Big Data Research最新文献

Incomplete data classification via positive approximation based rough subspaces ensemble 通过基于正逼近的粗糙子空间集合进行不完整数据分类

IF 3.5 3区计算机科学

Big Data Research Pub Date : 2024-11-14 DOI: 10.1016/j.bdr.2024.100496

Yuanting Yan , Meili Yang , Zhong Zheng , Hao Ge , Yiwen Zhang , Yanping Zhang

{"title":"Incomplete data classification via positive approximation based rough subspaces ensemble","authors":"Yuanting Yan , Meili Yang , Zhong Zheng , Hao Ge , Yiwen Zhang , Yanping Zhang","doi":"10.1016/j.bdr.2024.100496","DOIUrl":"10.1016/j.bdr.2024.100496","url":null,"abstract":"<div><div>Classifying incomplete data using ensemble techniques is a prevalent method for addressing missing values, where multiple classifiers are trained on diverse subsets of features. However, current ensemble-based methods overlook the redundancy within feature subsets, presenting challenges for training robust prediction models, because the redundant features can hinder the learning of the underlying rules in the data. In this paper, we propose a Reduct-Missing Pattern Fusion (RMPF) method to address the aforementioned limitation. It leverages both the advantages of rough set theory and the effectiveness of missing patterns in classifying incomplete data. RMPF employs a heuristic algorithm to generate a set of positive approximation-based attribute reducts. Subsequently, it integrates the missing patterns with these reducts through a fusion strategy to minimize data redundancy. Finally, the optimized subsets are utilized to train a group of base classifiers, and a selective prediction procedure is applied to produce the ensembled prediction results. Experimental results show that our method is superior to the compared state-of-the-art methods in both performance and robustness. Especially, our method obtains significant superiority in the scenarios of data with high missing rates.</div></div>","PeriodicalId":56017,"journal":{"name":"Big Data Research","volume":"38 ","pages":"Article 100496"},"PeriodicalIF":3.5,"publicationDate":"2024-11-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142650891","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

Joint embedding in hierarchical distance and semantic representation learning for link prediction 分层距离和语义表征学习中的联合嵌入，用于链接预测

IF 3.5 3区计算机科学

Big Data Research Pub Date : 2024-11-13 DOI: 10.1016/j.bdr.2024.100495

Jin Liu, Jianye Chen, Chongfeng Fan, Fengyu Zhou

{"title":"Joint embedding in hierarchical distance and semantic representation learning for link prediction","authors":"Jin Liu, Jianye Chen, Chongfeng Fan, Fengyu Zhou","doi":"10.1016/j.bdr.2024.100495","DOIUrl":"10.1016/j.bdr.2024.100495","url":null,"abstract":"<div><div>The link prediction task aims to predict missing entities or relations in the knowledge graph and is essential for the downstream application. Existing well-known models deal with this task by mainly focusing on representing knowledge graph triplets in the distance space or semantic space. However, they can not fully capture the information of head and tail entities, nor even make good use of hierarchical level information. Thus, in this paper, we propose a novel knowledge graph embedding model for the link prediction task, namely, HIE, which models each triplet (<em>h</em>, <em>r</em>, <em>t</em>) into distance measurement space and semantic measurement space, simultaneously. Moreover, HIE is introduced into hierarchical-aware space to leverage rich hierarchical information of entities and relations for better representation learning. Specifically, we apply distance transformation operation on the head entity in distance space to obtain the tail entity instead of translation-based or rotation-based approaches. Experimental results of HIE on four real-world datasets show that HIE outperforms several existing state-of-the-art knowledge graph embedding methods on the link prediction task and deals with complex relations accurately.</div></div>","PeriodicalId":56017,"journal":{"name":"Big Data Research","volume":"38 ","pages":"Article 100495"},"PeriodicalIF":3.5,"publicationDate":"2024-11-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142650890","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

Deep semantics-preserving cross-modal hashing 深度语义保全跨模态散列

IF 3.5 3区计算机科学

Big Data Research Pub Date : 2024-11-07 DOI: 10.1016/j.bdr.2024.100494

Zhihui Lai , Xiaomei Fang , Heng Kong

{"title":"Deep semantics-preserving cross-modal hashing","authors":"Zhihui Lai , Xiaomei Fang , Heng Kong","doi":"10.1016/j.bdr.2024.100494","DOIUrl":"10.1016/j.bdr.2024.100494","url":null,"abstract":"<div><div>Cross-modal hashing has been paid widespread attention in recent years due to its outstanding performance in cross-modal data retrieval. Cross-modal hashing can be decomposed into two steps, i.e., the feature learning and the binarization. However, most existing cross-modal hash methods do not take the supervisory information of the data into consideration during binary quantization, and thus often fail to adequately preserve semantic information. To solve these problems, this paper proposes a novel deep cross-modal hashing method called deep semantics-preserving cross-modal hashing (DSCMH), which makes full use of intra and inter-modal semantic information to improve the model's performance. Moreover, by designing a label network for semantic alignment during the binarization process, DSCMH's performance can be further improved. In order to verify the performance of the proposed method, extensive experiments were conducted on four big datasets. The results show that the proposed method is better than most of the existing cross-modal hashing methods. In addition, the ablation experiment shows that the proposed new regularized terms all have positive effects on the model's performances in cross-modal retrieval. The code of this paper can be downloaded from <span><span>http://www.scholat.com/laizhihui</span><svg><path></path></svg></span>.</div></div>","PeriodicalId":56017,"journal":{"name":"Big Data Research","volume":"38 ","pages":"Article 100494"},"PeriodicalIF":3.5,"publicationDate":"2024-11-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142650889","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

Research on the characteristics of information propagation dynamic on the weighted multiplex Weibo networks 加权复用微博网络信息传播动态特征研究

IF 3.5 3区计算机科学

Big Data Research Pub Date : 2024-09-27 DOI: 10.1016/j.bdr.2024.100493

Yinuo Qian, Fuzhong Nian

{"title":"Research on the characteristics of information propagation dynamic on the weighted multiplex Weibo networks","authors":"Yinuo Qian, Fuzhong Nian","doi":"10.1016/j.bdr.2024.100493","DOIUrl":"10.1016/j.bdr.2024.100493","url":null,"abstract":"<div><div>In order to simulate the forwarding situation of different categories of Weibo and discover interesting propagation phenomena in different layers of Weibo networks, this paper proposes the retweeting weighted multiplex networks and propagation model coupled with multi-class Weibo. Firstly, the weighted multiplex social network is constructed through the processing of Weibo network data. Secondly, a new information propagation model is established by using the weight and interlayer information of the Weibo multiplex network combined with the coupling factors in the propagation. Finally, the information propagation simulated by the propagation model is compared with the real data, so as to summarize different information propagation phenomena in multiplex social multiplex network. At the same time, by comparing the structure of the forwarding weighted multiplex network constructed by the short time data and the long time data, we find the self-similarity of the forwarding weighted multiplex network, which proves the generalization of the experiment. Through the above research, the mystery of the Weibo social network has been deeply explored, and a new perspective has been opened up for the exploration of social media information propagation.</div></div>","PeriodicalId":56017,"journal":{"name":"Big Data Research","volume":"38 ","pages":"Article 100493"},"PeriodicalIF":3.5,"publicationDate":"2024-09-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142417738","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

Leveraging social computing for epidemic surveillance: A case study 利用社交计算进行流行病监测：案例研究

IF 3.5 3区计算机科学

Big Data Research Pub Date : 2024-08-08 DOI: 10.1016/j.bdr.2024.100483

Bilal Tahir , Muhammad Amir Mehmood

{"title":"Leveraging social computing for epidemic surveillance: A case study","authors":"Bilal Tahir , Muhammad Amir Mehmood","doi":"10.1016/j.bdr.2024.100483","DOIUrl":"10.1016/j.bdr.2024.100483","url":null,"abstract":"<div><p>Social media platforms have become a popular source of information for real-time monitoring of events and user behavior. In particular, Twitter provides invaluable information related to diseases and public health to build real-time disease surveillance systems. Effective use of such social media platforms for public health surveillance requires data-driven AI models which are hindered by the difficult, expensive, and time-consuming task of collecting high-quality and large-scale datasets. In this paper, we build and analyze the Epidemic TweetBank (EpiBank) dataset containing 271 million English tweets related to six epidemic-prone diseases COVID19, Flu, Hepatitis, Dengue, Malaria, and HIV/AIDs. For this purpose, we develop a tool of ESS-T (Epidemic Surveillance Study via Twitter) which collects tweets according to provided input parameters and keywords. Also, our tool assigns location to tweets with 95% accuracy value and performs analysis of collected tweets focusing on temporal distribution, spatial patterns, users, entities, sentiment, and misinformation. Leveraging ESS-T, we build two geo-tagged datasets of EpiBank-global and EpiBank-Pak containing 86 million tweets from 190 countries and 2.6 million tweets from Pakistan, respectively. Our spatial analysis of EpiBank-global for COVID19, Malaria, and Dengue indicates that our framework correctly identifies high-risk epidemic-prone countries according to World Health Organization (WHO) statistics.</p></div>","PeriodicalId":56017,"journal":{"name":"Big Data Research","volume":"38 ","pages":"Article 100483"},"PeriodicalIF":3.5,"publicationDate":"2024-08-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141978839","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

Anomaly detection based on system text logs of virtual network functions 基于虚拟网络功能系统文本日志的异常检测

IF 3.5 3区计算机科学

Big Data Research Pub Date : 2024-08-02 DOI: 10.1016/j.bdr.2024.100485

Daniela N. Rim , DongNyeong Heo , Chungjun Lee , Sukhyun Nam , Jae-Hyoung Yoo , James Won-Ki Hong , Heeyoul Choi

引用次数: 0

A dual algorithmic approach to deal with multiclass imbalanced classification problems 处理多类不平衡分类问题的双重算法方法

IF 3.5 3区计算机科学

Big Data Research Pub Date : 2024-08-02 DOI: 10.1016/j.bdr.2024.100484

S. Sridhar , S. Anusuya

{"title":"A dual algorithmic approach to deal with multiclass imbalanced classification problems","authors":"S. Sridhar , S. Anusuya","doi":"10.1016/j.bdr.2024.100484","DOIUrl":"10.1016/j.bdr.2024.100484","url":null,"abstract":"<div><p>Many real-world applications involve multiclass classification problems, and often the data across classes is not evenly distributed. Due to this disproportion, supervised learning models tend to classify instances towards the class with the maximum number of instances, which is a severe issue that needs to be addressed. In multiclass imbalanced data classification, machine learning researchers try to reduce the learning model's bias towards the class with a high sample count. Researchers attempt to reduce this unfairness by either balancing the data before the classifier learns it, modifying the classifier's learning phase to pay more attention to the class with a minimum number of instances, or a combination of both. The existing algorithmic approaches find it difficult to understand the clear boundary between the samples of different classes due to unfair class distribution and overlapping issues. As a result, the minority class recognition rate is poor. A new algorithmic approach is proposed that uses dual decision trees. One is used to create an induced dataset using a PCA based grouping approach and by assigning weights to the data samples followed by another decision tree to learn and predict from the induced dataset. The distinct feature of this algorithmic approach is that it recognizes the data instances without altering their underlying data distribution and is applicable for all categories of multiclass imbalanced datasets. Five multiclass imbalanced datasets from UCI were used to classify the data using our proposed algorithm, and the results revealed that the duo-decision tree approach pays better attention to both the minor and major class samples.</p></div>","PeriodicalId":56017,"journal":{"name":"Big Data Research","volume":"38 ","pages":"Article 100484"},"PeriodicalIF":3.5,"publicationDate":"2024-08-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141985718","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

Multi-step trend aware graph neural network for traffic flow forecasting 用于交通流量预测的多步骤趋势感知图神经网络

IF 3.5 3区计算机科学

Big Data Research Pub Date : 2024-07-29 DOI: 10.1016/j.bdr.2024.100482

Lipeng Zhao , Bing Guo , Cheng Dai , Yan Shen , Fei Chen , Mingjie Zhao , Yuchuan Hu

{"title":"Multi-step trend aware graph neural network for traffic flow forecasting","authors":"Lipeng Zhao , Bing Guo , Cheng Dai , Yan Shen , Fei Chen , Mingjie Zhao , Yuchuan Hu","doi":"10.1016/j.bdr.2024.100482","DOIUrl":"10.1016/j.bdr.2024.100482","url":null,"abstract":"<div><p>Traffic flow prediction plays an important role in smart cities. Although many neural network models already existed that can predict traffic flow, in the face of complex spatio-temporal data, these models still have some shortcomings. Firstly, they although take into account local spatio-temporal relations, ignore global information, leading to inability to capture global trend. Secondly, most models although construct spatio-temporal graphs for convolution, ignore the dynamic characteristics of spatio-temporal graphs, leading to the inability to capture local fluctuation. Finally, the current popular models need to take a lot of training time to obtain better prediction results, resulting in higher computing cost. To this end, we propose a new model: <strong>M</strong>ulti-<strong>S</strong>tep <strong>T</strong>rend <strong>A</strong>ware <strong>G</strong>raph <strong>N</strong>eural <strong>N</strong>etwork (MSTAGNN), which considers the influence of global spatio-temporal information and captures the dynamic characteristics of spatio-temporal graph. It can not only accurately capture local fluctuation, but also extract global trend and dramatically reduce computing cost. The experimental results showed that our proposed model achieved optimal results compared to baseline. Among them, mean absolute error (MAE) was reduced by 6.25% and the total training time was reduced by 79% on the PEMSD8 dataset. The source codes are available at: <span><span>https://github.com/Vitalitypi/MSTAGNN</span><svg><path></path></svg></span>.</p></div>","PeriodicalId":56017,"journal":{"name":"Big Data Research","volume":"38 ","pages":"Article 100482"},"PeriodicalIF":3.5,"publicationDate":"2024-07-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141940505","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

Unmasking hate in the pandemic: A cross-platform study of the COVID-19 infodemic 在大流行中揭开仇恨的面纱：COVID-19 信息大流行的跨平台研究

IF 3.5 3区计算机科学

Big Data Research Pub Date : 2024-06-25 DOI: 10.1016/j.bdr.2024.100481

Fatima Zahrah , Jason R.C. Nurse , Michael Goldsmith

{"title":"Unmasking hate in the pandemic: A cross-platform study of the COVID-19 infodemic","authors":"Fatima Zahrah , Jason R.C. Nurse , Michael Goldsmith","doi":"10.1016/j.bdr.2024.100481","DOIUrl":"https://doi.org/10.1016/j.bdr.2024.100481","url":null,"abstract":"<div><p>The past few decades have established how digital technologies and platforms have provided an effective medium for spreading hateful content, which has been linked to several catastrophic consequences. Recent academic studies have also highlighted how online hate is a phenomenon that strategically makes use of multiple online platforms. In this article, we seek to advance the current research landscape by harnessing a cross-platform approach to computationally analyse content relating to the 2020 COVID-19 pandemic. More specifically, we analyse content on hate-specific environments from Twitter, Reddit, 4chan and Stormfront. Our findings show how content and posting activity can change across platforms, and how the psychological components of online content can differ depending on the platform being used. Through this, we provide unique insight into the cross-platform behaviours of online hate. We further define several avenues for future research within this field so as to gain a more comprehensive understanding of the global hate ecosystem.</p></div>","PeriodicalId":56017,"journal":{"name":"Big Data Research","volume":"37 ","pages":"Article 100481"},"PeriodicalIF":3.5,"publicationDate":"2024-06-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2214579624000558/pdfft?md5=a8e2330701051448866927c6cb877d10&pid=1-s2.0-S2214579624000558-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141480176","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

Two-dimensional data partitioning for non-negative matrix tri-factorization 非负矩阵三因子化的二维数据分区

IF 3.5 3区计算机科学

Big Data Research Pub Date : 2024-06-19 DOI: 10.1016/j.bdr.2024.100473

Jiaxing Yan , Hai Liu , Zhiqi Lei , Yanghui Rao , Guan Liu , Haoran Xie , Xiaohui Tao , Fu Lee Wang

{"title":"Two-dimensional data partitioning for non-negative matrix tri-factorization","authors":"Jiaxing Yan , Hai Liu , Zhiqi Lei , Yanghui Rao , Guan Liu , Haoran Xie , Xiaohui Tao , Fu Lee Wang","doi":"10.1016/j.bdr.2024.100473","DOIUrl":"https://doi.org/10.1016/j.bdr.2024.100473","url":null,"abstract":"<div><p>As a two-sided clustering and dimensionality reduction paradigm, Non-negative Matrix Tri-Factorization (NMTF) has attracted much attention in machine learning and data mining researchers due to its excellent performance and reliable theoretical support. Unlike Non-negative Matrix Factorization (NMF) methods applicable to one-sided clustering only, NMTF introduces an additional factor matrix and uses the inherent duality of data to realize the mutual promotion of sample clustering and feature clustering, thus showing great advantages in many scenarios (e.g., text co-clustering). However, the existing methods for solving NMTF usually involve intensive matrix multiplication, which is characterized by high time and space complexities, that is, there are limitations of slow convergence of the multiplicative update rules and high memory overhead. In order to solve the above problems, this paper develops a distributed parallel algorithm with a 2-dimensional data partition scheme for NMTF (i.e., PNMTF-2D). Experiments on multiple text datasets show that the proposed PNMTF-2D can substantially improve the computational efficiency of NMTF (e.g., the average iteration time is reduced by up to 99.7% on Amazon) while ensuring the effectiveness of convergence and co-clustering.</p></div>","PeriodicalId":56017,"journal":{"name":"Big Data Research","volume":"37 ","pages":"Article 100473"},"PeriodicalIF":3.5,"publicationDate":"2024-06-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141480175","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