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

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.

[1]  Roel Wieringa,et al.  Requirements engineering paper classification and evaluation criteria: a proposal and a discussion , 2005, Requirements Engineering.

[2]  Nandamudi Lankalapalli Vijaykumar,et al.  Analyzing the Use of Concept Maps in Computer Science: A Systematic Mapping Study , 2017, Informatics Educ..

[3]  Sanjay Misra,et al.  Cloud-Based Security Driven Human Resource Management System , 2017, ICADIWT.

[4]  Kai Petersen,et al.  Systematic Mapping Studies in Software Engineering , 2008, EASE.

[5]  Mohsen Guizani,et al.  Deep Learning for IoT Big Data and Streaming Analytics: A Survey , 2017, IEEE Communications Surveys & Tutorials.

[6]  Zucker Andreas,et al.  Systematic mapping of power system models: Expert survey , 2017 .

[7]  Marjan Mernik,et al.  Domain-Specific Languages: A Systematic Mapping Study , 2017, SOFSEM.

[8]  Fabiane Barreto Vavassori Benitti,et al.  Systematic Mapping Protocol: The impact of using software patterns during requirements engineering activities in real-world settings , 2017, ArXiv.

[9]  N. B. Anuar,et al.  The rise of "big data" on cloud computing: Review and open research issues , 2015, Inf. Syst..

[10]  Sanjay Misra,et al.  Cloud Multi-Tenancy: Issues and Developments , 2017, UCC.

[11]  Rajkumar Buyya,et al.  Cloud Computing Principles and Paradigms , 2011 .

[12]  Isaac Odun-Ayo,et al.  Cloud Computing Architecture: A Critical Analysis , 2018, 2018 18th International Conference on Computational Science and Applications (ICCSA).

[13]  Muhammad Ali Babar,et al.  A Systematic Mapping Study of Software Architectures for Cloud Based Systems , 2014 .

[14]  Isaac Odun-Ayo,et al.  Cloud Applications Management - Issues and Developments , 2018, ICCSA.

[15]  Eduardo Figueiredo,et al.  A systematic mapping study on game-related methods for software engineering education , 2017, Inf. Softw. Technol..

[16]  Giancarlo Guizzardi,et al.  A Systematic Mapping of the Literature on Legal Core Ontologies , 2015, ONTOBRAS.

[17]  Olasupo Ajayi,et al.  Cloud Ownership and Reliability - Issues and Developments , 2017, SpaCCS Workshops.

[18]  Ciprian Dobre,et al.  Big Data and Cloud Computing: A Survey of the State-of-the-Art and Research Challenges , 2017 .

[19]  Pearl Brereton,et al.  A Systematic Mapping Study of Empirical Studies on Software Cloud Testing Methods , 2017, 2017 IEEE International Conference on Software Quality, Reliability and Security Companion (QRS-C).

[20]  Marjan Mernik,et al.  Domain-Specific Languages: A Systematic Mapping Study , 2016, Inf. Softw. Technol..