Large-scale data visualization using parallel data streaming

2001 ◽  
Vol 21 (4) ◽  
pp. 34-41 ◽  
Author(s):  
J. Ahrens ◽  
K. Brislawn ◽  
K. Martin ◽  
B. Geveci ◽  
C.C. Law ◽  
...  
Keyword(s):  
Data Visualization ◽  
Large Scale ◽  
Data Streaming ◽  
Large Scale Data ◽  
Parallel Data ◽  
Scale Data

Large-Scale Data Streaming in Fog Computing and Its Applications

2021 ◽  
pp. 50-65
Author(s):  
Oshin Sharma ◽  
Anusha S.
Keyword(s):  
Data Storage ◽  
Large Scale ◽  
Fog Computing ◽  
Data Streaming ◽  
Cloud Data ◽  
Large Scale Data ◽  
Emerging Trends ◽  
Iot Devices ◽  
Data Centres ◽  
Scale Data

The emerging trends in fog computing have increased the interests and focus in both industry and academia. Fog computing extends cloud computing facilities like the storage, networking, and computation towards the edge of networks wherein it offloads the cloud data centres and reduces the latency of providing services to the users. This paradigm is like cloud in terms of data, storage, application, and computation services, except with a fundamental difference: it is decentralized. Furthermore, these fog systems can process huge amounts of data locally and can be installed on hardware of different types. These characteristics make fog suitable for time- and location-based applications like internet of things (IoT) devices which can process large amounts of data. In this chapter, the authors present fog data streaming, its architecture, and various applications.

Visualization of Large-Scale Distributed Data

2012 ◽  
pp. 242-274
Author(s):  
Jason Leigh ◽  
Andrew Johnson ◽  
Luc Renambot ◽  
Venkatram Vishwanath ◽  
Tom Peterka ◽  
...  
Keyword(s):  
Distributed Computing ◽  
Data Visualization ◽  
High Speed ◽  
Large Scale ◽  
Distributed Data ◽  
Large Scale Data ◽  
Distribution Of Resources ◽  
Integrated Facility ◽  
Effective Visualization ◽  
Scale Data

An effective visualization is best achieved through the creation of a proper representation of data and the interactive manipulation and querying of the visualization. Large-scale data visualization is particularly challenging because the size of the data is several orders of magnitude larger than what can be managed on an average desktop computer. Large-scale data visualization therefore requires the use of distributed computing. By leveraging the widespread expansion of the Internet and other national and international high-speed network infrastructure such as the National LambdaRail, Internet-2, and the Global Lambda Integrated Facility, data and service providers began to migrate toward a model of widespread distribution of resources. This chapter introduces different instantiations of the visualization pipeline and the historic motivation for their creation. The authors examine individual components of the pipeline in detail to understand the technical challenges that must be solved in order to ensure continued scalability. They discuss distributed data management issues that are specifically relevant to large-scale visualization. They also introduce key data rendering techniques and explain through case studies approaches for scaling them by leveraging distributed computing. Lastly they describe advanced display technologies that are now considered the “lenses” for examining large-scale data.

scholarly journals A study on high dimensional large-scale data visualization

2016 ◽  
Vol 29 (6) ◽  
pp. 1061-1075
Author(s):  
Eun-Kyung Lee ◽  
Nayoung Hwang ◽  
Yoondong Lee
Keyword(s):  
Data Visualization ◽  
Large Scale ◽  
High Dimensional ◽  
Large Scale Data ◽  
Scale Data

3D-RadViz: Three Dimensional Radial Visualization for Large-Scale Data Visualization

2021 ◽  
Author(s):  
Abdelrahman Elewah ◽  
Abeer A. Badawi ◽  
Haytham Khalil ◽  
Shahryar Rahnamayan ◽  
Khalid Elgazzar
Keyword(s):  
Data Visualization ◽  
Large Scale ◽  
Three Dimensional ◽  
Large Scale Data ◽  
Radial Visualization ◽  
Scale Data

A Survey on MapReduce Implementations

2016 ◽  
Vol 6 (1) ◽  
pp. 59-87 ◽  
Author(s):  
Amer Al-Badarneh ◽  
Amr Mohammad ◽  
Salah Harb
Keyword(s):  
Large Scale ◽  
Programming Model ◽  
Data Sets ◽  
Mapreduce Framework ◽  
Large Scale Data ◽  
Parallel Data ◽  
Efficiency Performance ◽  
Scale Data ◽  
Large Scale Data Sets ◽  
Open Issues

A distinguished successful platform for parallel data processing MapReduce is attracting a significant momentum from both academia and industry as the volume of data to capture, transform, and analyse grows rapidly. Although MapReduce is used in many applications to analyse large scale data sets, there is still a lot of debate among scientists and researchers on its efficiency, performance, and usability to support more classes of applications. This survey presents a comprehensive review of various implementations of MapReduce framework. Initially the authors give an overview of MapReduce programming model. They then present a broad description of various technical aspects of the most successful implementations of MapReduce framework reported in the literature and discuss their main strengths and weaknesses. Finally, the authors conclude by introducing a comparison between MapReduce implementations and discuss open issues and challenges on enhancing MapReduce.

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