Mining Skyline Patterns from Big Data Environments based on a Spark Framework

被引：0
|
作者：
Jimmy Ming-Tai Wu
Huiying Zhou
Jerry Chun-Wei Lin
Gautam Srivastava
Mohamed Baza
机构：
[1] Shandong University of Science and Technology,College of Computer Science and Engineering
[2] Western Norway University of Applied Sciences,Department of Computer Science, Electrical Engineering and Mathematical Sciences
[3] Brandon University,Department of Mathematics, Computer Science
[4] China Medical University,Research Centre for Interneural Computing
[5] Lebanese American University,Department of Computer Science and Mathematics
[6] College of Charleston,Department of Computer Science
来源：
Journal of Grid Computing | 2023年 / 21卷
关键词：
Data mining; Skyline frequent-utility pattern; Spark; Edge computing;
D O I：
暂无
中图分类号：
学科分类号：
摘要：
Simultaneously, the application of resilient distributed datasets (RDD) in cloud computing provides a good environment for data analysis of big data. In addition, the combination of Machine Learning (ML) algorithms of the edge computing paradigm and the SFUP-SP algorithm may be able to also be used to improve local computing capabilities and speed up data analysis and user decision-making.
引用
收藏
相关论文
共 50 条
  • [1] Mining Skyline Patterns from Big Data Environments based on a Spark Framework
    Wu, Jimmy Ming-Tai
    Zhou, Huiying
    Lin, Jerry Chun-Wei
    Srivastava, Gautam
    Baza, Mohamed
    [J]. JOURNAL OF GRID COMPUTING, 2023, 21 (02)
  • [2] Mining skyline frequent-utility patterns from big data environment based on MapReduce framework
    Wu, Jimmy Ming-Tai
    Li, Ranran
    Wu, Mu-En
    Lin, Jerry Chun-Wei
    [J]. INTELLIGENT DATA ANALYSIS, 2023, 27 (05) : 1359 - 1377
  • [3] Mining Sequential Patterns from Uncertain Big DNA in the Spark Framework
    Jiang, Fan
    Leung, Carson K.
    Sarumi, Oluwafemi A.
    Zhang, Christine Y.
    [J]. 2016 IEEE INTERNATIONAL CONFERENCE ON BIOINFORMATICS AND BIOMEDICINE (BIBM), 2016, : 874 - 881
  • [4] A Parallel DistributedWeka Framework for Big Data Mining using Spark
    Koliopoulos, Aris-Kyriakos
    Yiapanis, Paraskevas
    Tekiner, Firat
    Nenadic, Goran
    Keane, John
    [J]. 2015 IEEE INTERNATIONAL CONGRESS ON BIG DATA - BIGDATA CONGRESS 2015, 2015, : 9 - 16
  • [5] A Framework for Co-location Patterns Mining in Big Spatial Data
    Garaeva, A.
    Makhmutova, F.
    Anikin, I.
    Sattler, Kai-Uwe
    [J]. PROCEEDINGS OF 2017 XX IEEE INTERNATIONAL CONFERENCE ON SOFT COMPUTING AND MEASUREMENTS (SCM), 2017, : 477 - 480
  • [6] A data cleaning model for electric power big data based on Spark framework
    Qu, Zhao-Yang
    Wang, Yong-Wen
    Wang, Chong
    Qu, Nan
    Yan, Jia
    [J]. International Journal of Database Theory and Application, 2016, 9 (03): : 137 - 150
  • [7] An Efficient Parallel Algorithm for Clustering Big Data based on the Spark Framework
    Faculty of Science of Rabat, Mohammed V University, Rabat, Morocco
    [J]. Intl. J. Adv. Comput. Sci. Appl., 7 (890-896):
  • [8] Spark Based Distributed Deep Learning Framework For Big Data Applications
    Khumoyun, Akhmedov
    Cui, Yun
    Hanku, Lee
    [J]. 2016 INTERNATIONAL CONFERENCE ON INFORMATION SCIENCE AND COMMUNICATIONS TECHNOLOGIES (ICISCT), 2016,
  • [9] An Efficient Parallel Algorithm for Clustering Big Data based on the Spark Framework
    Dafir, Zineb
    Slaoui, Said
    [J]. INTERNATIONAL JOURNAL OF ADVANCED COMPUTER SCIENCE AND APPLICATIONS, 2022, 13 (07) : 890 - 896
  • [10] A Dynamic Spark-based Classification Framework for Imbalanced Big Data
    Abdel-Hamid, Nahla B.
    ElGhamrawy, Sally
    El Desouky, Ali
    Arafat, Hesham
    [J]. JOURNAL OF GRID COMPUTING, 2018, 16 (04) : 607 - 626
12345