A Systematic Mapping Study of Cloud Large-Scale Foundation-Big Data, IoT, and Real-Time Analytics

被引:1
|
作者
Odun-Ayo, Isaac [1 ]
Goddy-Worlu, Rowland [1 ]
Abayomi-Zannu, Temidayo [1 ]
Grant, Emanuel [2 ]
机构
[1] Covenant Univ, Dept Comp & Informat Sci, Ota, Nigeria
[2] Univ North Dakota, Sch Elect Engn & Comp Sci, Grand Forks, ND USA
关键词
Big data; Cloud computing; Internet of Things; Real-time analytics; Systematic mapping; DOMAIN-SPECIFIC LANGUAGES; RESOURCE-MANAGEMENT; MAPREDUCE; MECHANISM; FRAMEWORK; SERVICES; INTERNET; CLUSTERS; ACCESS;
D O I
10.1007/978-981-32-9949-8_24
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
Cloud computing is a unique concept which makes analysis and data easy to manipulate using large-scale infrastructure available to Cloud service providers. However, it is sometimes rigorous to determine a topic for research in terms of Cloud. A systematic map allows the categorization of study in a particular field using an exclusive scheme enabling the identification of gaps for further research. In addition, a systematic mapping study can provide insight into the level of the research that is being conducted in any area of interest. The results generated from such a study are presented using a map. The method utilized in this study involved analysis using three categories which are research, topic, and contribution facets. Topics were obtained from the primary studies, while the research type such as evaluation and the contribution type such as tool were utilized in the analysis. The objective of this paper was to achieve a systematic mapping study of the Cloud large-scale foundation. This provided an insight into the frequency of work which has been carried out in this area of study. The results indicated that the highest publications were on IoT as it relates to model with 12.26%; there were more publications on data analytics as is relates to metric with 2.83%, more articles on big data in terms of tool, with 11.32%, method with 9.43% and more research carried out on data management in terms of process with 6.6%. This outcome will be valuable to the Cloud research community, service providers, and users alike.
引用
收藏
页码:339 / 363
页数:25
相关论文
共 50 条
  • [21] A Methodology of Real-Time Data Fusion for Localized Big Data Analytics
    Jabbar, Sohail
    Malik, Kaleem R.
    Ahmad, Mudassar
    Aldabbas, Omar
    Asif, Muhammad
    Khalid, Shehzad
    Han, Kijun
    Ahmed, Syed Hassan
    [J]. IEEE ACCESS, 2018, 6 : 24510 - 24520
  • [22] Real-Time Big Data Analytics in Smart Cities from LoRa-Based IoT Networks
    Fernandez, Antonio M.
    Gutierrez-Aviles, David
    Troncoso, Alicia
    Martinez-Alvarez, Francisco
    [J]. 14TH INTERNATIONAL CONFERENCE ON SOFT COMPUTING MODELS IN INDUSTRIAL AND ENVIRONMENTAL APPLICATIONS (SOCO 2019), 2020, 950 : 91 - 100
  • [23] Logical big data integration and near real-time data analytics
    Silva, Bruno
    Moreira, Jose
    Costa, Rogerio Luis de C.
    [J]. DATA & KNOWLEDGE ENGINEERING, 2023, 146
  • [24] Big Data Stream Computing in Healthcare Real-Time Analytics
    Ta, Van-Dai
    Liu, Chuan-Ming
    Nkabinde, Goodwill Wandile
    [J]. PROCEEDINGS OF 2016 IEEE INTERNATIONAL CONFERENCE ON CLOUD COMPUTING AND BIG DATA ANALYSIS (ICCCBDA 2016), 2016, : 37 - 42
  • [25] A Survey on Real-time Big Data Analytics: Applications and Tools
    Yadranjiaghdam, Babak
    Pool, Nathan
    Tabrizi, Nasseh
    [J]. 2016 INTERNATIONAL CONFERENCE ON COMPUTATIONAL SCIENCE & COMPUTATIONAL INTELLIGENCE (CSCI), 2016, : 404 - 409
  • [26] An incremental approach for real-time Big Data visual analytics
    Garcia, Ignacio
    Casado, Ruben
    Bouchachia, Abdelhamid
    [J]. 2016 IEEE 4TH INTERNATIONAL CONFERENCE ON FUTURE INTERNET OF THINGS AND CLOUD WORKSHOPS (FICLOUDW), 2016, : 177 - 182
  • [27] Real-time Big Data Analytics for Multimedia Transmission and Storage
    Wang, Kun
    Mi, Jun
    Xu, Chenhan
    Shu, Lei
    Deng, Der-Jiunn
    [J]. 2016 IEEE/CIC INTERNATIONAL CONFERENCE ON COMMUNICATIONS IN CHINA (ICCC), 2016,
  • [28] Big Data Streaming Platforms to Support Real-time Analytics
    Fernandes, Eliana
    Salgado, Ana Carolina
    Bernardino, Jorge
    [J]. ICSOFT: PROCEEDINGS OF THE 15TH INTERNATIONAL CONFERENCE ON SOFTWARE TECHNOLOGIES, 2020, : 426 - 433
  • [29] A Big Data Architecture for Near Real-time Traffic Analytics
    Gong, Yikai
    Rimba, Paul
    Sinnott, Richard O.
    [J]. COMPANION PROCEEDINGS OF THE 10TH INTERNATIONAL CONFERENCE ON UTILITY AND CLOUD COMPUTING (UCC'17 COMPANION), 2017, : 157 - 162
  • [30] Big Data Analytics Architecture for Real-Time Traffic Control
    Amini, Sasan
    Gerostathopoulos, Ilias
    Prehofer, Christian
    [J]. 2017 5TH IEEE INTERNATIONAL CONFERENCE ON MODELS AND TECHNOLOGIES FOR INTELLIGENT TRANSPORTATION SYSTEMS (MT-ITS), 2017, : 710 - 715