Big Data and Its Visualization With Fog Computing

2020 ◽  
pp. 341-377
Author(s):  
Richard S. Segall ◽  
Gao Niu
Keyword(s):  
Big Data ◽  
Traffic Safety ◽  
New Technology ◽  
Fog Computing ◽  
Real Data ◽  
The United States ◽  
Data Sets ◽  
United States Department ◽  
Challenges And Opportunities ◽  
Continuous Stream

Big Data is data sets that are so voluminous and complex that traditional data processing application software are inadequate to deal with them. This article discusses what is Big Data, and its characteristics, and how this information revolution of Big Data is transforming our lives and the new technology and methodologies that have been developed to process data of these huge dimensionalities. Big Data can be discrete or a continuous stream of data, and can be accessed using many types and kinds of computing devices ranging from supercomputers, personal work stations, to mobile devices and tablets. Discussion is presented of how fog computing can be performed with cloud computing as a mechanism for visualization of Big Data. An example of visualization techniques for Big Data transmitted by devices connected by Internet of Things (IoT) is presented for real data from fatality analysis reporting system (FARS) managed by the National Highway Traffic Safety Administration (NHTSA) of the United States Department of Transportation (USDoT). Big Data web-based visualization software are discussed that are both JavaScript-based and user interface-based. Challenges and opportunities of using Big Data with fog computing are also discussed.

Overview of Big Data and Its Visualization

2022 ◽  
pp. 22-53
Author(s):  
Richard S. Segall ◽  
Gao Niu
Keyword(s):  
Big Data ◽  
Traffic Safety ◽  
Data Analytics ◽  
Big Data Analytics ◽  
Real Data ◽  
Flow Diagram ◽  
Data Sets ◽  
United States Department ◽  
Big Data Visualization ◽  
Challenges And Opportunities

Big Data is data sets that are so voluminous and complex that traditional data processing application software are inadequate to deal with them. This chapter discusses what Big Data is and its characteristics, and how this information revolution of Big Data is transforming our lives and the new technology and methodologies that have been developed to process data of these huge dimensionalities. This chapter discusses the components of the Big Data stack interface, categories of Big Data analytics software and platforms, descriptions of the top 20 Big Data analytics software. Big Data visualization techniques are discussed with real data from fatality analysis reporting system (FARS) managed by National Highway Traffic Safety Administration (NHTSA) of the United States Department of Transportation. Big Data web-based visualization software are discussed that are both JavaScript-based and user-interface-based. This chapter also discusses the challenges and opportunities of using Big Data and presents a flow diagram of the 30 chapters within this handbook.

Overview of Big Data and Its Visualization

2018 ◽  
pp. 1-32 ◽  
Author(s):  
Richard S. Segall ◽  
Gao Niu
Keyword(s):  
Big Data ◽  
Traffic Safety ◽  
Data Analytics ◽  
Big Data Analytics ◽  
Real Data ◽  
Flow Diagram ◽  
Data Sets ◽  
United States Department ◽  
Big Data Visualization ◽  
Challenges And Opportunities

Big Data is data sets that are so voluminous and complex that traditional data processing application software are inadequate to deal with them. This chapter discusses what Big Data is and its characteristics, and how this information revolution of Big Data is transforming our lives and the new technology and methodologies that have been developed to process data of these huge dimensionalities. This chapter discusses the components of the Big Data stack interface, categories of Big Data analytics software and platforms, descriptions of the top 20 Big Data analytics software. Big Data visualization techniques are discussed with real data from fatality analysis reporting system (FARS) managed by National Highway Traffic Safety Administration (NHTSA) of the United States Department of Transportation. Big Data web-based visualization software are discussed that are both JavaScript-based and user-interface-based. This chapter also discusses the challenges and opportunities of using Big Data and presents a flow diagram of the 30 chapters within this handbook.

Big Data and Its Visualization With Fog Computing

2018 ◽  
Vol 1 (2) ◽  
pp. 51-82 ◽  
Author(s):  
Richard S. Segall ◽  
Gao Niu
Keyword(s):  
Big Data ◽  
Traffic Safety ◽  
Fog Computing ◽  
Real Data ◽  
The United States ◽  
United States Department ◽  
Highway Traffic ◽  
Process Data ◽  
National Highway Traffic Safety ◽  
Processing Application

Big Data is data sets that are so voluminous and complex that traditional data processing application software are inadequate to deal with them. This article discusses what is Big Data, and its characteristics, and how this information revolution of Big Data is transforming our lives and the new technology and methodologies that have been developed to process data of these huge dimensionalities. Big Data can be discrete or a continuous stream of data, and can be accessed using many types and kinds of computing devices ranging from supercomputers, personal work stations, to mobile devices and tablets. Discussion is presented of how fog computing can be performed with cloud computing as a mechanism for visualization of Big Data. An example of visualization techniques for Big Data transmitted by devices connected by Internet of Things (IoT) is presented for real data from fatality analysis reporting system (FARS) managed by the National Highway Traffic Safety Administration (NHTSA) of the United States Department of Transportation (USDoT). Big Data web-based visualization software are discussed that are both JavaScript-based and user interface-based. Challenges and opportunities of using Big Data with fog computing are also discussed.

What Is Open Source Software (OSS) and What Is Big Data?

2021 ◽  
pp. 817-857
Author(s):  
Richard S. Segall
Keyword(s):  
Big Data ◽  
Open Source ◽  
Open Source Software ◽  
Fog Computing ◽  
Computer Software ◽  
Data Sets ◽  
Copyright Holder ◽  
Stream Data ◽  
Big Data Visualization ◽  
Continuous Stream

This chapter discusses what Open Source Software is and its relationship to Big Data and how it differs from other types of software and its software development cycle. Open source software (OSS) is a type of computer software in which source code is released under a license in which the copyright holder grants users the rights to study, change, and distribute the software to anyone and for any purpose. Big Data are data sets that are so voluminous and complex that traditional data processing application software are inadequate to deal with them. Big data can be discrete or a continuous stream data and is accessible using many types of computing devices ranging from supercomputers and personal workstations to mobile devices and tablets. It is discussed how fog computing can be performed with cloud computing for visualization of Big Data. This chapter also presents a summary of additional web-based Big Data visualization software.

What Is Open Source Software (OSS) and What Is Big Data?

2020 ◽  
pp. 1-49
Author(s):  
Richard S. Segall
Keyword(s):  
Big Data ◽  
Open Source ◽  
Open Source Software ◽  
Fog Computing ◽  
Computer Software ◽  
Data Sets ◽  
Copyright Holder ◽  
Stream Data ◽  
Big Data Visualization ◽  
Continuous Stream

This chapter discusses what Open Source Software is and its relationship to Big Data and how it differs from other types of software and its software development cycle. Open source software (OSS) is a type of computer software in which source code is released under a license in which the copyright holder grants users the rights to study, change, and distribute the software to anyone and for any purpose. Big Data are data sets that are so voluminous and complex that traditional data processing application software are inadequate to deal with them. Big data can be discrete or a continuous stream data and is accessible using many types of computing devices ranging from supercomputers and personal workstations to mobile devices and tablets. It is discussed how fog computing can be performed with cloud computing for visualization of Big Data. This chapter also presents a summary of additional web-based Big Data visualization software.

What Is Open Source Software (OSS) and What Is Big Data?

2022 ◽  
pp. 77-118
Author(s):  
Richard S. Segall
Keyword(s):  
Big Data ◽  
Open Source ◽  
Open Source Software ◽  
Fog Computing ◽  
Computer Software ◽  
Data Sets ◽  
Copyright Holder ◽  
Stream Data ◽  
Big Data Visualization ◽  
Continuous Stream

This chapter discusses what Open Source Software is and its relationship to Big Data and how it differs from other types of software and its software development cycle. Open source software (OSS) is a type of computer software in which source code is released under a license in which the copyright holder grants users the rights to study, change, and distribute the software to anyone and for any purpose. Big Data are data sets that are so voluminous and complex that traditional data processing application software are inadequate to deal with them. Big data can be discrete or a continuous stream data and is accessible using many types of computing devices ranging from supercomputers and personal workstations to mobile devices and tablets. It is discussed how fog computing can be performed with cloud computing for visualization of Big Data. This chapter also presents a summary of additional web-based Big Data visualization software.

scholarly journals Measuring the Effectiveness of Adaptive Random Forest for Handling Concept Drift in Big Data Streams

Entropy ◽  
2021 ◽  
Vol 23 (7) ◽  
pp. 859
Author(s):  
Abdulaziz O. AlQabbany ◽  
Aqil M. Azmi
Keyword(s):  
Big Data ◽  
Random Forest ◽  
Real Time ◽  
Data Streams ◽  
Learning Algorithm ◽  
Concept Drift ◽  
The United States ◽  
Careful Consideration ◽  
Data Sets ◽  
Stream Data

We are living in the age of big data, a majority of which is stream data. The real-time processing of this data requires careful consideration from different perspectives. Concept drift is a change in the data’s underlying distribution, a significant issue, especially when learning from data streams. It requires learners to be adaptive to dynamic changes. Random forest is an ensemble approach that is widely used in classical non-streaming settings of machine learning applications. At the same time, the Adaptive Random Forest (ARF) is a stream learning algorithm that showed promising results in terms of its accuracy and ability to deal with various types of drift. The incoming instances’ continuity allows for their binomial distribution to be approximated to a Poisson(1) distribution. In this study, we propose a mechanism to increase such streaming algorithms’ efficiency by focusing on resampling. Our measure, resampling effectiveness (ρ), fuses the two most essential aspects in online learning; accuracy and execution time. We use six different synthetic data sets, each having a different type of drift, to empirically select the parameter λ of the Poisson distribution that yields the best value for ρ. By comparing the standard ARF with its tuned variations, we show that ARF performance can be enhanced by tackling this important aspect. Finally, we present three case studies from different contexts to test our proposed enhancement method and demonstrate its effectiveness in processing large data sets: (a) Amazon customer reviews (written in English), (b) hotel reviews (in Arabic), and (c) real-time aspect-based sentiment analysis of COVID-19-related tweets in the United States during April 2020. Results indicate that our proposed method of enhancement exhibited considerable improvement in most of the situations.

Predictable policing: New technology, old bias, and future resistance in big data surveillance

2020 ◽  
Vol 26 (5-6) ◽  
pp. 1108-1124
Author(s):  
Xerxes Minocher ◽  
Caelyn Randall
Keyword(s):  
Big Data ◽  
Science And Technology Studies ◽  
New Technology ◽  
The United States ◽  
Surveillance Network ◽  
Predictive Policing ◽  
Sociotechnical System ◽  
Power And Control ◽  
Data Surveillance ◽  
And Control

Within this article, we explore the rise of predictive policing in the United States as a form of big data surveillance. Bringing together literature from communication, criminology, and science and technology studies, we use a case study of Milwaukee, Wisconsin, USA to outline that predictive policing, rather than being a novel development, is in fact part of a much larger, historical network of power and control. By examining the mechanics of these policing practices: the data inputs, behavioral outputs, as well as the key controllers of these systems, and the individuals who influenced their adoption, we show that predictive policing as a form of big data surveillance is a sociotechnical system that is wholly human-constructed, biases and all. Identifying these elements of the surveillance network then allows us to turn our attention to the resistive practices of communities who historically and presently live under surveillance – pointing to the types of actions and imaginaries required to combat the myth and allure that swirls around the rhetoric of big data surveillance today.

The New Dynamics of Organizational Change

2019 ◽  
pp. 129-149
Author(s):  
Matthew S. Weber
Keyword(s):  
Organizational Change ◽  
News Media ◽  
New Technology ◽  
Academic Research ◽  
Large Data ◽  
The United States ◽  
Data Sets ◽  
Media Industry ◽  
And Control

Organizational change is well established as an important area of academic research. Across disciplines, from communication to sociology to management, scholars have produced a wide array of notable works examining various facets of organizational change. In recent years, however, advances in methodology and access to large data sets have opened up innovative avenues of research. This chapter examines new dynamics of organizational change, illustrated through a case study of the evolution of the news media industry in the United States. Many processes of organizational change are still based on traditional theories and established dynamics, but extant research and the present case study demonstrate that new technology and emergent patterns of communication lead to the development of new dynamics for coordination and control of information flow as organizations change over time. The chapter concludes with guidance and future directions for this evolving domain of scholarship.

The Northeast Big Data Innovation Hub: "Enabling Seamless Data Sharing in Industry and Academia" Workshop Report

2017 ◽  
Author(s):  
Jane Greenberg ◽  
◽  
Samantha Grabus ◽  
Florence Hudson ◽  
Tim Kraska ◽  
...  
Keyword(s):  
Big Data ◽  
Data Sharing ◽  
Technology Policy ◽  
Data Sets ◽  
Real World Data ◽  
Academic Impact ◽  
Analysis Technique ◽  
Challenges And Opportunities ◽  
Technological Barriers ◽  
Access To Data

Increasingly, both industry and academia, in fields ranging from biology and social sciences to computing and engineering, are driven by data (Provost & Fawcett, 2013; Wixom, et al, 2014); and both commercial success and academic impact are dependent on having access to data. Many organizations collecting data lack the expertise required to process it (Hazen, et al, 2014), and, thus, pursue data sharing with researchers who can extract more value from data they own. For example, a biosciences company may benefit from a specific analysis technique a researcher has developed. At the same time, researchers are always on the search for real-world data sets to demonstrate the effectiveness of their methods. Unfortunately, many data sharing attempts fail, for reasons ranging from legal restrictions on how data can be used—to privacy policies, different cultural norms, and technological barriers. In fact, many data sharing partnerships that are vital to addressing pressing societal challenges in cities, health, energy, and the environment are not being pursued due to such obstacles. Addressing these data sharing challenges requires open, supportive dialogue across many sectors, including technology, policy, industry, and academia. Further, there is a crucial need for well-defined agreements that can be shared among key stakeholders, including researchers, technologists, legal representatives, and technology transfer officers. The Northeast Big Data Innovation Hub (NEBDIH) took an important step in this area with the recent "Enabling Seamless Data Sharing in Industry and Academia" workshop, held at Drexel University September 29-30, 2016. The workshop brought together representatives from these critical stakeholder communities to launch a national dialogue on challenges and opportunities in this complex space.

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