Analysis and Visualization of Data Assimilating Hive and COGNOS Insight 10.2.2

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 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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ScaDS Dresden/Leipzig – A competence center for collaborative big data research

it - Information Technology ◽

10.1515/itit-2018-0026 ◽

2018 ◽

Vol 60 (5-6) ◽

pp. 327-333 ◽

Cited By ~ 1

Author(s):

René Jäkel ◽

Eric Peukert ◽

Wolfgang E. Nagel ◽

Erhard Rahm

Keyword(s):

Big Data ◽

Heterogeneous Data ◽

Data Sets ◽

Data Intensive ◽

Innovative Methods ◽

Huge Data ◽

Wide Range ◽

Resource Requirements ◽

Visualization Of Data ◽

Data Intensive Applications

Abstract The efficient and intelligent handling of large, often distributed and heterogeneous data sets increasingly determines the scientific and economic competitiveness in most application areas. Mobile applications, social networks, multimedia collections, sensor networks, data intense scientific experiments, and complex simulations nowadays generate a huge data deluge. Nonetheless, processing and analyzing these data sets with innovative methods open up new opportunities for its exploitation and new insights. Nevertheless, the resulting resource requirements exceed usually the possibilities of state-of-the-art methods for the acquisition, integration, analysis and visualization of data and are summarized under the term big data. ScaDS Dresden/Leipzig, as one Germany-wide competence center for collaborative big data research, bundles efforts to realize data-intensive applications for a wide range of applications in science and industry. In this article, we present the basic concept of the competence center and give insights in some of its research topics.

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Big Data and Shipping-managing vessel performance

JOIV International Journal on Informatics Visualization ◽

10.30630/joiv.2.2.116 ◽

2018 ◽

Vol 2 (2) ◽

pp. 73

Author(s):

Mandeep Virk ◽

Vaishali Chauhan

Keyword(s):

Machine Learning ◽

Decision Making ◽

Big Data ◽

Fault Tolerance ◽

High Rate ◽

Shipping Industry ◽

Large Sets ◽

Huge Data ◽

Reliable Performance ◽

Increasing Demand

Shipping business is staggering the trade by a substantial number which portrays the usage of leading technologies to deliver formative and reliable performance to deal with the increasing demand. Technologies like AIS, machine learning, and IoT are making a shift in shipping industry by introducing robots and more sensor equipped devices. The hitch big data originates as a technology which is proficient for assembling and transforming the colossal and divergent figures of data providing organizations with meaningful insights for better decision-making. The size of data is increasing at a higher rate because of the procreation of peripatitic gadgets and sensors attached. Big data is accustomed to delineate technologies and techniques which are used to store, manage, distribute and analyze huge data sheets with a high rate of data occurrence. This gigantic data is allowing to terminate the business by developing meaningful and valuable insights by processing the data. Hadoop is the fundamental basic for composing big data and furnishes with convenient judgments through analysis. It enables the processing of large sets of data by providing a higher degree of fault-tolerance. Parallelism is adapted to process big size of data in the efficient and inexpensive way. Contending massive bulk of data is a determined and vigorous assignment that needs an enormous crunching armature to guaranty affluent data processing and analysis.

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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 in IoT

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.ch015 ◽

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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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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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 Science and EXASOL as Big Data Analytics tool

International Journal of Innovative Technology and Exploring Engineering - Special Issue ◽

10.35940/ijitee.i1151.0789s19 ◽

2019 ◽

Vol 8 (9S) ◽

pp. 933-937

Keyword(s):

Big Data ◽

Data Analysis ◽

Data Analytics ◽

Data Science ◽

Big Data Analytics ◽

System Architectures ◽

Huge Data ◽

Exploratory Approach ◽

Data Scientist ◽

Using Data

Big data and Data science are the two top trends of recent years. Both can be combined together as big data science. This leads to the demand for new system architectures which facilitates the development of processes which can handle huge data volumes without deterring the agility, flexibility and the interactive feel which suits the exploratory approach of a data scientist. Businesses today have found ways of using data as the principal factor for value generation. These data-driven businesses apply a variety of data tools as data analysis is one of the chief elements in this process. In order to raise data science to the new computational level that is required to meet the challenges of big data and interactive advanced analytics, EXASOL has introduced a new technological approach. This tool enables us more effective and easy data analysis.

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Big Data Analysis in IoT

The Internet of Things ◽

10.4018/978-1-5225-1832-7.ch018 ◽

2017 ◽

pp. 383-397

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.

Download Full-text