Journal of Big Data最新文献

{"title":"Integrating microarray-based spatial transcriptomics and RNA-seq reveals tissue architecture in colorectal cancer","authors":"Zheng Li, Xiaojie Zhang, Chongyuan Sun, Zefeng Li, He Fei, Dongbing Zhao","doi":"10.1186/s40537-024-00992-9","DOIUrl":"https://doi.org/10.1186/s40537-024-00992-9","url":null,"abstract":"<h3 data-test=\"abstract-sub-heading\">Background</h3><p>The tumor microenvironment (TME) provides a region for intricate interactions within or between immune and non-immune cells. We aimed to reveal the tissue architecture and comprehensive landscape of cells within the TME of colorectal cancer (CRC).</p><h3 data-test=\"abstract-sub-heading\">Methods</h3><p>Fresh frozen invasive adenocarcinoma of the large intestine tissue from 10× Genomics Datasets was obtained from BioIVT Asterand. The integration of microarray-based spatial transcriptomics (ST) and RNA sequencing (RNA-seq) was applied to characterize gene expression and cell landscape within the TME of CRC tissue architecture. Multiple R packages and deconvolution algorithms including MCPcounter, XCELL, EPIC, and ESTIMATE methods were performed for further immune distribution analysis.</p><h3 data-test=\"abstract-sub-heading\">Results</h3><p>The subpopulations of immune and non-immune cells within the TME of the CRC tissue architecture were appropriately annotated. According to ST and RNA-seq analyses, a heterogeneous spatial atlas of gene distribution and cell landscape was comprehensively characterized. We distinguished between the cancer and stromal regions of CRC tissues. As expected, epithelial cells were located in the cancerous region, whereas fibroblasts were mainly located in the stroma. In addition, the fibroblasts were further subdivided into two subgroups (F1 and F2) according to the differentially expressed genes (DEGs), which were mainly enriched in pathways including hallmark-oxidative-phosphorylation, hallmark-e2f-targets and hallmark-unfolded-protein-response. Furthermore, the top 5 DEGs, SPP1, CXCL10, APOE, APOC1, and LYZ, were found to be closely related to immunoregulation of the TME, methylation, and survival of CRC patients.</p><h3 data-test=\"abstract-sub-heading\">Conclusions</h3><p>This study characterized the heterogeneous spatial landscape of various cell subtypes within the TME of the tissue architecture. The TME-related roles of fibroblast subsets addressed the potential crosstalk among diverse cells.</p>","PeriodicalId":15158,"journal":{"name":"Journal of Big Data","volume":"26 1","pages":""},"PeriodicalIF":8.1,"publicationDate":"2024-09-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142253793","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Development and evaluation of a deep learning model for automatic segmentation of non-perfusion area in fundus fluorescein angiography","authors":"Wei Feng, Bingjie Wang, Dan Song, Mengda Li, Anming Chen, Jing Wang, Siyong Lin, Yiran Zhao, Bin Wang, Zongyuan Ge, Shuyi Xu, Yuntao Hu","doi":"10.1186/s40537-024-00968-9","DOIUrl":"https://doi.org/10.1186/s40537-024-00968-9","url":null,"abstract":"<p>Diabetic retinopathy (DR) is the most prevalent cause of preventable vision loss worldwide, imposing a significant economic and medical burden on society today, of which early identification is the cornerstones of the management. The diagnosis and severity grading of DR rely on scales based on clinical visualized features, but lack detailed quantitative parameters. Retinal non-perfusion area (NPA) is a pathogenic characteristic of DR that symbolizes retinal hypoxia conditions, and was found to be intimately associated with disease progression, prognosis, and management. However, the practical value of NPA is constrained since it appears on fundus fluorescein angiography (FFA) as distributed, irregularly shaped, darker plaques that are challenging to measure manually. In this study, we propose a deep learning-based method, NPA-Net, for accurate and automatic segmentation of NPAs from FFA images acquired in clinical practice. NPA-Net uses the U-net structure as the basic backbone, which has an encoder-decoder model structure. To enhance the recognition performance of the model for NPA, we adaptively incorporate multi-scale features and contextual information in feature learning and design three modules: Adaptive Encoder Feature Fusion (AEFF) module, Multilayer Deep Supervised Loss, and Atrous Spatial Pyramid Pooling (ASPP) module, which enhance the recognition ability of the model for NPAs of different sizes from different perspectives. We conducted extensive experiments on a clinical dataset with 163 eyes with NPAs manually annotated by ophthalmologists, and NPA-Net achieved better segmentation performance compared to other existing methods with an area under the receiver operating characteristic curve (AUC) of 0.9752, accuracy of 0.9431, sensitivity of 0.8794, specificity of 0.9459, IOU of 0.3876 and Dice of 0.5686. This new automatic segmentation model is useful for identifying NPA in clinical practice, generating quantitative parameters that can be useful for further research as well as guiding DR detection, grading severity, treatment planning, and prognosis.</p>","PeriodicalId":15158,"journal":{"name":"Journal of Big Data","volume":"37 1","pages":""},"PeriodicalIF":8.1,"publicationDate":"2024-09-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142186332","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Leveraging large-scale genetic data to assess the causal impact of COVID-19 on multisystemic diseases","authors":"Xiangyang Zhang, Zhaohui Jiang, Jiayao Ma, Yaru Qi, Yin Li, Yan Zhang, Yihan Liu, Chaochao Wei, Yihong Chen, Ping Liu, Yinghui Peng, Jun Tan, Ying Han, Shan Zeng, Changjing Cai, Hong Shen","doi":"10.1186/s40537-024-00997-4","DOIUrl":"https://doi.org/10.1186/s40537-024-00997-4","url":null,"abstract":"<h3 data-test=\"abstract-sub-heading\">Background</h3><p>The long-term impacts of COVID-19 on human health are a major concern, yet comprehensive evaluations of its effects on various health conditions are lacking.</p><h3 data-test=\"abstract-sub-heading\">Methods</h3><p>This study aims to evaluate the role of various diseases in relation to COVID-19 by analyzing genetic data from a large-scale population over 2,000,000 individuals. A bidirectional two-sample Mendelian randomization approach was used, with exposures including COVID-19 susceptibility, hospitalization, and severity, and outcomes encompassing 86 different diseases or traits. A reverse Mendelian randomization analysis was performed to assess the impact of these diseases on COVID-19.</p><h3 data-test=\"abstract-sub-heading\">Results</h3><p>Our analysis identified causal relationships between COVID-19 susceptibility and several conditions, including breast cancer (OR = 1.0073, 95% CI = 1.0032–1.0114, <i>p</i> = 5 × 10 − 4), ER + breast cancer (OR = 0.5252, 95% CI = 0.3589–0.7685, <i>p</i> = 9 × 10 − 4), and heart failure (OR = 1.0026, 95% CI = 1.001–1.0042, <i>p</i> = 0.002). COVID-19 hospitalization was causally linked to heart failure (OR = 1.0017, 95% CI = 1.0006–1.0028, <i>p</i> = 0.002) and Alzheimer’s disease (OR = 1.5092, 95% CI = 1.1942–1.9072, <i>p</i> = 0.0006). COVID-19 severity had causal effects on primary biliary cirrhosis (OR = 2.6333, 95% CI = 1.8274–3.7948, <i>p</i> = 2.059 × 10 − 7), celiac disease (OR = 0.0708, 95% CI = 0.0538–0.0932, <i>p</i> = 9.438 × 10–80), and Alzheimer’s disease (OR = 1.5092, 95% CI = 1.1942–1.9072, <i>p</i> = 0.0006). Reverse MR analysis indicated that rheumatoid arthritis, diabetic nephropathy, multiple sclerosis, and total testosterone (female) influence COVID-19 outcomes. We assessed heterogeneity and horizontal pleiotropy to ensure result reliability and employed the Steiger directionality test to confirm the direction of causality.</p><h3 data-test=\"abstract-sub-heading\">Conclusions</h3><p>This study provides a comprehensive analysis of the causal relationships between COVID-19 and diverse health conditions. Our findings highlight the long-term impacts of COVID-19 on human health, emphasizing the need for continuous monitoring and targeted interventions for affected individuals. Future research should explore these relationships to develop comprehensive healthcare strategies.</p>","PeriodicalId":15158,"journal":{"name":"Journal of Big Data","volume":"1 1","pages":""},"PeriodicalIF":8.1,"publicationDate":"2024-09-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142186331","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Evolutionary computation-based self-supervised learning for image processing: a big data-driven approach to feature extraction and fusion for multispectral object detection","authors":"Xiaoyang Shen, Haibin Li, Achyut Shankar, Wattana Viriyasitavat, Vinay Chamola","doi":"10.1186/s40537-024-00988-5","DOIUrl":"https://doi.org/10.1186/s40537-024-00988-5","url":null,"abstract":"<p>The image object recognition and detection technology are widely used in many scenarios. In recent years, big data has become increasingly abundant, and big data-driven artificial intelligence models have attracted more and more attention. Evolutionary computation has also provided a powerful driving force for the optimization and improvement of deep learning models. In this paper, we propose an image object detection method based on self-supervised and data-driven learning. Differ from other methods, our approach stands out due to its innovative use of multispectral data fusion and evolutionary computation for model optimization. Specifically, our method uniquely combines visible light images and infrared images to detect and identify image targets. Firstly, we utilize a self-supervised learning method and the AutoEncoder model to perform high-dimensional feature extraction on the two types of images. Secondly, we fuse the extracted features from the visible light and infrared images to detect and identify objects. Thirdly, we introduce a model parameter optimization method using evolutionary learning algorithms to enhance model performance. Validation on public datasets shows that our method achieves comparable or superior performance to existing methods.</p>","PeriodicalId":15158,"journal":{"name":"Journal of Big Data","volume":"6 1","pages":""},"PeriodicalIF":8.1,"publicationDate":"2024-09-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142224547","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A model for investment type recommender system based on the potential investors based on investors and experts feedback using ANFIS and MNN","authors":"Asefeh Asemi, Adeleh Asemi, Andrea Ko","doi":"10.1186/s40537-024-00965-y","DOIUrl":"https://doi.org/10.1186/s40537-024-00965-y","url":null,"abstract":"<p>This article presents an investment recommender system based on an Adaptive Neuro-Fuzzy Inference System (ANFIS) and pre-trained weights from a Multimodal Neural Network (MNN). The model is designed to support the investment process for the customers and takes into consideration seven factors to implement the proposed investment system model through the customer or potential investor data set. The system takes input from a web-based questionnaire that collects data on investors' preferences and investment goals. The data is then preprocessed and clustered using ETL tools, JMP, MATLAB, and Python. The ANFIS-based recommender system is designed with three inputs and one output and trained using a hybrid approach over three epochs with 188 data pairs and 18 fuzzy rules. The system's performance is evaluated using metrics such as RMSE, accuracy, precision, recall, and F1-score. The system is also designed to incorporate expert feedback and opinions from investors to customize and improve investment recommendations. The article concludes that the proposed ANFIS-based investment recommender system is effective and accurate in generating investment recommendations that meet investors' preferences and goals.</p><h3 data-test=\"abstract-sub-heading\">Graphical abstract</h3>\u0000","PeriodicalId":15158,"journal":{"name":"Journal of Big Data","volume":"9 1","pages":""},"PeriodicalIF":8.1,"publicationDate":"2024-09-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142186330","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Inhibitory neuron links the causal relationship from air pollution to psychiatric disorders: a large multi-omics analysis","authors":"Xisong Liang, Jie Wen, Chunrun Qu, Nan Zhang, Ziyu Dai, Hao Zhang, Peng Luo, Ming Meng, Zhixiong Liu, Fan Fan, Quan Cheng","doi":"10.1186/s40537-024-00960-3","DOIUrl":"https://doi.org/10.1186/s40537-024-00960-3","url":null,"abstract":"<p>Psychiatric disorders are severe health challenges that exert a heavy public burden. Air pollution has been widely reported as related to psychiatric disorder risk, but their casual association and pathological mechanism remained unclear. Herein, we systematically investigated the large genome-wide association studies (6 cohorts with 1,357,645 samples), single-cell RNA (26 samples with 157,488 cells), and bulk-RNAseq (1595 samples) datasets to reveal the genetic causality and biological link between four air pollutants and nine psychiatric disorders. As a result, we identified ten positive genetic correlations between air pollution and psychiatric disorders. Besides, PM2.5 and NO₂ presented significant causal effects on schizophrenia risk which was robust with adjustment of potential confounders. Besides, transcriptome-wide association studies identified the shared genes between PM2.5/NO2 and schizophrenia. We then discovered a schizophrenia-derived inhibitory neuron subtype with highly expressed shared genes and abnormal synaptic and metabolic pathways by scRNA analyses and confirmed their abnormal level and correlations with the shared genes in schizophrenia patients in a large RNA-seq cohort. Comprehensively, we discovered robust genetic causality between PM2.5, NO₂, and schizophrenia and identified an abnormal inhibitory neuron subtype that links schizophrenia pathology and PM2.5/NO2 exposure. These discoveries highlight the schizophrenia risk under air pollutants exposure and provide novel mechanical insights into schizophrenia pathology, contributing to pollutant-related schizophrenia risk control and therapeutic strategies development.</p><h3 data-test=\"abstract-sub-heading\">Graphical Abstract</h3>","PeriodicalId":15158,"journal":{"name":"Journal of Big Data","volume":"58 1","pages":""},"PeriodicalIF":8.1,"publicationDate":"2024-09-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142186333","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Modeling the impact of BDA-AI on sustainable innovation ambidexterity and environmental performance","authors":"Chin-Tsu Chen, Asif Khan, Shih-Chih Chen","doi":"10.1186/s40537-024-00995-6","DOIUrl":"https://doi.org/10.1186/s40537-024-00995-6","url":null,"abstract":"<p>Data has evolved into one of the principal resources for contemporary businesses. Moreover, corporations have undergone digitalization; consequently, their supply chains generate substantial amounts of data. The theoretical framework of this investigation was built on novel concepts like big data analytics—artificial intelligence (BDA-AI) and supply chain ambidexterity’s (SCA) direct impacts on sustainable supply chain management (SSCM) and indirect impacts on sustainable innovation ambidexterity (SIA) and environmental performance (EP). This study selected employees of manufacturing industries as respondents for environmental performance, sustainable supply chain management, big data analytics, artificial intelligence, and supply chain ambidexterity. The results from this study show that BDA-AI and SCA significantly affect SSCM. SSCM has significant associations with SIA and EP. Finally, SIA has a significant impact on EP. According to the results indicating the indirect impacts, BDA-AI has significant indirect relationships with SIA and EP by having SSCM as the mediating variable. Furthermore, SCA has significant indirect associations with SIA and EP, with SSCM as the mediating variable. Additionally, both BDA-AI and SCA have significant indirect associations with EP, while SIA and SSCM are mediating variables. Finally, SSCM has an indirect association with EP while having SIA as a mediating variable. The findings of this paper provide several theoretical contributions to the research in sustainability and big data analytics artificial intelligence field. Furthermore, based on the suggested framework, this study offers a number of practical implications for decision-makers to improve significantly in the supply chain and BDA-AI. For instance, this paper provides significant insight for logistics and supply chain managers, supporting them in implementing BDA-AI solutions to help SSCM and enhance EP.</p>","PeriodicalId":15158,"journal":{"name":"Journal of Big Data","volume":"13 1","pages":""},"PeriodicalIF":8.1,"publicationDate":"2024-09-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142186334","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Enhancing oil palm segmentation model with GAN-based augmentation","authors":"Qi Bin Kwong, Yee Thung Kon, Wan Rusydiah W. Rusik, Mohd Nor Azizi Shabudin, Shahirah Shazana A. Rahman, Harikrishna Kulaveerasingam, David Ross Appleton","doi":"10.1186/s40537-024-00990-x","DOIUrl":"https://doi.org/10.1186/s40537-024-00990-x","url":null,"abstract":"<p>In digital agriculture, accurate crop detection is fundamental to developing automated systems for efficient plantation management. For oil palm, the main challenge lies in developing robust models that perform well in different environmental conditions. This study addresses the feasibility of using GAN augmentation methods to improve palm detection models. For this purpose, drone images of young palms (&lt; 5 year-old) from eight different estates were collected, annotated, and used to build a baseline detection model based on DETR. StyleGAN2 was trained on the extracted palms and then used to generate a series of synthetic palms, which were then inserted into tiles representing different environments. CycleGAN networks were trained for bidirectional translation between synthetic and real tiles, subsequently utilized to augment the authenticity of synthetic tiles. Both synthetic and real tiles were used to train the GAN-based detection model. The baseline model achieved precision and recall values of 95.8% and 97.2%. The GAN-based model achieved comparable result, with precision and recall values of 98.5% and 98.6%. In the challenge dataset 1 consisting older palms (&gt; 5 year-old), both models also achieved similar accuracies, with baseline model achieving precision and recall of 93.1% and 99.4%, and GAN-based model achieving 95.7% and 99.4%. As for the challenge dataset 2 consisting of storm affected palms, the baseline model achieved precision of 100% but recall was only 13%. The GAN-based model achieved a significantly better result, with a precision and recall values of 98.7% and 95.3%. This result demonstrates that images generated by GANs have the potential to enhance the accuracies of palm detection models.</p>","PeriodicalId":15158,"journal":{"name":"Journal of Big Data","volume":"25 1","pages":""},"PeriodicalIF":8.1,"publicationDate":"2024-09-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142186336","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"AI sees beyond humans: automated diagnosis of myopia based on peripheral refraction map using interpretable deep learning","authors":"Yong Tang, Zhenghua Lin, Linjing Zhou, Weijia Wang, Longbo Wen, Yongli Zhou, Zongyuan Ge, Zhao Chen, Weiwei Dai, Zhikuan Yang, He Tang, Weizhong Lan","doi":"10.1186/s40537-024-00989-4","DOIUrl":"https://doi.org/10.1186/s40537-024-00989-4","url":null,"abstract":"<p>The question of whether artificial intelligence (AI) can surpass human capabilities is crucial in the application of AI in clinical medicine. To explore this, an interpretable deep learning (DL) model was developed to assess myopia status using retinal refraction maps obtained with a novel peripheral refractor. The DL model demonstrated promising performance, achieving an AUC of 0.9074 (95% CI 0.83–0.97), an accuracy of 0.8140 (95% CI 0.70–0.93), a sensitivity of 0.7500 (95% CI 0.51–0.90), and a specificity of 0.8519 (95% CI 0.68–0.94). Grad-CAM analysis provided interpretable visualization of the attention of DL model and revealed that the DL model utilized information from the central retina, similar to human readers. Additionally, the model considered information from vertical regions across the central retina, which human readers had overlooked. This finding suggests that AI can indeed surpass human capabilities, bolstering our confidence in the use of AI in clinical practice, especially in new scenarios where prior human knowledge is limited.</p>","PeriodicalId":15158,"journal":{"name":"Journal of Big Data","volume":"23 1","pages":""},"PeriodicalIF":8.1,"publicationDate":"2024-09-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142186335","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Efficient microservices offloading for cost optimization in diverse MEC cloud networks","authors":"Abdul Rasheed Mahesar, Xiaoping Li, Dileep Kumar Sajnani","doi":"10.1186/s40537-024-00975-w","DOIUrl":"https://doi.org/10.1186/s40537-024-00975-w","url":null,"abstract":"<p>In recent years, mobile applications have proliferated across domains such as E-banking, Augmented Reality, E-Transportation, and E-Healthcare. These applications are often built using microservices, an architectural style where the application is composed of independently deployable services focusing on specific functionalities. Mobile devices cannot process these microservices locally, so traditionally, cloud-based frameworks using cost-efficient Virtual Machines (VMs) and edge servers have been used to offload these tasks. However, cloud frameworks suffer from extended boot times and high transmission overhead, while edge servers have limited computational resources. To overcome these challenges, this study introduces a Microservices Container-Based Mobile Edge Cloud Computing (MCBMEC) environment and proposes an innovative framework, Optimization Task Scheduling and Computational Offloading with Cost Awareness (OTSCOCA). This framework addresses Resource Matching, Task Sequencing, and Task Scheduling to enhance server utilization, reduce service latency, and improve service bootup times. Empirical results validate the efficacy of MCBMEC and OTSCOCA, demonstrating significant improvements in server efficiency, reduced service latency, faster service bootup times, and notable cost savings. These outcomes underscore the pivotal role of these methodologies in advancing mobile edge computing applications amidst the challenges of edge server limitations and traditional cloud-based approaches.</p>","PeriodicalId":15158,"journal":{"name":"Journal of Big Data","volume":"1 1","pages":""},"PeriodicalIF":8.1,"publicationDate":"2024-09-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142186337","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}