Big Data Analysis in IoT

2015 ◽

pp. 313-327

Author(s):

Aqeel-ur Rehman ◽

Rafi Ullah ◽

Faisal Abdullah

Keyword(s):

Big Data ◽

Data Analysis ◽

Data Management ◽

Big Data Analysis ◽

Data Generation ◽

New Techniques ◽

Huge Data ◽

Data Rates ◽

Huge Challenge

In IoT, data management is a big problem due to the connectivity of billions of devices, objects, processes generating big data. Since the Things are not following any specific (common) standard, so analysis of such data becomes a big challenge. There is a need to elaborate about the characteristics of IoT based data to find out the available and applicable solutions. Such kind of study also directs to realize the need of new techniques to cope up with such challenges. Due to the heterogeneity of connected nodes, different data rates and formats it is getting a huge challenge to deal with such variety of data. As IoT is providing processing nodes in quantity in form of smart nodes, it is presenting itself a good platform for big data analysis. In this chapter, characteristics of big data and requirements for big data analysis are highlighted. Considering the big source of data generation as well as the plausible suitable platform of such huge data analysis, the associated challenges are also underlined.

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Big Data Analysis and Implementation in Different Areas Using IoT

International Journal of Hyperconnectivity and the Internet of Things ◽

10.4018/ijhiot.2017070102 ◽

2017 ◽

Vol 1 (2) ◽

pp. 12-25

Author(s):

Aqeel ur Rehman ◽

Muhammad Fahad ◽

Rafi Ullah ◽

Faisal Abdullah

Keyword(s):

Data Mining ◽

Big Data ◽

Data Analysis ◽

Big Data Analysis ◽

Healthcare Applications ◽

New Techniques ◽

Standard Data ◽

Huge Data ◽

Data Rates ◽

Definition Of

This article describes how in IoT, data management is a major issue because of communication among billions of electronic devices, which generate the huge dataset. Due to the unavailability of any standard, data analysis on such a large amount of data is a complex task. There should be a definition of IoT-based data to find out what is available and its applicable solutions. Such a study also directs the need for new techniques to cope up with such challenges. Due to the heterogeneity of connected nodes, different data rates, and formats, it is a huge challenge to deal with such a variety of data. As IoT is providing processing nodes in the form of smart nodes; it is presenting a good platform to support the big data study. In this article, the characteristics of data mining requirements for data mining analysis are highlighted. The associated challenges of facts generation, as well as the plausible suitable platform of such huge data analysis is also underlined. The application of IoT to support big data analysis in healthcare applications is also presented.

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Big Data Analysis and Implementation in Different Areas Using IoT

Securing the Internet of Things ◽

10.4018/978-1-5225-9866-4.ch049 ◽

2020 ◽

pp. 1096-1111

Author(s):

Aqeel ur Rehman ◽

Muhammad Fahad ◽

Rafi Ullah ◽

Faisal Abdullah

Keyword(s):

Data Mining ◽

Big Data ◽

Data Analysis ◽

Big Data Analysis ◽

Healthcare Applications ◽

Standard Data ◽

Huge Data ◽

Data Rates ◽

Definition Of ◽

Huge Challenge

This article describes how in IoT, data management is a major issue because of communication among billions of electronic devices, which generate the huge dataset. Due to the unavailability of any standard, data analysis on such a large amount of data is a complex task. There should be a definition of IoT-based data to find out what is available and its applicable solutions. Such a study also directs the need for new techniques to cope up with such challenges. Due to the heterogeneity of connected nodes, different data rates, and formats, it is a huge challenge to deal with such a variety of data. As IoT is providing processing nodes in the form of smart nodes; it is presenting a good platform to support the big data study. In this article, the characteristics of data mining requirements for data mining analysis are highlighted. The associated challenges of facts generation, as well as the plausible suitable platform of such huge data analysis is also underlined. The application of IoT to support big data analysis in healthcare applications is also presented.

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Big Data Analysis Techniques for Visualization of Genomics in Medicinal Plants

Biotechnology ◽

10.4018/978-1-5225-8903-7.ch032 ◽

2019 ◽

pp. 804-837

Author(s):

Hithesh Kumar ◽

Vivek Chandramohan ◽

Smrithy M. Simon ◽

Rahul Yadav ◽

Shashi Kumar

Keyword(s):

Big Data ◽

Data Analysis ◽

Next Generation Sequencing ◽

Medicinal Plants ◽

Big Data Analysis ◽

Next Generation ◽

Genome Data ◽

Huge Data ◽

Transcriptomics Data ◽

Generation Sequencing

In this chapter, the complete overview and application of Big Data analysis in the field of health care industries, Clinical Informatics, Personalized Medicine and Bioinformatics is provided. The major tools and databases used for the Big Data analysis are discussed in this chapter. The development of sequencing machines has led to the fast and effective ways of generating DNA, RNA, Whole Genome data, Transcriptomics data, etc. available in our hands in just a matter of hours. The complete Next Generation Sequencing (NGS) huge data analysis work flow for the medicinal plants are discussed in the chapter. This chapter serves as an introduction to the big data analysis in Next Generation Sequencing and concludes with a summary of the topics of the remaining chapters of this book.

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Big Data Analysis Techniques for Visualization of Genomics in Medicinal Plants

Advances in Data Mining and Database Management - Handbook of Research on Big Data Storage and Visualization Techniques ◽

10.4018/978-1-5225-3142-5.ch026 ◽

2018 ◽

pp. 749-781

Author(s):

Hithesh Kumar ◽

Vivek Chandramohan ◽

Smrithy M. Simon ◽

Rahul Yadav ◽

Shashi Kumar

Keyword(s):

Big Data ◽

Data Analysis ◽

Next Generation Sequencing ◽

Medicinal Plants ◽

Big Data Analysis ◽

Next Generation ◽

Genome Data ◽

Huge Data ◽

Transcriptomics Data ◽

Generation Sequencing

In this chapter, the complete overview and application of Big Data analysis in the field of health care industries, Clinical Informatics, Personalized Medicine and Bioinformatics is provided. The major tools and databases used for the Big Data analysis are discussed in this chapter. The development of sequencing machines has led to the fast and effective ways of generating DNA, RNA, Whole Genome data, Transcriptomics data, etc. available in our hands in just a matter of hours. The complete Next Generation Sequencing (NGS) huge data analysis work flow for the medicinal plants are discussed in the chapter. This chapter serves as an introduction to the big data analysis in Next Generation Sequencing and concludes with a summary of the topics of the remaining chapters of this book.

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Big Data Analysis

Effective Big Data Management and Opportunities for Implementation - Advances in Data Mining and Database Management ◽

10.4018/978-1-5225-0182-4.ch006 ◽

2016 ◽

pp. 83-93

Author(s):

Rajanala Vijaya Prakash

Keyword(s):

Big Data ◽

Data Analysis ◽

Data Management ◽

Data Base ◽

Big Data Analysis ◽

Full Potential ◽

New Class ◽

Base Management ◽

Relational Data Base

The data management industry has matured over the last three decades, primarily based on Relational Data Base Management Systems (RDBMS) technology. The amount of data collected and analyzed in enterprises has increased several folds in volume, variety and velocity of generation and consumption, organizations have started struggling with architectural limitations of traditional RDBMS architecture. As a result a new class of systems had to be designed and implemented, giving rise to the new phenomenon of “Big Data”. The data-driven world has the potential to improve the efficiencies of enterprises and improve the quality of our lives. There are a number of challenges that must be addressed to allow us to exploit the full potential of Big Data. This article highlights the key technical challenges of Big Data.

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Data Management Optimization in a Real-Time Big Data Analysis System for Intensive Care

Communications in Computer and Information Science - Cloud Computing, Big Data & Emerging Topics ◽

10.1007/978-3-030-61218-4_7 ◽

2020 ◽

pp. 93-107

Author(s):

Rodrigo Cañibano ◽

Claudia Rozas ◽

Cristina Orlandi ◽

Javier Balladini

Keyword(s):

Big Data ◽

Intensive Care ◽

Data Analysis ◽

Data Management ◽

Real Time ◽

Big Data Analysis ◽

Management Optimization ◽

Analysis System ◽

Data Analysis System

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On the Effectiveness of Hybrid Canopy With Hoeffding Adaptive Naive Bayes Trees

Web Services ◽

10.4018/978-1-5225-7501-6.ch043 ◽

2019 ◽

pp. 788-802

Author(s):

Mrutyunjaya Panda

Keyword(s):

Big Data ◽

Data Analysis ◽

Large Scale ◽

Big Data Analysis ◽

Data Sets ◽

Large Scale Data ◽

Huge Data ◽

With Memory ◽

Memory Constraints ◽

Scale Data

The Big Data, due to its complicated and diverse nature, poses a lot of challenges for extracting meaningful observations. This sought smart and efficient algorithms that can deal with computational complexity along with memory constraints out of their iterative behavior. This issue may be solved by using parallel computing techniques, where a single machine or a multiple machine can perform the work simultaneously, dividing the problem into sub problems and assigning some private memory to each sub problems. Clustering analysis are found to be useful in handling such a huge data in the recent past. Even though, there are many investigations in Big data analysis are on, still, to solve this issue, Canopy and K-Means++ clustering are used for processing the large-scale data in shorter amount of time with no memory constraints. In order to find the suitability of the approach, several data sets are considered ranging from small to very large ones having diverse filed of applications. The experimental results opine that the proposed approach is fast and accurate.

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