1016/j.bdr.2024.100438

Shuoxi Zhang , Hanpeng Liu , Kun He

{"title":"Knowledge Distillation via Token-Level Relationship Graph Based on the Big Data Technologies","authors":"Shuoxi Zhang , Hanpeng Liu , Kun He","doi":"10.1016/j.bdr.2024.100438","DOIUrl":"https://doi.org/10.1016/j.bdr.2024.100438","url":null,"abstract":"<div><p>In the big data era, characterized by vast volumes of complex data, the efficiency of machine learning models is of utmost importance, particularly in the context of intelligent agriculture. Knowledge distillation (KD), a technique aimed at both model compression and performance enhancement, serves as a pivotal solution by distilling the knowledge from an elaborate model (teacher) to a lightweight, compact counterpart (student). However, the true potential of KD has not been fully explored. Existing approaches primarily focus on transferring instance-level information by big data technologies, overlooking the valuable information embedded in token-level relationships, which may be particularly affected by the long-tail effects. To address the above limitations, we propose a novel method called Knowledge Distillation with Token-level Relationship Graph (TRG) that leverages token-wise relationships to enhance the performance of knowledge distillation. By employing TRG, the student model can effectively emulate higher-level semantic information from the teacher model, resulting in improved performance and mobile-friendly efficiency. To further enhance the learning process, we introduce a dynamic temperature adjustment strategy, which encourages the student model to capture the topology structure of the teacher model more effectively. We conduct experiments to evaluate the effectiveness of the proposed method against several state-of-the-art approaches. Empirical results demonstrate the superiority of TRG across various visual tasks, including those involving imbalanced data. Our method consistently outperforms the existing baselines, establishing a new state-of-the-art performance in the field of KD based on big data technologies.</p></div>","PeriodicalId":56017,"journal":{"name":"Big Data Research","volume":"36 ","pages":"Article 100438"},"PeriodicalIF":3.3,"publicationDate":"2024-02-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139737402","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Attentive Implicit Relation Embedding for Event Recommendation in Event-Based Social Network 为基于事件的社交网络中的事件推荐嵌入注意隐含关系

IF 3.3 3区计算机科学

Big Data Research Pub Date : 2024-02-05 DOI: 10.1016/j.bdr.2024.100426

Yuan Liang

{"title":"Attentive Implicit Relation Embedding for Event Recommendation in Event-Based Social Network","authors":"Yuan Liang","doi":"10.1016/j.bdr.2024.100426","DOIUrl":"10.1016/j.bdr.2024.100426","url":null,"abstract":"<div><p>The <u>e</u>vent-<u>b</u>ased <u>s</u>ocial <u>n</u>etwork (EBSN) is a new type of social network that combines online and offline networks, and its primary goal is to recommend appropriate events to users. Most studies do not model event recommendations on the EBSN platform as graph representation learning, nor do they consider the implicit relationship between events, resulting in recommendations that are not accepted by users. Thus, we study graph representation learning, which integrates implicit relationships between social networks and events. First, we propose an algorithm that integrates implicit relationships between social networks and events based on a multiple attention model. The graph structure that integrates implicit relationships between social networks and events is divided into user modeling and event modeling: modeling the interactive information of user events, user social relationships, and implicit relationships between users in user modeling; modeling user information and implicit relationships between events in event modeling; and deeply mining high-level transfer relationships between users and events. Then, the user modeling and event modeling models are fused using a multiattention joint learning mechanism to capture the different impacts of social and implicit relationships on user preferences, improving the recommendation quality of the recommendation system. Finally, the effectiveness of the proposed algorithm is verified in real datasets.</p></div>","PeriodicalId":56017,"journal":{"name":"Big Data Research","volume":"36 ","pages":"Article 100426"},"PeriodicalIF":3.3,"publicationDate":"2024-02-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139688835","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Chlorophyll-a concentration variations in Bohai sea: Impacts of environmental complexity and human activities based on remote sensing technologies 渤海叶绿素 a 浓度变化：基于遥感技术的环境复杂性和人类活动的影响

IF 3.3 3区计算机科学

Big Data Research Pub Date : 2024-02-03 DOI: 10.1016/j.bdr.2024.100440

Yong Du , Xiaoyu Zhang , Shuchang Ma , Nan Yao

{"title":"Chlorophyll-a concentration variations in Bohai sea: Impacts of environmental complexity and human activities based on remote sensing technologies","authors":"Yong Du , Xiaoyu Zhang , Shuchang Ma , Nan Yao","doi":"10.1016/j.bdr.2024.100440","DOIUrl":"10.1016/j.bdr.2024.100440","url":null,"abstract":"<div><p>This study extensively explores the intricate dynamics of the Bohai Sea ecosystem, a semi-closed marginal sea in China, influenced by both environmental complexity and human activities. By utilizing chlorophyll-a as an indicator, we closely examine how phytoplankton responds to coastal environmental conditions and stressors. The temporal analysis conducted over the 23-year period from 1998 to 2020 reveals a distinctive \"bell-shaped\" variation in chlorophyll-a concentration. Spatially, a declining trend is observed from coastal to central regions, characterized by widespread low-value areas. Employing M-K and slope trend analyses, we observe a 42.13 % decline in the northern Bohai Sea, contrasting with a significant 57.87 % increase in the central and southern regions. The innovative aspects of this research lie in identifying the complex interplay between chlorophyll-a concentration, human pollution controls, and nutrient inputs. Factors contributing to chlorophyll-a concentration, ranked by significance, include sea surface temperature, photosynthetically available radiation (PAR), and wind speed. Remarkably, the negligible impact of the \"2015 Tianjin explosion\" underscores the robustness of the Bohai Sea's chlorophyll-a dynamics. Furthermore, the positive correlation between phosphorus input and chlorophyll classifies Bohai Bay as a phosphorus-limited aquatic ecosystem. In conclusion, this study provides crucial insights for the preservation of the Bohai Sea ecosystem, emphasizing the necessity for ongoing monitoring and management strategies in the face of evolving environmental and anthropogenic influences.</p></div>","PeriodicalId":56017,"journal":{"name":"Big Data Research","volume":"36 ","pages":"Article 100440"},"PeriodicalIF":3.3,"publicationDate":"2024-02-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139663075","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Tropical cyclone trajectory based on satellite remote sensing prediction and time attention mechanism ConvLSTM model 基于卫星遥感预测和时间注意机制 ConvLSTM 模型的热带气旋轨迹

IF 3.3 3区计算机科学

Big Data Research Pub Date : 2024-02-03 DOI: 10.1016/j.bdr.2024.100439

Tongfei Li , Mingzheng Lai , Shixian Nie , Haifeng Liu , Zhiyao Liang , Wei Lv

{"title":"Tropical cyclone trajectory based on satellite remote sensing prediction and time attention mechanism ConvLSTM model","authors":"Tongfei Li , Mingzheng Lai , Shixian Nie , Haifeng Liu , Zhiyao Liang , Wei Lv","doi":"10.1016/j.bdr.2024.100439","DOIUrl":"10.1016/j.bdr.2024.100439","url":null,"abstract":"<div><p>The accurate and timely prediction of tropical cyclones is of paramount importance in mitigating the impact of these catastrophic meteorological events. Presently, methods for predicting tropical cyclones based on satellite remote sensing images encounter notable challenges, including the inadequate extraction of three-dimensional spatial features and limitations in long-term forecasting. As a response to these challenges, this study introduces the Temporal Attention Mechanism ConvLSTM (TAM-CL) model, designed to conduct thorough spatiotemporal feature extraction on three-dimensional atmospheric reanalysis data of tropical cyclones. By leveraging ConvLSTM with three-dimensional convolution kernels, our model enhances the extraction of three-dimensional spatiotemporal features. Furthermore, an attention mechanism is integrated to bolster long-term prediction accuracy by emphasizing crucial temporal nodes. In the evaluation of tropical cyclone track and intensity forecasts across 24, 48, and 72 h, TAM-CL demonstrates a notable reduction in prediction errors, thereby underscoring its efficacy in forecasting both cyclone tracks and intensities. This contributes to an effective exploration of the application of deep networks in conjunction with atmospheric reanalysis data.</p></div>","PeriodicalId":56017,"journal":{"name":"Big Data Research","volume":"36 ","pages":"Article 100439"},"PeriodicalIF":3.3,"publicationDate":"2024-02-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139662985","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Graph Spatial-Temporal Transformer Network for Traffic Prediction 用于交通预测的图时空变换器网络

IF 3.3 3区计算机科学

Big Data Research Pub Date : 2024-01-26 DOI: 10.1016/j.bdr.2024.100427

Zhenzhen Zhao , Guojiang Shen , Lei Wang , Xiangjie Kong

{"title":"Graph Spatial-Temporal Transformer Network for Traffic Prediction","authors":"Zhenzhen Zhao , Guojiang Shen , Lei Wang , Xiangjie Kong","doi":"10.1016/j.bdr.2024.100427","DOIUrl":"10.1016/j.bdr.2024.100427","url":null,"abstract":"<div><p><span>Traffic information can reflect the operating status of a city, and accurate traffic forecasting is critical in intelligent transportation systems (ITS) and urban planning. However, traffic information has complex nonlinearity and dynamic spatial-temporal dependencies due to human mobility, bringing new traffic forecasting challenges. This paper proposed a graph spatial-temporal transformer network for </span>traffic prediction<span> (GSTTN) to cope with the above problems. Specifically, the proposed framework explores spatial characteristics of the across-road network of traffic information hidden in human behavior patterns via a multi-view graph convolutional network<span> (GCN). Furthermore, the transformer network with a multi-head attention mechanism is adopted to capture the random disturbance in the time series characteristics of traffic information. As a result, these two components can be used to model spatial relations and temporal trends. Finally, we examine real-world datasets, and the experiments show that the proposed framework outperforms the current state-of-the-art baselines.</span></span></p></div>","PeriodicalId":56017,"journal":{"name":"Big Data Research","volume":"36 ","pages":"Article 100427"},"PeriodicalIF":3.3,"publicationDate":"2024-01-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139582754","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Airspace situation analysis of terminal area traffic flow prediction based on big data and machine learning methods 基于大数据和机器学习方法的终端区交通流预测空域态势分析

IF 3.3 3区计算机科学

Big Data Research Pub Date : 2024-01-18 DOI: 10.1016/j.bdr.2024.100425

Yandong Li , Bo Jiang , Weilong Liu , Chenglong Li , Yunfan Zhou

{"title":"Airspace situation analysis of terminal area traffic flow prediction based on big data and machine learning methods","authors":"Yandong Li , Bo Jiang , Weilong Liu , Chenglong Li , Yunfan Zhou","doi":"10.1016/j.bdr.2024.100425","DOIUrl":"10.1016/j.bdr.2024.100425","url":null,"abstract":"<div><p>Real-time and accurate prediction of terminal area arrival traffic flow is a key issue for terminal area traffic management. In this paper, we study the advantages and disadvantages of traditional dynamics-based prediction methods and time-series based prediction methods in the first step. Taking the advantages of the two type of methods, a terminal area arrival flow prediction framework based on airspace situation is proposed. In our method, the airspace situation is used as the machine learning feature to estimate the number of arrival aircraft. In addition, also based on machine learning approach, a correction stage is added to the algorithm to improve the accuracy of the prediction. ADS-B data collected from the terminal area of Chengdu is used to study the prediction accuracy based on different machine learning algorithms in the proposed framework. Experimental results show that the proposed method can predict the air traffic flow accurately. The average absolute error is only 0.35 aircraft/15 min, the root mean square error is 0.67 aircraft/15 min, and the maximum absolute error is 2 aircraft/15 min. Compared with the AOL method, our proposed method improves the accuracy of prediction by a margin of 90 % and 60 % according to the evaluation metrics of MAE and MAXAE, respectively.</p></div>","PeriodicalId":56017,"journal":{"name":"Big Data Research","volume":"35 ","pages":"Article 100425"},"PeriodicalIF":3.3,"publicationDate":"2024-01-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2214579624000017/pdfft?md5=399453e55e15e7b2fc74c8ad5fce66dc&pid=1-s2.0-S2214579624000017-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139509236","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

The Predictability of Stock Price: Empirical Study on Tick Data in Chinese Stock Market 股票价格的可预测性:基于中国股票市场波动数据的实证研究

IF 3.3 3区计算机科学

Big Data Research Pub Date : 2023-11-17 DOI: 10.1016/j.bdr.2023.100414

Yueshan Chen , Xingyu Xu , Tian Lan , Sihai Zhang

{"title":"The Predictability of Stock Price: Empirical Study on Tick Data in Chinese Stock Market","authors":"Yueshan Chen , Xingyu Xu , Tian Lan , Sihai Zhang","doi":"10.1016/j.bdr.2023.100414","DOIUrl":"https://doi.org/10.1016/j.bdr.2023.100414","url":null,"abstract":"<div><p>Whether or not stocks are predictable has been a topic of concern for decades. The efficient market hypothesis (EMH) says that it is difficult for investors to make extra profits by predicting stock prices, but this may not be true, especially for the Chinese stock market. Therefore, we explore the predictability of the Chinese stock market based on tick data, a widely studied high-frequency data. We obtain the predictability of 3, 834 Chinese stocks by adopting the concept of true entropy, which is calculated by Limpel-Ziv data compression method. The Markov chain model and the diffusion kernel model are used to compare the upper bounds on predictability, and it is concluded that there is still a significant performance gap between the forecasting models used and the theoretical upper bounds. Our work shows that more than 73% of stocks have prediction accuracy greater than 70% and RMSE less than 2 CNY under different quantification intervals with different models. We further take Spearman's correlation to reveal that the average stock price and price volatility may have a negative impact on prediction accuracy, which may be helpful for stock investors.</p></div>","PeriodicalId":56017,"journal":{"name":"Big Data Research","volume":"35 ","pages":"Article 100414"},"PeriodicalIF":3.3,"publicationDate":"2023-11-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2214579623000473/pdfft?md5=df49b0edd2f0330b446f4870f4a82ce5&pid=1-s2.0-S2214579623000473-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138413020","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0