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  • Open Access

    ARTICLE

    Research on Tensor Multi-Clustering Distributed Incremental Updating Method for Big Data

    Hongjun Zhang1,2, Zeyu Zhang3, Yilong Ruan4, Hao Ye5,6, Peng Li1,*, Desheng Shi1

    CMC-Computers, Materials & Continua, Vol.81, No.1, pp. 1409-1432, 2024, DOI:10.32604/cmc.2024.055406 - 15 October 2024

    Abstract The scale and complexity of big data are growing continuously, posing severe challenges to traditional data processing methods, especially in the field of clustering analysis. To address this issue, this paper introduces a new method named Big Data Tensor Multi-Cluster Distributed Incremental Update (BDTMCDIncreUpdate), which combines distributed computing, storage technology, and incremental update techniques to provide an efficient and effective means for clustering analysis. Firstly, the original dataset is divided into multiple sub-blocks, and distributed computing resources are utilized to process the sub-blocks in parallel, enhancing efficiency. Then, initial clustering is performed on each sub-block… More >

  • Open Access

    ARTICLE

    Data-Driven Decision-Making for Bank Target Marketing Using Supervised Learning Classifiers on Imbalanced Big Data

    Fahim Nasir1, Abdulghani Ali Ahmed1,*, Mehmet Sabir Kiraz1, Iryna Yevseyeva1, Mubarak Saif2

    CMC-Computers, Materials & Continua, Vol.81, No.1, pp. 1703-1728, 2024, DOI:10.32604/cmc.2024.055192 - 15 October 2024

    Abstract Integrating machine learning and data mining is crucial for processing big data and extracting valuable insights to enhance decision-making. However, imbalanced target variables within big data present technical challenges that hinder the performance of supervised learning classifiers on key evaluation metrics, limiting their overall effectiveness. This study presents a comprehensive review of both common and recently developed Supervised Learning Classifiers (SLCs) and evaluates their performance in data-driven decision-making. The evaluation uses various metrics, with a particular focus on the Harmonic Mean Score (F-1 score) on an imbalanced real-world bank target marketing dataset. The findings indicate… More >

  • Open Access

    ARTICLE

    Research on computer network data security storage technology in the era of big data

    Liying Zhang1, Xin Gu1, Qiang Zhao2

    Revista Internacional de Métodos Numéricos para Cálculo y Diseño en Ingeniería, Vol.40, No.2, pp. 1-7, 2024, DOI:10.23967/j.rimni.2024.05.001 - 17 May 2024

    Abstract In the burgeoning epoch of big data, the imperative for secure computer network data storage is confronted with formidable challenges, including the perils of data breaches and a paucity of robust security measures. An enhanced storage paradigm, predicated upon a refined Hash algorithm—termed H-AONT—is herein delineated. This methodology augments data storage security through the formulation of an apposite system model, the amalgamation of the merits inherent in conventional encryption algorithms, and the deployment of the H-AONT dual encryption algorithm in data processing. Empirical evidence substantiates that, vis-à-vis alternative approaches, the proposed method significantly bolsters data More >

  • Open Access

    ARTICLE

    Balancing the load and scheduling the tasks using zebra optimizer in IoT based cloud computing for big-data applications

    V. Vijayaraj1, M. Balamurugan1, Monisha Oberoi2

    Revista Internacional de Métodos Numéricos para Cálculo y Diseño en Ingeniería, Vol.40, No.2, pp. 1-11, 2024, DOI:10.23967/j.rimni.2024.05.009 - 31 May 2024

    Abstract Task scheduling is one of the major problems with Internet of Things (IoT) cloud computing. The need for cloud storage has skyrocketed due to recent advancements in IoT-based technology. Sophisticated planning approaches are needed to load the IoT services onto cloud resources professionally while meeting application necessities. This is significant because, in order to optimise resource utilisation and reduce waiting times, several procedures must be properly configured on various virtual machines. Because of the diverse nature of IoT, scheduling various IoT application activities in a cloud-based computing architecture can be challenging. Fog cloud computing is… More >

  • Open Access

    ARTICLE

    A Quarterly High RFM Mining Algorithm for Big Data Management

    Cuiwei Peng1, Jiahui Chen2,*, Shicheng Wan3, Guotao Xu4

    CMC-Computers, Materials & Continua, Vol.80, No.3, pp. 4341-4360, 2024, DOI:10.32604/cmc.2024.054109 - 12 September 2024

    Abstract In today’s highly competitive retail industry, offline stores face increasing pressure on profitability. They hope to improve their ability in shelf management with the help of big data technology. For this, on-shelf availability is an essential indicator of shelf data management and closely relates to customer purchase behavior. RFM (recency, frequency, and monetary) pattern mining is a powerful tool to evaluate the value of customer behavior. However, the existing RFM pattern mining algorithms do not consider the quarterly nature of goods, resulting in unreasonable shelf availability and difficulty in profit-making. To solve this problem, we… More >

  • Open Access

    ARTICLE

    Analysis and Modeling of Mobile Phone Activity Data Using Interactive Cyber-Physical Social System

    Farhan Amin, Gyu Sang Choi*

    CMC-Computers, Materials & Continua, Vol.80, No.3, pp. 3507-3521, 2024, DOI:10.32604/cmc.2024.053183 - 12 September 2024

    Abstract Mobile networks possess significant information and thus are considered a gold mine for the researcher’s community. The call detail records (CDR) of a mobile network are used to identify the network’s efficacy and the mobile user’s behavior. It is evident from the recent literature that cyber-physical systems (CPS) were used in the analytics and modeling of telecom data. In addition, CPS is used to provide valuable services in smart cities. In general, a typical telecom company has millions of subscribers and thus generates massive amounts of data. From this aspect, data storage, analysis, and processing… More >

  • Open Access

    ARTICLE

    Data-Oriented Operating System for Big Data and Cloud

    Selwyn Darryl Kessler, Kok-Why Ng*, Su-Cheng Haw*

    Intelligent Automation & Soft Computing, Vol.39, No.4, pp. 633-647, 2024, DOI:10.32604/iasc.2024.054154 - 06 September 2024

    Abstract Operating System (OS) is a critical piece of software that manages a computer’s hardware and resources, acting as the intermediary between the computer and the user. The existing OS is not designed for Big Data and Cloud Computing, resulting in data processing and management inefficiency. This paper proposes a simplified and improved kernel on an x86 system designed for Big Data and Cloud Computing purposes. The proposed algorithm utilizes the performance benefits from the improved Input/Output (I/O) performance. The performance engineering runs the data-oriented design on traditional data management to improve data processing speed by… More >

  • Open Access

    ARTICLE

    Big Data Access Control Mechanism Based on Two-Layer Permission Decision Structure

    Aodi Liu, Na Wang*, Xuehui Du, Dibin Shan, Xiangyu Wu, Wenjuan Wang

    CMC-Computers, Materials & Continua, Vol.79, No.1, pp. 1705-1726, 2024, DOI:10.32604/cmc.2024.049011 - 25 April 2024

    Abstract Big data resources are characterized by large scale, wide sources, and strong dynamics. Existing access control mechanisms based on manual policy formulation by security experts suffer from drawbacks such as low policy management efficiency and difficulty in accurately describing the access control policy. To overcome these problems, this paper proposes a big data access control mechanism based on a two-layer permission decision structure. This mechanism extends the attribute-based access control (ABAC) model. Business attributes are introduced in the ABAC model as business constraints between entities. The proposed mechanism implements a two-layer permission decision structure composed… More >

  • Open Access

    ARTICLE

    An Innovative K-Anonymity Privacy-Preserving Algorithm to Improve Data Availability in the Context of Big Data

    Linlin Yuan1,2, Tiantian Zhang1,3, Yuling Chen1,*, Yuxiang Yang1, Huang Li1

    CMC-Computers, Materials & Continua, Vol.79, No.1, pp. 1561-1579, 2024, DOI:10.32604/cmc.2023.046907 - 25 April 2024

    Abstract The development of technologies such as big data and blockchain has brought convenience to life, but at the same time, privacy and security issues are becoming more and more prominent. The K-anonymity algorithm is an effective and low computational complexity privacy-preserving algorithm that can safeguard users’ privacy by anonymizing big data. However, the algorithm currently suffers from the problem of focusing only on improving user privacy while ignoring data availability. In addition, ignoring the impact of quasi-identified attributes on sensitive attributes causes the usability of the processed data on statistical analysis to be reduced. Based… More >

  • Open Access

    ARTICLE

    Privacy-Preserving Federated Deep Learning Diagnostic Method for Multi-Stage Diseases

    Jinbo Yang1, Hai Huang1, Lailai Yin2, Jiaxing Qu3, Wanjuan Xie4,*

    CMES-Computer Modeling in Engineering & Sciences, Vol.139, No.3, pp. 3085-3099, 2024, DOI:10.32604/cmes.2023.045417 - 11 March 2024

    Abstract Diagnosing multi-stage diseases typically requires doctors to consider multiple data sources, including clinical symptoms, physical signs, biochemical test results, imaging findings, pathological examination data, and even genetic data. When applying machine learning modeling to predict and diagnose multi-stage diseases, several challenges need to be addressed. Firstly, the model needs to handle multimodal data, as the data used by doctors for diagnosis includes image data, natural language data, and structured data. Secondly, privacy of patients’ data needs to be protected, as these data contain the most sensitive and private information. Lastly, considering the practicality of the… More >

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