Applications of the MapReduce programming framework to clinical big data analysis: current landscape and future trends

被引:0
|
作者
Emad A Mohammed
Behrouz H Far
Christopher Naugler
机构
[1] Schulich School of Engineering,Department of Electrical and Computer Engineering
[2] University of Calgary,Department of Pathology and Laboratory Medicine
[3] University of Calgary and Calgary Laboratory Services,undefined
来源
关键词
MapReduce; Hadoop; Big data; Clinical big data analysis; Clinical data analysis; Bioinformatics; Distributed programming;
D O I
暂无
中图分类号
学科分类号
摘要
The emergence of massive datasets in a clinical setting presents both challenges and opportunities in data storage and analysis. This so called “big data” challenges traditional analytic tools and will increasingly require novel solutions adapted from other fields. Advances in information and communication technology present the most viable solutions to big data analysis in terms of efficiency and scalability. It is vital those big data solutions are multithreaded and that data access approaches be precisely tailored to large volumes of semi-structured/unstructured data.
引用
收藏
相关论文
共 50 条
  • [11] Big data in cybersecurity: a survey of applications and future trends
    Alani M.M.
    Journal of Reliable Intelligent Environments, 2021, 7 (02) : 85 - 114
  • [12] Exploring the landscape of big data applications in librarianship: a bibliometric analysis of research trends and patterns
    Islam, Md. Nurul
    Hu, Guangwei
    Ashiq, Murtaza
    Ahmad, Shakil
    LIBRARY HI TECH, 2024,
  • [13] Big Data in Construction: Current Applications and Future Opportunities
    Munawar, Hafiz Suliman
    Ullah, Fahim
    Qayyum, Siddra
    Shahzad, Danish
    BIG DATA AND COGNITIVE COMPUTING, 2022, 6 (01)
  • [14] A Performance Analysis of MapReduce Applications on Big Data in Cloud based Hadoop
    Gohil, Parth
    Garg, Dweepna
    Panchal, Bakul
    2014 INTERNATIONAL CONFERENCE ON INFORMATION COMMUNICATION AND EMBEDDED SYSTEMS (ICICES), 2014,
  • [15] MapReduce: Simplified Data Analysis of Big Data
    Maitrey, Seema
    Jha, C. K.
    3RD INTERNATIONAL CONFERENCE ON RECENT TRENDS IN COMPUTING 2015 (ICRTC-2015), 2015, 57 : 563 - 571
  • [16] Analysis of the Big Data based on MapReduce
    Tian, Zi-de
    PROCEEDINGS OF THE 2015 INTERNATIONAL CONFERENCE ON AUTOMATION, MECHANICAL CONTROL AND COMPUTATIONAL ENGINEERING, 2015, 124 : 224 - 228
  • [17] MapReduce Algorithms for Big Data Analysis
    Shim, Kyuseok
    PROCEEDINGS OF THE VLDB ENDOWMENT, 2012, 5 (12): : 2016 - 2017
  • [18] MapReduce Algorithms for Big Data Analysis
    Shim, Kyuseok
    DATABASES THEORY AND APPLICATIONS, ADC 2018, 2018, 10837 : XV - XV
  • [19] Big data classification with optimization driven MapReduce framework
    Mohammed, Mujeeb Shaik
    Rachapudy, Praveen Sam
    Kasa, Madhavi
    INTERNATIONAL JOURNAL OF KNOWLEDGE-BASED AND INTELLIGENT ENGINEERING SYSTEMS, 2021, 25 (02) : 173 - 183
  • [20] Improving Network Traffic in MapReduce for Big Data Applications
    Gawande, Priya
    Shaikh, Nuzhaft
    2016 INTERNATIONAL CONFERENCE ON ELECTRICAL, ELECTRONICS, AND OPTIMIZATION TECHNIQUES (ICEEOT), 2016, : 2979 - 2983