CBA: Cloud-based Bigdata Analytics

被引:6
|
作者
Pradhananga, Yanish [1 ]
Karande, Shridevi [1 ]
Karande, Chandraprakash [2 ]
机构
[1] Maharashtra Inst Technol, Dept Comp Engn, Pune, Maharashtra, India
[2] Greenova Corp, Pune, Maharashtra, India
关键词
R; minitab; spss; IBM BigInsight; Revolution Analytics; Apache Hadoop; Cloud; SaaS;
D O I
10.1109/ICCUBEA.2015.18
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
The requirement to perform complicated statistic analysis of big data by institutions of engineering, scientific research, health care, commerce, banking and computer research is immense. However, the limitations of the widely used current desktop software like R, excel, minitab and spss gives a researcher limitation to deal with big data. The big data analytic tools like IBM BigInsight, Revolution Analytics, and tableau software are commercial and heavily license. Still, to deal with big data, client has to invest in infrastructure, installation and maintenance of hadoop cluster to deploy these analytical tools. Apache Hadoop is an open source distributed computing framework that uses commodity hardware. With this project, I intend to collaborate Apac\he Hadoop and R software over the on the Cloud. Objective is to build a SaaS (Software-as-a-Service) analytic platform that stores & analyzes big data using open source Apache Hadoop and open source R software. The benefits of this cloud based big data analytical service are user friendliness & cost as it is developed using open-source software. The system is cloud based so users have their own space in cloud where user can store there data. User can browse data, files, folders using browser and arrange datasets. User can select dataset and analyze required dataset and store result back to cloud storage. Enterprise with a cloud environment can save cost of hardware, upgrading software, maintenance or network configuration, thus it making it more economical.
引用
收藏
页码:47 / 51
页数:5
相关论文
共 50 条
  • [1] BBC: A DSL for Designing Cloud-based Heterogeneous Bigdata Pipelines
    Jacob, Ferosh
    Karunanithi, Ilamgumaran
    Salian, Pramod
    Sambhu, Ravi
    [J]. 2017 IEEE INTERNATIONAL CONFERENCE ON BIG DATA (BIG DATA), 2017, : 1642 - 1645
  • [2] Towards Cloud-based Analytics-as-a-Service (CLAaaS) for Big Data Analytics in the Cloud
    Zulkernine, Farhana
    Martin, Patrick
    Zou, Ying
    Bauer, Michael
    Gwadry-Sridhar, Femida
    Aboulnaga, Ashraf
    [J]. 2013 IEEE INTERNATIONAL CONGRESS ON BIG DATA, 2013, : 62 - 69
  • [3] Facilitating personalized medicine with cloud-based storage and analytics
    Howard, Rachel
    Hicks, Kevin
    Teer, Jamie
    Reisman, Phillip
    O'Leary, Mandy
    Eschrich, Steven
    Mitchell, Ross
    McLeod, Howard
    Rollison, Dana
    [J]. CANCER RESEARCH, 2020, 80 (16)
  • [4] An Instructional Cloud-Based Testbed for Image and Video Analytics
    Hacker, Thomas J.
    Lu, Yung-Hsiang
    [J]. 2014 IEEE 6TH INTERNATIONAL CONFERENCE ON CLOUD COMPUTING TECHNOLOGY AND SCIENCE (CLOUDCOM), 2014, : 859 - 862
  • [5] Distributed and Cloud-based Big Data Analytics and Fusion
    Das, Subrata
    [J]. SIGNAL PROCESSING, SENSOR FUSION, AND TARGET RECOGNITION XXII, 2013, 8745
  • [6] Pipeline provenance for cloud-based big data analytics
    Wang, Ruoyu
    Sun, Daniel
    Li, Guoqiang
    Wong, Raymond
    Chen, Shiping
    [J]. SOFTWARE-PRACTICE & EXPERIENCE, 2020, 50 (05): : 658 - 674
  • [7] Demo: Cloud-Based Vehicular Data Analytics Platform
    Muramudalige, Shashika Ranga
    Bandara, H. M. N. Dilum
    [J]. MOBISYS'16: COMPANION COMPANION PUBLICATION OF THE 14TH ANNUAL INTERNATIONAL CONFERENCE ON MOBILE SYSTEMS, APPLICATIONS, AND SERVICES, 2016, : 1 - 1
  • [8] Characterizing Incidents in Cloud-based IoT Data Analytics
    Hong-Linh Truong
    Halper, Manfred
    [J]. 2018 IEEE 42ND ANNUAL COMPUTER SOFTWARE AND APPLICATIONS CONFERENCE (COMPSAC), VOL 1, 2018, : 442 - 447
  • [9] Optimized Data Analysis in Cloud using BigData Analytics Techniques
    Ramamoorthy, S.
    Rajalakshmi, S.
    [J]. 2013 FOURTH INTERNATIONAL CONFERENCE ON COMPUTING, COMMUNICATIONS AND NETWORKING TECHNOLOGIES (ICCCNT), 2013,
  • [10] Cloud-based video analytics using convolutional neural networks
    Yaseen, Muhammad Usman
    Anjum, Ashiq
    Farid, Mohsen
    Antonopoulos, Nick
    [J]. SOFTWARE-PRACTICE & EXPERIENCE, 2019, 49 (04): : 565 - 583