Big Data Analytics in Cloud Computing

2019 ◽

pp. 325-341

Author(s):

Rajganesh Nagarajan ◽

Ramkumar Thirunavukarasu

Keyword(s):

Cloud Computing ◽

Big Data ◽

Data Processing ◽

Data Visualization ◽

Data Analytics ◽

Big Data Analytics ◽

Future Research ◽

Big Data Processing ◽

Investment Cost

In this chapter, the authors consider different categories of data, which are processed by the big data analytics tools. The challenges with respect to the big data processing are identified and a solution with the help of cloud computing is highlighted. Since the emergence of cloud computing is highly advocated because of its pay-per-use concept, the data processing tools can be effectively deployed within cloud computing and certainly reduce the investment cost. In addition, this chapter talks about the big data platforms, tools, and applications with data visualization concept. Finally, the applications of data analytics are discussed for future research.

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Cloud Computing in Bioinformatics and Big Data Analytics: Current Status and Future Research

Advances in Intelligent Systems and Computing - Big Data Analytics ◽

10.1007/978-981-10-6620-7_60 ◽

2017 ◽

pp. 629-640 ◽

Cited By ~ 3

Author(s):

Kashish Ara Shakil ◽

Mansaf Alam

Keyword(s):

Cloud Computing ◽

Big Data ◽

Data Analytics ◽

Big Data Analytics ◽

Current Status ◽

Future Research

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Modeling Big Data Analytics with a Real-Time Executable Specification Language

Handbook of Research on Trends and Future Directions in Big Data and Web Intelligence - Advances in Data Mining and Database Management ◽

10.4018/978-1-4666-8505-5.ch014 ◽

2015 ◽

pp. 289-312

Author(s):

Amir A. Khwaja

Keyword(s):

Big Data ◽

Data Processing ◽

Real Time ◽

Data Analytics ◽

Big Data Analytics ◽

Exception Handling ◽

Specification Language ◽

Big Data Processing ◽

Business Decisions ◽

Concurrent Processes

Big data explosion has already happened and the situation is only going to exacerbate with such a high number of data sources and high-end technology prevalent everywhere, generating data at a frantic pace. One of the most important aspects of big data is being able to capture, process, and analyze data as it is happening in real-time to allow real-time business decisions. Alternate approaches must be investigated especially consisting of highly parallel and real-time computations for big data processing. The chapter presents RealSpec real-time specification language that may be used for the modeling of big data analytics due to the inherent language features needed for real-time big data processing such as concurrent processes, multi-threading, resource modeling, timing constraints, and exception handling. The chapter provides an overview of RealSpec and applies the language to a detailed big data event recognition case study to demonstrate language applicability to big data framework and analytics modeling.

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Modeling Big Data Analytics with a Real-Time Executable Specification Language

Big Data ◽

10.4018/978-1-4666-9840-6.ch021 ◽

2016 ◽

pp. 418-440

Author(s):

Amir A. Khwaja

Keyword(s):

Big Data ◽

Data Processing ◽

Real Time ◽

Data Analytics ◽

Big Data Analytics ◽

Exception Handling ◽

Specification Language ◽

Big Data Processing ◽

Business Decisions ◽

Concurrent Processes

Big data explosion has already happened and the situation is only going to exacerbate with such a high number of data sources and high-end technology prevalent everywhere, generating data at a frantic pace. One of the most important aspects of big data is being able to capture, process, and analyze data as it is happening in real-time to allow real-time business decisions. Alternate approaches must be investigated especially consisting of highly parallel and real-time computations for big data processing. The chapter presents RealSpec real-time specification language that may be used for the modeling of big data analytics due to the inherent language features needed for real-time big data processing such as concurrent processes, multi-threading, resource modeling, timing constraints, and exception handling. The chapter provides an overview of RealSpec and applies the language to a detailed big data event recognition case study to demonstrate language applicability to big data framework and analytics modeling.

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Big Data Overview

Big Data ◽

10.4018/978-1-4666-9840-6.ch001 ◽

2016 ◽

pp. 1-29 ◽

Cited By ~ 3

Author(s):

Yushi Shen ◽

Yale Li ◽

Ling Wu ◽

Shaofeng Liu ◽

Qian Wen

Keyword(s):

Big Data ◽

Data Processing ◽

Data Analytics ◽

Big Data Analytics ◽

Big Data Processing ◽

Data Scientist ◽

Definition Of

This chapter provides an overview of big data and its environment and opportunities. It starts with a definition of big data and describes the unique characteristics, structure, and value of big data, and the business drivers for big data analytics. It defines the role of the data scientist and describes the new ecosystem for big data processing and analysis.

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Big data analytics with swarm intelligence

Industrial Management & Data Systems ◽

10.1108/imds-06-2015-0222 ◽

2016 ◽

Vol 116 (4) ◽

pp. 646-666 ◽

Cited By ~ 29

Author(s):

Shi Cheng ◽

Qingyu Zhang ◽

Quande Qin

Keyword(s):

Big Data ◽

Data Processing ◽

Swarm Intelligence ◽

Data Analytics ◽

Large Scale ◽

Big Data Analytics ◽

Underlying Mechanism ◽

Research Area ◽

Future Research ◽

Content Type

Purpose – The quality and quantity of data are vital for the effectiveness of problem solving. Nowadays, big data analytics, which require managing an immense amount of data rapidly, has attracted more and more attention. It is a new research area in the field of information processing techniques. It faces the big challenges and difficulties of a large amount of data, high dimensionality, and dynamical change of data. However, such issues might be addressed with the help from other research fields, e.g., swarm intelligence (SI), which is a collection of nature-inspired searching techniques. The paper aims to discuss these issues. Design/methodology/approach – In this paper, the potential application of SI in big data analytics is analyzed. The correspondence and association between big data analytics and SI techniques are discussed. As an example of the application of the SI algorithms in the big data processing, a commodity routing system in a port in China is introduced. Another example is the economic load dispatch problem in the planning of a modern power system. Findings – The characteristics of big data include volume, variety, velocity, veracity, and value. In the SI algorithms, these features can be, respectively, represented as large scale, high dimensions, dynamical, noise/surrogates, and fitness/objective problems, which have been effectively solved. Research limitations/implications – In current research, the example problem of the port is formulated but not solved yet given the ongoing nature of the project. The example could be understood as advanced IT or data processing technology, however, its underlying mechanism could be the SI algorithms. This paper is the first step in the research to utilize the SI algorithm to a big data analytics problem. The future research will compare the performance of the method and fit it in a dynamic real system. Originality/value – Based on the combination of SI and data mining techniques, the authors can have a better understanding of the big data analytics problems, and design more effective algorithms to solve real-world big data analytical problems.

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Big Data Overview

Advances in Systems Analysis, Software Engineering, and High Performance Computing - Enabling the New Era of Cloud Computing ◽

10.4018/978-1-4666-4801-2.ch008 ◽

2013 ◽

pp. 156-184 ◽

Cited By ~ 2

Author(s):

Yushi Shen ◽

Yale Li ◽

Ling Wu ◽

Shaofeng Liu ◽

Qian Wen

Keyword(s):

Big Data ◽

Data Processing ◽

Data Analytics ◽

Big Data Analytics ◽

Big Data Processing ◽

Data Scientist ◽

Definition Of

This chapter provides an overview of big data and its environment and opportunities. It starts with a definition of big data and describes the unique characteristics, structure, and value of big data, and the business drivers for big data analytics. It defines the role of the data scientist and describes the new ecosystem for big data processing and analysis.

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RESEARCH OF DISTRIBUTED VIRTUAL SOFTWARE-DEFINED INFRASTRUCTURE PERFORMANCE FOR BIG DATA PROCESSING TASKS

Vestnik of Ryazan State Radio Engineering University ◽

10.21667/1995-4565-2021-77-58-67 ◽

2021 ◽

Vol 77 ◽

pp. 58-67

Author(s):

Y. A. Ushakov ◽

Keyword(s):

Experimental Study ◽

Cloud Computing ◽

Big Data ◽

Data Processing ◽

Data Analytics ◽

Distributed Processing ◽

Big Data Analytics ◽

Work Efficiency ◽

Infrastructure Performance ◽

Rapid Deployment

Container-based virtual software-defined virtual infrastructures have become an integral part of the underlying cloud computing engine and are used in a variety of distributed, scalable, resilient systems. But big data analytics tasks are mostly handled by traditional distributed clusters that require initial deployment, careful upgrades, and skilled maintenance. The aim of the work is to study the effectiveness of using software- defined virtual infrastructures based on containers and methods for their rapid deployment according to cloud principles for the implementation of automation platforms for distributed processing of big data. The schemes of Hadoop and Spark architecture deployment based on Docker Swarm and Kubernetes clusters are given. The development of criteria for evaluating the performance of distributed computing for processing big data was also carried out and an experimental study of the work efficiency was carried out.

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Trends and Opportunities in Health Analytics as a Service and Implications for Use in Low Resource Settings: A Literature Review Abstract (Preprint)

10.2196/preprints.15737 ◽

2019 ◽

Author(s):

Meghana Bastwadkar ◽

Carolyn McGregor ◽

S Balaji

Keyword(s):

Data Mining ◽

Cloud Computing ◽

Big Data ◽

Intensive Care ◽

Literature Review ◽

Health Monitoring ◽

Data Analytics ◽

Neonatal Intensive Care ◽

Big Data Analytics ◽

Healthcare Facilities

BACKGROUND This paper presents a systematic literature review of existing remote health monitoring systems with special reference to neonatal intensive care (NICU). Articles on NICU clinical decision support systems (CDSSs) which used cloud computing and big data analytics were surveyed. OBJECTIVE The aim of this study is to review technologies used to provide NICU CDSS. The literature review highlights the gaps within frameworks providing HAaaS paradigm for big data analytics METHODS Literature searches were performed in Google Scholar, IEEE Digital Library, JMIR Medical Informatics, JMIR Human Factors and JMIR mHealth and only English articles published on and after 2015 were included. The overall search strategy was to retrieve articles that included terms that were related to “health analytics” and “as a service” or “internet of things” / ”IoT” and “neonatal intensive care unit” / ”NICU”. Title and abstracts were reviewed to assess relevance. RESULTS In total, 17 full papers met all criteria and were selected for full review. Results showed that in most cases bedside medical devices like pulse oximeters have been used as the sensor device. Results revealed a great diversity in data acquisition techniques used however in most cases the same physiological data (heart rate, respiratory rate, blood pressure, blood oxygen saturation) was acquired. Results obtained have shown that in most cases data analytics involved data mining classification techniques, fuzzy logic-NICU decision support systems (DSS) etc where as big data analytics involving Artemis cloud data analysis have used CRISP-TDM and STDM temporal data mining technique to support clinical research studies. In most scenarios both real-time and retrospective analytics have been performed. Results reveal that most of the research study has been performed within small and medium sized urban hospitals so there is wide scope for research within rural and remote hospitals with NICU set ups. Results have shown creating a HAaaS approach where data acquisition and data analytics are not tightly coupled remains an open research area. Reviewed articles have described architecture and base technologies for neonatal health monitoring with an IoT approach. CONCLUSIONS The current work supports implementation of the expanded Artemis cloud as a commercial offering to healthcare facilities in Canada and worldwide to provide cloud computing services to critical care. However, no work till date has been completed for low resource setting environment within healthcare facilities in India which results in scope for research. It is observed that all the big data analytics frameworks which have been reviewed in this study have tight coupling of components within the framework, so there is a need for a framework with functional decoupling of components.

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