Cloud-Based Big Data Analytics—A Survey of Current Research and Future Directions
TL;DR: The existing research, challenges, open issues, and future research direction for cloud-based analytics are explored, with a view to practical applications of this synergistic model can be popularly used.
Abstract: The advent of the digital age has led to a rise in different types of data with every passing day. In fact, it is expected that half of the total data will be on the cloud by 2016. This data is complex and needs to be stored, processed, and analyzed for information that can be used by organizations. Cloud computing provides an apt platform for big data analytics in view of the storage and computing requirements of the latter. This makes cloud-based analytics a viable research field. However, several issues need to be addressed and risks need to be mitigated before practical applications of this synergistic model can be popularly used. This paper explores the existing research, challenges, open issues, and future research direction for this field of study.
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02 Feb 2019
TL;DR: This paper is 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.
Abstract: 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.
63 citations
Cites background from "Cloud-Based Big Data Analytics—A Su..."
...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]....
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TL;DR: Comparison of IoT middleware platforms in each category, based on basic, sensing, communication and application development features, is presented and can be useful for IoT application developers to select the most appropriate platform according to their application requirement.
Abstract: With the growing number of Internet of Things (IoT) devices, the data generated through these devices is also increasing. By 2030, it has 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 health care, 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 challenges 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.
28 citations
Cites background from "Cloud-Based Big Data Analytics—A Su..."
...Number of applications can be developed using big data analytics in cloud environment [21]....
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01 Dec 2016
TL;DR: This research paper explores how cloud-based big data analytics can be applied to Indian education and research and reviews the challenges that need to be addressed before the true benefits of this technology can be obtained.
Abstract: Big Data technology is a generic technology, which can be applied to any real-world problem that involves a lot of data. Moreover, the use of cloud-based infrastructure to implement the big data technology makes it a cost-effective solution to the big data problem. One of the fundamental sectors that can benefit from this technology is education and research. The education system can use big data analytics to provide better education and administer the institutional operations. Research, which is an extension of education, can use analytics of big scholar data, for diverse applications, to facilitate research at the individual, team and organization level. With that said, the practical implementation and adoption of big data for education and research, collectively referred to as ‘Educational Intelligence’, faces several challenges, particularly in a developing country like India. This research paper explores how cloud-based big data analytics can be applied to Indian education and research and reviews the challenges that need to be addressed before the true benefits of this technology can be obtained.
22 citations
01 Jan 2021
TL;DR: In this paper, the authors present wearable devices as part of the healthcare system toward combating the COIVD-19 pandemic and discuss the challenges associated with wearable devices in the COVID-19 scenario such as real-time processing, heterogeneity, interoperability, security and privacy.
Abstract: In the current century, the novel coronavirus has presented itself as a serious threat to the global human population. However, constructively, with the intervention of the latest computing technology such as the Internet of Things, distributed cloud computing, and artificial intelligence, the COVID-19 pandemic can be effectively handled. From this aspect, the main objectives of this chapter are to study and present various wearable devices as part of the healthcare system toward combating the COIVD-19 pandemic. First, this work aims to review the different wearable devices and their usage to combat COVID-19 by patients, healthcare professional, frontliners, and global citizens. Hence, the major objectives of these wearable devices include device tracking, information sharing, and awareness creation to minimize the risk of coronavirus infection. Second, the chapter addresses a generalized framework toward the implementation of wearable devices to handle the COVID-19 pandemic. Next, this chapter aims to review monitoring techniques and various mechanisms used to analyze the data gathered from wearable devices in order to extract useful and critical information pertaining to users in the COVID-19 scenario. This chapter involves reviewing efficient techniques and algorithms that exist in literature for data analysis based on vital body signals from the wearable sensor devices. This effort enhances the patient/healthcare staff monitoring mechanism and helps to uncover preventive solutions in the COVID-19 scenario. Particularly, the data processing and analysis mechanisms such as data denoising, data aggregation, data outlier detection, and missing data imputation are emphasized. Finally, the chapter addresses various challenges associated with wearable devices in the COVID-19 scenario such as real-time processing, heterogeneity, interoperability, security, and privacy.
19 citations
01 Jan 2019
TL;DR: This paper identifies open research problems associated with this domain, giving insights on specific issues like workflow scheduling and execution and deployment of big data scientific workflows in a multi-site cloud environment.
Abstract: The concept of workflows was implemented to mitigate the complexities involved in tasks related to scientific computing and business analytics. With time, they have found applications in many diverse fields and domains. Handling big data has given rise to many other issues like growing computing complexity, increasing data size, provisioning of resources and the need for such systems to enable working together of heterogeneous systems. As a result, traditional systems are deemed obsolete for this purpose. To meet the variable resource requirements, cloud has emerged as an ostensible solution. Execution and deployment of big data scientific workflows in the cloud is an area that requires research attention before a synergistic model for the same can be presented. This paper identifies open research problems associated with this domain, giving insights on specific issues like workflow scheduling and execution and deployment of big data scientific workflows in a multi-site cloud environment.
16 citations
References
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University of Vermont1, University of Minnesota2, University of Texas at Austin3, Hong Kong University of Science and Technology4, Osaka University5, University of Queensland6, Griffith University7, University of Illinois at Chicago8, IBM9, Nanjing University10, Imperial College London11, University of Salford12
TL;DR: This paper presents the top 10 data mining algorithms identified by the IEEE International Conference on Data Mining (ICDM) in December 2006: C4.5, k-Means, SVM, Apriori, EM, PageRank, AdaBoost, kNN, Naive Bayes, and CART.
Abstract: This paper presents the top 10 data mining algorithms identified by the IEEE International Conference on Data Mining (ICDM) in December 2006: C4.5, k-Means, SVM, Apriori, EM, PageRank, AdaBoost, kNN, Naive Bayes, and CART. These top 10 algorithms are among the most influential data mining algorithms in the research community. With each algorithm, we provide a description of the algorithm, discuss the impact of the algorithm, and review current and further research on the algorithm. These 10 algorithms cover classification, clustering, statistical learning, association analysis, and link mining, which are all among the most important topics in data mining research and development.
4,944 citations
"Cloud-Based Big Data Analytics—A Su..." refers background in this paper
... It was in the 1980s that artificial intelligence-based algorithms were developed for data mining. Wu, Kumar, Quinlan, Ghosh, Yang, Motoda, McLachlan, Ng, Liu, Yu, Zhou, Steinbach, Hand and Steinberg [25] mention the ten most influential data mining algorithms k-means, C4.5, Apriori, Expectation Maximization (EM), PageRank, SVM (support vector machine), AdaBoost, CART, a ve Bayes and kNN (k-nearest ne...
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TL;DR: This introduction to the MIS Quarterly Special Issue on Business Intelligence Research first provides a framework that identifies the evolution, applications, and emerging research areas of BI&A, and introduces and characterized the six articles that comprise this special issue in terms of the proposed BI &A research framework.
Abstract: Business intelligence and analytics (BI&A) has emerged as an important area of study for both practitioners and researchers, reflecting the magnitude and impact of data-related problems to be solved in contemporary business organizations. This introduction to the MIS Quarterly Special Issue on Business Intelligence Research first provides a framework that identifies the evolution, applications, and emerging research areas of BI&A. BI&A 1.0, BI&A 2.0, and BI&A 3.0 are defined and described in terms of their key characteristics and capabilities. Current research in BI&A is analyzed and challenges and opportunities associated with BI&A research and education are identified. We also report a bibliometric study of critical BI&A publications, researchers, and research topics based on more than a decade of related academic and industry publications. Finally, the six articles that comprise this special issue are introduced and characterized in terms of the proposed BI&A research framework.
4,610 citations
TL;DR: This paper is aimed to demonstrate a close-up view about Big Data, including Big Data applications, Big Data opportunities and challenges, as well as the state-of-the-art techniques and technologies currently adopt to deal with the Big Data problems.
2,516 citations
"Cloud-Based Big Data Analytics—A Su..." refers background in this paper
...Apart from standard applications in business and commerce and society administration, scientific research is one of the most critical applications of big data in the real world [30]....
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TL;DR: A HACE theorem is presented that characterizes the features of the Big Data revolution, and a Big Data processing model is proposed, from the data mining perspective, which involves demand-driven aggregation of information sources, mining and analysis, user interest modeling, and security and privacy considerations.
Abstract: Big Data concern large-volume, complex, growing data sets with multiple, autonomous sources. With the fast development of networking, data storage, and the data collection capacity, Big Data are now rapidly expanding in all science and engineering domains, including physical, biological and biomedical sciences. This paper presents a HACE theorem that characterizes the features of the Big Data revolution, and proposes a Big Data processing model, from the data mining perspective. This data-driven model involves demand-driven aggregation of information sources, mining and analysis, user interest modeling, and security and privacy considerations. We analyze the challenging issues in the data-driven model and also in the Big Data revolution.
2,233 citations
TL;DR: The definition, characteristics, and classification of big data along with some discussions on cloud computing are introduced, and research challenges are investigated, with focus on scalability, availability, data integrity, data transformation, data quality, data heterogeneity, privacy, legal and regulatory issues, and governance.
2,141 citations
"Cloud-Based Big Data Analytics—A Su..." refers methods in this paper
...2 – Use of Cloud Computing in Big Data [27])...
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...The requirement of an efficient and effective analytics service, applications, programming tools and frameworks has given birth to the concept of Big Data Processing and Analytics. global security and prediction and management of issues concerning the socio-economic and environmental sector, to name a few....
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...Moreover, the data mining algorithms used for Big Data analytics possess high computing requirements....
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...Data model for Big Data in cloud environment....
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...Traditional data management tools and data processing or data mining techniques cannot be used for Big Data Analytics for the large volume and complexity of the datasets that it includes....
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