IEEE Transactions on Big Data最新文献_第8页

Online Non-Stationary Pricing Incentives for Budget-Limited Crowdsensing 预算有限的群体感知的在线非平稳定价激励

IF 7.5 3区计算机科学

IEEE Transactions on Big Data Pub Date : 2024-12-26 DOI: 10.1109/TBDATA.2024.3522804

Jiajun Sun;Dianliang Wu

{"title":"Online Non-Stationary Pricing Incentives for Budget-Limited Crowdsensing","authors":"Jiajun Sun;Dianliang Wu","doi":"10.1109/TBDATA.2024.3522804","DOIUrl":"https://doi.org/10.1109/TBDATA.2024.3522804","url":null,"abstract":"The promising applications of mobile crowdsensing (MCS) have attracted much research interest recently, especially for the posted-pricing scenes. However, existing works mainly focus on the stationary MCS, no matter whether in a stochastic or adversarial environment, where each price (or arm) remains identical over time. However, in many realistic MCS applications such as environment monitoring and recommendation systems, stationary bandits do not model the posted-pricing sequential decision problems where the reward distributions of each price (arm) and cost distribution vary over time due to the changes in light intensity and mobile devices’ remnant energy. While in this paper, we study a more general submodular crowdsensing scene to address the non-stationary sequential pricing problems, and construct a monotonic submodular function merging the marginal reward and temporal difference errors (TD-errors) of deep reinforcement learning (DRL). Moreover, we explore a weighted budget-limited non-stationary pricing mechanism by using the deep deterministic policy gradient (DDPG) method for submodular MCS from the perspectives of the hard-drop and soft-drop weights. Our mechanism can readily be extended to non-submodular MCS or other MCS scenes. Extensive simulations demonstrate that our mechanism outweighs existing benchmarks.","PeriodicalId":13106,"journal":{"name":"IEEE Transactions on Big Data","volume":"11 4","pages":"2025-2035"},"PeriodicalIF":7.5,"publicationDate":"2024-12-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144597723","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

Tailored Definitions With Easy Reach: Complexity-Controllable Definition Generation 定制定义与容易达到：复杂可控的定义生成

IF 7.5 3区计算机科学

IEEE Transactions on Big Data Pub Date : 2024-12-26 DOI: 10.1109/TBDATA.2024.3522805

Liner Yang;Jiaxin Yuan;Cunliang Kong;Jingsi Yu;Ruining Chong;Zhenghao Liu;Erhong Yang

{"title":"Tailored Definitions With Easy Reach: Complexity-Controllable Definition Generation","authors":"Liner Yang;Jiaxin Yuan;Cunliang Kong;Jingsi Yu;Ruining Chong;Zhenghao Liu;Erhong Yang","doi":"10.1109/TBDATA.2024.3522805","DOIUrl":"https://doi.org/10.1109/TBDATA.2024.3522805","url":null,"abstract":"The task of complexity-controllable definition generation refers to providing definitions with different readability for words in specific contexts. This task can be utilized to help language learners eliminate reading barriers and facilitate language acquisition. However, the available training data for this task remains scarce due to the difficulty of obtaining reliable definition data and the high cost of data standardization. To tackle those challenges, we introduce a general solution from both the data-driven and method-driven perspectives. We construct a large-scale standard Chinese dataset, COMPILING, which contains both difficult and simple definitions and can serve as a benchmark for future research. Besides, we propose a multitasking framework SimpDefiner for unsupervised controllable definition generation. By designing a parameter-sharing scheme between two decoders, the framework can extract the complexity information from the non-parallel corpus. Moreover, we propose the SimpDefiner guided prompting (SGP) method, where simple definitions generated by SimpDefiner are utilized to construct prompts for GPT-4, hence obtaining more realistic and contextually appropriate definitions. The results demonstrate SimpDefiner's outstanding ability to achieve controllable generation and better results could be achieved when GPT-4 is incorporated.","PeriodicalId":13106,"journal":{"name":"IEEE Transactions on Big Data","volume":"11 4","pages":"2061-2071"},"PeriodicalIF":7.5,"publicationDate":"2024-12-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144597737","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

MC-GNN: Multi-Channel Graph Neural Networks With Hilbert-Schmidt Independence Criterion MC-GNN：具有Hilbert-Schmidt独立准则的多通道图神经网络

IF 7.5 3区计算机科学

IEEE Transactions on Big Data Pub Date : 2024-12-26 DOI: 10.1109/TBDATA.2024.3522817

Shicheng Cui;Deqiang Li;Jing Zhang

{"title":"MC-GNN: Multi-Channel Graph Neural Networks With Hilbert-Schmidt Independence Criterion","authors":"Shicheng Cui;Deqiang Li;Jing Zhang","doi":"10.1109/TBDATA.2024.3522817","DOIUrl":"https://doi.org/10.1109/TBDATA.2024.3522817","url":null,"abstract":"Graph Neural Networks (GNNs) have been proven to be useful for learning graph-based knowledge. However, one of the drawbacks of GNN techniques is that they may get stuck in the problem of over-squashing. Recent studies attribute to the message passing paradigm that it may amplify some specific local relations and distort long-range information under a certain GNN. To alleviate such phenomena, we propose a novel and general GNN framework, dubbed MC-GNN, which introduces the multi-channel neural architecture to learn and fuse multi-view graph-based information. The purpose of MC-GNN is to extract distinct channel-based graph features and adaptively adjust the importance of the features. To this end, we use the Hilbert-Schmidt Independence Criterion (HSIC) to enlarge the disparity between the embeddings encoded by each channel and follow an attention mechanism to fuse the embeddings with adaptive weight adjustment. MC-GNN can apply multiple GNN backbones, which provides a solution for learning structural relations from a multi-view perspective. Experimental results demonstrate that the proposed MC-GNN is superior to the compared state-of-the-art GNN methods.","PeriodicalId":13106,"journal":{"name":"IEEE Transactions on Big Data","volume":"11 4","pages":"2036-2045"},"PeriodicalIF":7.5,"publicationDate":"2024-12-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144597728","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

Detection of Rumors and Their Sources in Social Networks: A Comprehensive Survey 社交网络中谣言的检测及其来源：一个综合调查

IF 7.5 3区计算机科学

IEEE Transactions on Big Data Pub Date : 2024-12-25 DOI: 10.1109/TBDATA.2024.3522801

Otabek Sattarov;Jaeyoung Choi

{"title":"Detection of Rumors and Their Sources in Social Networks: A Comprehensive Survey","authors":"Otabek Sattarov;Jaeyoung Choi","doi":"10.1109/TBDATA.2024.3522801","DOIUrl":"https://doi.org/10.1109/TBDATA.2024.3522801","url":null,"abstract":"With the recent advancements in social network platform technology, an overwhelming amount of information is spreading rapidly. In this situation, it can become increasingly difficult to discern what information is false or true. If false information proliferates significantly, it can lead to undesirable outcomes. Hence, when we receive some information, we can pose the following two questions: <inline-formula><tex-math>$(i)$</tex-math></inline-formula> Is the information true? <inline-formula><tex-math>$(ii)$</tex-math></inline-formula> If not, who initially spread that information? The first problem is the rumor detection issue, while the second is the rumor source detection problem. A rumor-detection problem involves identifying and mitigating false or misleading information spread via various communication channels, particularly online platforms and social media. Rumors can range from harmless ones to deliberately misleading content aimed at deceiving or manipulating audiences. Detecting misinformation is crucial for maintaining the integrity of information ecosystems and preventing harmful effects such as the spread of false beliefs, polarization, and even societal harm. Therefore, it is very important to quickly distinguish such misinformation while simultaneously finding its source to block it from spreading on the network. However, most of the existing surveys have analyzed these two issues separately. In this work, we first survey the existing research on the rumor-detection and rumor source detection problems with joint detection approaches, simultaneously. This survey deals with these two issues together so that their relationship can be observed and it provides how the two problems are similar and different. The limitations arising from the rumor detection, rumor source detection, and their combination problems are also explained, and some challenges to be addressed in future works are presented.","PeriodicalId":13106,"journal":{"name":"IEEE Transactions on Big Data","volume":"11 3","pages":"1528-1547"},"PeriodicalIF":7.5,"publicationDate":"2024-12-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143949328","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

Cost-Aware Triangle Counting Over Geo-Distributed Datacenters 地理分布数据中心上的成本意识三角计数

IF 7.5 3区计算机科学

IEEE Transactions on Big Data Pub Date : 2024-12-25 DOI: 10.1109/TBDATA.2024.3522816

Delong Ma;Ye Yuan;Yanfeng Zhang;Chunze Cao;Yuliang Ma

{"title":"Cost-Aware Triangle Counting Over Geo-Distributed Datacenters","authors":"Delong Ma;Ye Yuan;Yanfeng Zhang;Chunze Cao;Yuliang Ma","doi":"10.1109/TBDATA.2024.3522816","DOIUrl":"https://doi.org/10.1109/TBDATA.2024.3522816","url":null,"abstract":"Counting triangles is an important topic in many practical applications, such as anomaly detection, community search, and recommendation systems. For triangle counting in large and dynamic graphs, recent work has focused on distributed streaming algorithms. These works assume that the graph is processed in the same location, while in reality, the graph stream may be generated and processed in datacenters that are geographically distributed. This raises new challenges to existing triangle counting algorithms, due to the multi-level heterogeneities in network bandwidth and communication prices in geo-distributed datacenters. In this article, we propose a cost-aware framework named <inline-formula><tex-math>${sf GeoTri}$</tex-math></inline-formula> based on the Master-Worker-Aggregator architecture, which takes both the cost and performance objectives into consideration for triangle counting in geo-distributed datacenters. The two core parts of this framework are the cost-aware nodes assignment strategy in master, which is critical to obtain node's position and distribute edges reasonably to reduce the cost (i.e., time cost and monetary cost), and cost-aware neighbor transfer strategy among workers, which further eliminates redundancy in data transfers. Additionally, we conduct extensive experiments on seven real-world graphs, and the results demonstrate that <inline-formula><tex-math>${sf GeoTri}$</tex-math></inline-formula> significantly lowers both runtime and monetary cost while exhibiting nice accuracy and scalability.","PeriodicalId":13106,"journal":{"name":"IEEE Transactions on Big Data","volume":"11 4","pages":"2008-2024"},"PeriodicalIF":7.5,"publicationDate":"2024-12-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144597619","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

Towards Fraud Detection via Fine-Grained Classification of User Behavior 基于用户行为细粒度分类的欺诈检测

IF 7.5 3区计算机科学

IEEE Transactions on Big Data Pub Date : 2024-12-13 DOI: 10.1109/TBDATA.2024.3517313

Xinzhi Wang;Hang Yu;Jiayu Guo;Pengbo Li;Xiangfeng Luo

{"title":"Towards Fraud Detection via Fine-Grained Classification of User Behavior","authors":"Xinzhi Wang;Hang Yu;Jiayu Guo;Pengbo Li;Xiangfeng Luo","doi":"10.1109/TBDATA.2024.3517313","DOIUrl":"https://doi.org/10.1109/TBDATA.2024.3517313","url":null,"abstract":"The mass volume of data in the modern business world requires fraud detection to be automated. Hence, some researchers constructed the fraud scenario into graph data and proposed graph-based fraud detection methods. These methods treat the problem of fraud detection as a binary node classification task. However, the differences between the nodes of the same class are ignored. In this paper, we try to distinguish differences in behavior among nodes of the same class to improve the model’s ability to detect deviation, i.e., we make a fine-grained classification of user behavior (called prototypes) and propose an adaptive prototype-based graph neural network (APGNN) for fraud detection. APGNN learns node behavior representations by extracting both neighborhood and global information, supplying preliminary knowledge for the adaptive creation of several prototypes, each representing a distinct behavior pattern. Subsequently, a new loss function is employed to enhance the prototypes’ capacity to capture these behavior patterns and to amplify the feature differences between different prototypes. Nodes are then projected onto these prototypes to derive the final behavior patterns. Extensive experiments on four real-world datasets show that this method can provide better fraud detection as well as a more understandable result.","PeriodicalId":13106,"journal":{"name":"IEEE Transactions on Big Data","volume":"11 4","pages":"1994-2007"},"PeriodicalIF":7.5,"publicationDate":"2024-12-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144597738","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 Prompt Learning Method for the Demand-Responsive Transport Routing Problem 需求响应运输路线问题的图提示学习方法

IF 7.5 3区计算机科学

IEEE Transactions on Big Data Pub Date : 2024-12-09 DOI: 10.1109/TBDATA.2024.3512951

Ke Zhang;Meng Li

{"title":"Graph Prompt Learning Method for the Demand-Responsive Transport Routing Problem","authors":"Ke Zhang;Meng Li","doi":"10.1109/TBDATA.2024.3512951","DOIUrl":"https://doi.org/10.1109/TBDATA.2024.3512951","url":null,"abstract":"Demand Responsive Transport (DRT) plays a crucial role in mitigating the inefficiencies of current public transit systems. Efficient routing is paramount for enhancing the flexibility and applicability of this transportation mode. Machine learning techniques, such as the attention-based encoder-decoder methodology, have the capability to produce solutions within seconds after offline training. However, these algorithms encounter convergence issues during training process, and demonstrate limited generalization ability, particularly across different scales. Thus, this paper proposes a graph prompt learning-based method comprising an information encoder, token generation, and token mapping to effectively train models that can adapt to diverse vehicles and demand variations. Particularly, token generation considers the characteristics of the problem by integrating vehicle and customer urgency information each time step. Token mapping obtains vehicle decoding sequences through attention mechanisms and mask function. The proposed model's performance is comprehensively evaluated against commonly baselines across various request contexts. Results show that our method can significantly reduce the computational time, and improve the quality of routing solution compared with baselines. Overall, the proposed model can enhance the routing efficiency of DRT systems through token mapping and prompts design.","PeriodicalId":13106,"journal":{"name":"IEEE Transactions on Big Data","volume":"11 4","pages":"1983-1993"},"PeriodicalIF":7.5,"publicationDate":"2024-12-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144597788","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

A Privacy-Preserving Large-Scale Image Retrieval Framework With Vision GNN Hashing 基于视觉GNN哈希的大规模图像检索框架

IF 7.5 3区计算机科学

IEEE Transactions on Big Data Pub Date : 2024-11-27 DOI: 10.1109/TBDATA.2024.3505052

Yuan Cao;Fanlei Meng;Xinzheng Shang;Jie Gui;Yuan Yan Tang

{"title":"A Privacy-Preserving Large-Scale Image Retrieval Framework With Vision GNN Hashing","authors":"Yuan Cao;Fanlei Meng;Xinzheng Shang;Jie Gui;Yuan Yan Tang","doi":"10.1109/TBDATA.2024.3505052","DOIUrl":"https://doi.org/10.1109/TBDATA.2024.3505052","url":null,"abstract":"With the growing popularity of cloud services, companies and individuals outsource images to cloud servers to reduce storage and computing burdens. The images are encrypted before outsourcing for privacy protection. It has become urgent to solve the privacy-preserving image retrieval problem on the cloud. There are three main challenges in this area. First, how can we achieve high retrieval accuracy on the encryption domain? Second, how can we improve efficiency in large-scale encrypted image retrieval? Third, how can we ensure the reliability of the retrieval results? The existing schemes only consider some of these characteristics and the retrieval accuracy is insufficient. In this paper, we propose a privacy-preserving large-scale image retrieval framework with vision graph convolutional neural network hashing (ViGH). To the best of our knowledge, this is the first framework that is able to address all the above challenges with more advanced accuracy performance. To be specific, cycle-consistent adversarial networks and vision graph convolutional networks (ViG) are utilized to increase retrieval accuracy. By embedding encrypted images into hash codes, we can obtain high retrieval efficiency by Hamming distances. Cloud servers store the hash codes on the blockchain (Ethereum). The retrieval algorithm on the smart contracts and the consensus mechanism of blockchain ensure reliability of the retrieval results. The experimental results on three common datasets verify the effectiveness and efficiency of the proposed privacy-preserving image retrieval framework. The reliability of the retrieval results is ensured by the consensus mechanism of blockchain with no need for verification.","PeriodicalId":13106,"journal":{"name":"IEEE Transactions on Big Data","volume":"11 4","pages":"1970-1982"},"PeriodicalIF":7.5,"publicationDate":"2024-11-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144597810","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

Robust Privacy-Preserving Federated Item Ranking in Online Marketplaces: Exploiting Platform Reputation for Effective Aggregation 在线市场中健壮的隐私保护联合项目排名：利用平台声誉进行有效聚合

IF 7.5 3区计算机科学

IEEE Transactions on Big Data Pub Date : 2024-11-22 DOI: 10.1109/TBDATA.2024.3505055

Guilherme Ramos;Ludovico Boratto;Mirko Marras

{"title":"Robust Privacy-Preserving Federated Item Ranking in Online Marketplaces: Exploiting Platform Reputation for Effective Aggregation","authors":"Guilherme Ramos;Ludovico Boratto;Mirko Marras","doi":"10.1109/TBDATA.2024.3505055","DOIUrl":"https://doi.org/10.1109/TBDATA.2024.3505055","url":null,"abstract":"Online marketplaces often collect products to sell from several other platforms (and sellers) and produce a unique ranking/score of these products to users. Keeping as private the user preferences provided in each (individual) platform is a need and a challenge at the same time. We are currently used to rating items in the marketplace itself which, in turn, can produce more effective rankings. Hence, the shaping of an effective item ranking would require a sharing of the user ratings between the individual platforms and the marketplace, thus impacting users’ privacy. In this paper, we propose the initial steps towards a change of paradigm, where the ratings are kept as private in each platform. Under this paradigm, each platform produces its rankings, then aggregated by the marketplace, in a federated fashion. To ensure that the marketplace’s rankings maintain their effectiveness, we exploit the concept of <italic>reputation of the individual platform</i>, so that the final marketplace ranking is weighted by the reputation of each platform providing its ranking. Experiments on three datasets, covering different use cases, show that our approach can produce effective rankings, improving robustness to attacks, while keeping user preference data private within each seller platform.","PeriodicalId":13106,"journal":{"name":"IEEE Transactions on Big Data","volume":"11 1","pages":"303-309"},"PeriodicalIF":7.5,"publicationDate":"2024-11-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142993638","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

Differential Modal Multistage Adaptive Fusion Networks via Knowledge Distillation for RGB-D Mirror Segmentation 基于知识蒸馏的RGB-D镜像分割差分模态多级自适应融合网络

IF 7.5 3区计算机科学

IEEE Transactions on Big Data Pub Date : 2024-11-22 DOI: 10.1109/TBDATA.2024.3505057

Wujie Zhou;Han Zhang;Weiwei Qiu

{"title":"Differential Modal Multistage Adaptive Fusion Networks via Knowledge Distillation for RGB-D Mirror Segmentation","authors":"Wujie Zhou;Han Zhang;Weiwei Qiu","doi":"10.1109/TBDATA.2024.3505057","DOIUrl":"https://doi.org/10.1109/TBDATA.2024.3505057","url":null,"abstract":"Mirrors play a significant role in our daily lives and are ubiquitous. However, deep learning computer vision models find them challenging owing to the negative impact of reflected information on scene understanding. This study addresses two key challenges faced by multimodal models. First, the cross-modal variability of features at different stages is generally overlooked by contemporary backbone networks. Second, good performance has only been achieved at an unacceptable computational expense, owing to the numerous parameters used. To address the first challenge, we propose a differential-mode multistage adaptive fusion network (differential mode refers to images generated by different sensors that are differentiated to complement each other) that incorporates two-step fusion in the coding stage to account for the degrees of difference among the cross-modal features. In the first stage, wherein considerable differences in modal features exist, multi-angle fusion is performed. In the second stage, wherein the differences are smaller, a hierarchical adaptive fusion strategy is employed. Regarding the second challenge, we introduce a companion training framework for mirror segmentation that combines knowledge distillation and contrastive learning. Our proposed scheme achieves state-of-the-art performance on an available mirror segmentation dataset without requiring numerous parameters.","PeriodicalId":13106,"journal":{"name":"IEEE Transactions on Big Data","volume":"11 4","pages":"1959-1969"},"PeriodicalIF":7.5,"publicationDate":"2024-11-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144597982","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