Cloud-Based Big Data Analytics—A Survey of Current Research and Future Directions

Khan, Samiya; Shakil, Kashish Ara; Alam, Mansaf

doi:10.1007/978-981-10-6620-7_57

Cited by 41 publications

(24 citation statements)

References 20 publications

(17 reference statements)

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“…Data Analytics is the heart of application development in IoT Cloud environment. Number of applications can be developed using big data analytics in cloud environment [21]. Task scheduling and load balancing is required to improve efficiency of platforms [2].…”

Section: Literature Reviewmentioning

confidence: 99%

Investigating IoT Middleware Platforms for Smart Application Development

Agarwal

Alam

2020

Lecture Notes in Civil Engineering

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With the growing number of Internet of Things (IoT) devices, the data generated through these devices is also increasing. By 2030, it is been predicted that the number of IoT devices will exceed the number of human beings on earth. This gives rise to the requirement of middleware platform that can manage IoT devices, intelligently store and process gigantic data generated for building smart applications such as Smart Cities, Smart Healthcare, Smart Industry, and others. At present, market is overwhelming with the number of IoT middleware platforms with specific features. This raises one of the most serious and least discussed challenge for application developer to choose suitable platform for their application development. Across the literature, very little attempt is done in classifying or comparing IoT middleware platforms for the applications. This paper categorizes IoT platforms into four categories namely: publicly traded, open source, developer friendly and end-to-end connectivity. Some of the popular middleware platforms in each category are investigated based on general IoT architecture. Comparison of IoT middleware platforms in each category, based on basic, sensing, communication and application development features is presented. This study can be useful for IoT application developers to select the most appropriate platform according to their application requirement.

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Section: Literature Reviewmentioning

confidence: 99%

Investigating IoT Middleware Platforms for Smart Application Development

Agarwal

Alam

2020

Lecture Notes in Civil Engineering

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“…One of the research trends identified among the recent applications is cloud computing, which provides a better solution for big data storage, transmitting and computational requirements [125]. It is also worth noting the popularity of integrating the Internet of Things [126], the architecture of a platform that enables efficient, real-time, in memory cloud computing and storage of complex big data with the most advanced analytics or data mining techniques, as well as integrating the Internet of Things is the key to future research.…”

Section: Conclusion and Future Researchmentioning

confidence: 99%

Big Data and Climate Change

Hassani

Huang

Silva

2019

BDCC

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Climate science as a data-intensive subject has overwhelmingly affected by the era of big data and relevant technological revolutions. The big successes of big data analytics in diverse areas over the past decade have also prompted the expectation of big data and its efficacy on the big problem—climate change. As an emerging topic, climate change has been at the forefront of the big climate data analytics implementations and exhaustive research have been carried out covering a variety of topics. This paper aims to present an outlook of big data in climate change studies over the recent years by investigating and summarising the current status of big data applications in climate change related studies. It is also expected to serve as a one-stop reference directory for researchers and stakeholders with an overview of this trending subject at a glance, which can be useful in guiding future research and improvements in the exploitation of big climate data.

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“…The applicability of big data technologies [6], implemented using cloud-based infrastructures, to real-world, data-intensive scenarios has given rise to many sub-research areas. Big data has particularly found applications in healthcare [7], geospatial analytics [8] and business intelligence [9], in addition to many others. Education data is a form of big data and can make use of big data technologies for generating valuable analytics for an educational organization [10].…”

Section: Related Workmentioning

confidence: 99%

Big Data Technology-Enabled Analytical Solution for Quality Assessment of Higher Education Systems

Khan

Alam

Liu

et al. 2019

IJACSA

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Educational Intelligence is a broad area of big data analytical applications that make use of big data technologies for implementation of solutions for education and research. This paper demonstrates the designing, development and deployment of an educational intelligence application for real-world scenarios. Firstly, a quality assessment framework for higher education systems that evaluate institutions on the basis of performance of outgoing students was proposed. Secondly, big data enabled technological setup was used for its implementation. Literature was surveyed to evaluate existing quality frameworks. Most existing quality assessment systems take into account the dimensions related to inputs, processes and outputs, but they tend to ignore the perspective that assesses the institution on the basis of outcome of the educational process. This paper demonstrates the use of outcome perspective to compute quality metrics and create visual analytics. In order to implement and test the framework, R programming language and a cloud based big data technology that is Google, BigQuery were used.

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Cloud-Based Big Data Analytics—A Survey of Current Research and Future Directions

Cited by 41 publications

References 20 publications

Investigating IoT Middleware Platforms for Smart Application Development

Investigating IoT Middleware Platforms for Smart Application Development

Big Data and Climate Change

Big Data Technology-Enabled Analytical Solution for Quality Assessment of Higher Education Systems

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