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  • Open Access

    ARTICLE

    Blockchain Based Consensus Algorithm and Trustworthy Evaluation of Authenticated Subgraph Queries

    G. Sharmila1,*, M. K. Kavitha Devi2

    Computer Systems Science and Engineering, Vol.45, No.2, pp. 1743-1758, 2023, DOI:10.32604/csse.2023.032127

    Abstract Over the past era, subgraph mining from a large collection of graph database is a crucial problem. In addition, scalability is another big problem due to insufficient storage. There are several security challenges associated with subgraph mining in today’s on-demand system. To address this downside, our proposed work introduces a Blockchain-based Consensus algorithm for Authenticated query search in the Large-Scale Dynamic Graphs (BCCA-LSDG). The two-fold process is handled in the proposed BCCA-LSDG: graph indexing and authenticated query search (query processing). A blockchain-based reputation system is meant to maintain the trust blockchain and cloud server of the proposed architecture. To resolve… More >

  • Open Access

    ARTICLE

    Heterogeneous Ensemble Feature Selection Model (HEFSM) for Big Data Analytics

    M. Priyadharsini1,*, K. Karuppasamy2

    Computer Systems Science and Engineering, Vol.45, No.2, pp. 2187-2205, 2023, DOI:10.32604/csse.2023.031115

    Abstract Big Data applications face different types of complexities in classifications. Cleaning and purifying data by eliminating irrelevant or redundant data for big data applications becomes a complex operation while attempting to maintain discriminative features in processed data. The existing scheme has many disadvantages including continuity in training, more samples and training time in feature selections and increased classification execution times. Recently ensemble methods have made a mark in classification tasks as combine multiple results into a single representation. When comparing to a single model, this technique offers for improved prediction. Ensemble based feature selections parallel multiple expert’s judgments on a… More >

  • Open Access

    ARTICLE

    Application of Big Data Information Platform in Medical Equipment

    Meiyu Pang*, Cheng Liu

    Journal on Big Data, Vol.4, No.2, pp. 113-123, 2022, DOI:10.32604/jbd.2022.028791

    Abstract The application of big data in the medical device industry mainly refers to the analysis and processing of various medical devices, so as to provide patients with better treatment and rehabilitation services. At present, our country already has a relatively mature and reliable large database system. This article studies the application of medical equipment in the big data information platform. The main methods used in this article are survey method, case analysis method, and interview method. The big data information platform and medical devices are studied from different aspects. The survey results show that 41% of people completely agree with… More >

  • Open Access

    ARTICLE

    A Survey of Machine Learning for Big Data Processing

    Reem Almutiri*, Sarah Alhabeeb, Sarah Alhumud, Rehan Ullah Khan

    Journal on Big Data, Vol.4, No.2, pp. 97-111, 2022, DOI:10.32604/jbd.2022.028363

    Abstract Today’s world is a data-driven one, with data being produced in vast amounts as a result of the rapid growth of technology that permeates every aspect of our lives. New data processing techniques must be developed and refined over time to gain meaningful insights from this vast continuous volume of produced data in various forms. Machine learning technologies provide promising solutions and potential methods for processing large quantities of data and gaining value from it. This study conducts a literature review on the application of machine learning techniques in big data processing. It provides a general overview of machine learning… More >

  • Open Access

    ARTICLE

    Big Data Testing Techniques: Taxonomy, Challenges and Future Trends

    Iram Arshad1,*, Saeed Hamood Alsamhi1, Wasif Afzal2

    CMC-Computers, Materials & Continua, Vol.74, No.2, pp. 2739-2770, 2023, DOI:10.32604/cmc.2023.030266

    Abstract Big Data is reforming many industrial domains by providing decision support through analyzing large data volumes. Big Data testing aims to ensure that Big Data systems run smoothly and error-free while maintaining the performance and quality of data. However, because of the diversity and complexity of data, testing Big Data is challenging. Though numerous research efforts deal with Big Data testing, a comprehensive review to address testing techniques and challenges of Big Data is not available as yet. Therefore, we have systematically reviewed the Big Data testing techniques’ evidence occurring in the period 2010–2021. This paper discusses testing data processing… More >

  • Open Access

    ARTICLE

    A Big Data Based Dynamic Weight Approach for RFM Segmentation

    Lin Lang1, Shuang Zhou1, Minjuan Zhong1,*, Guang Sun1, Bin Pan1, Peng Guo1,2

    CMC-Computers, Materials & Continua, Vol.74, No.2, pp. 3503-3513, 2023, DOI:10.32604/cmc.2023.023596

    Abstract Using the RFM (Recency, Frequency, Monetary value) model can provide valuable insights about customer clusters which is the core of customer relationship management. Due to accurate customer segment coming from dynamic weighted applications, in-depth targeted marketing may also use type of dynamic weight of R, F and M as factors. In this paper, we present our dynamic weighted RFM approach which is intended to improve the performance of customer segmentation by using the factors and variations to attain dynamic weights. Our dynamic weight approach is a kind of Custom method in essential which roots in the understanding of the data… More >

  • Open Access

    ARTICLE

    An Edge-Fog-Cloud Computing-Based Digital Twin Model for Prognostics Health Management of Process Manufacturing Systems

    Jie Ren1,2, Chuqiao Xu3, Junliang Wang2,4, Jie Zhang2,*, Xinhua Mao4, Wei Shen4

    CMES-Computer Modeling in Engineering & Sciences, Vol.135, No.1, pp. 599-618, 2023, DOI:10.32604/cmes.2022.022415

    Abstract The prognostics health management (PHM) from the systematic view is critical to the healthy continuous operation of process manufacturing systems (PMS), with different kinds of dynamic interference events. This paper proposes a three leveled digital twin model for the systematic PHM of PMSs. The unit-leveled digital twin model of each basic device unit of PMSs is constructed based on edge computing, which can provide real-time monitoring and analysis of the device status. The station-leveled digital twin models in the PMSs are designed to optimize and control the process parameters, which are deployed for the manufacturing execution on the fog server.… More >

  • Open Access

    ARTICLE

    Filter and Embedded Feature Selection Methods to Meet Big Data Visualization Challenges

    Kamal A. ElDahshan, AbdAllah A. AlHabshy, Luay Thamer Mohammed*

    CMC-Computers, Materials & Continua, Vol.74, No.1, pp. 817-839, 2023, DOI:10.32604/cmc.2023.032287

    Abstract This study focuses on meeting the challenges of big data visualization by using of data reduction methods based the feature selection methods. To reduce the volume of big data and minimize model training time (Tt) while maintaining data quality. We contributed to meeting the challenges of big data visualization using the embedded method based “Select from model (SFM)” method by using “Random forest Importance algorithm (RFI)” and comparing it with the filter method by using “Select percentile (SP)” method based chi square “Chi2” tool for selecting the most important features, which are then fed into a classification process using the… More >

  • Open Access

    ARTICLE

    Big Data Analytics Using Graph Signal Processing

    Farhan Amin1, Omar M. Barukab2, Gyu Sang Choi1,*

    CMC-Computers, Materials & Continua, Vol.74, No.1, pp. 489-502, 2023, DOI:10.32604/cmc.2023.030615

    Abstract The networks are fundamental to our modern world and they appear throughout science and society. Access to a massive amount of data presents a unique opportunity to the researcher’s community. As networks grow in size the complexity increases and our ability to analyze them using the current state of the art is at severe risk of failing to keep pace. Therefore, this paper initiates a discussion on graph signal processing for large-scale data analysis. We first provide a comprehensive overview of core ideas in Graph signal processing (GSP) and their connection to conventional digital signal processing (DSP). We then summarize… More >

  • Open Access

    REVIEW

    Intelligent Identification over Power Big Data: Opportunities, Solutions, and Challenges

    Liang Luo1, Xingmei Li1, Kaijiang Yang1, Mengyang Wei1, Jiong Chen1, Junqian Yang1, Liang Yao2,*

    CMES-Computer Modeling in Engineering & Sciences, Vol.134, No.3, pp. 1565-1595, 2023, DOI:10.32604/cmes.2022.021198

    Abstract The emergence of power dispatching automation systems has greatly improved the efficiency of power industry operations and promoted the rapid development of the power industry. However, with the convergence and increase in power data flow, the data dispatching network and the main station dispatching automation system have encountered substantial pressure. Therefore, the method of online data resolution and rapid problem identification of dispatching automation systems has been widely investigated. In this paper, we perform a comprehensive review of automated dispatching of massive dispatching data from the perspective of intelligent identification, discuss unresolved research issues and outline future directions in this… More >

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