scholarly journals Research and implementation of big data visualization based on WebGIS

2019 ◽  
Vol 2 ◽  
pp. 1-6
Author(s):  
Wenjuan Lu ◽  
Aiguo Liu ◽  
Chengcheng Zhang
Keyword(s):  
Big Data ◽  
Data Visualization ◽  
Visual Analysis ◽  
Distributed Database ◽  
Research Problem ◽  
Research Area ◽  
Geographical Information ◽  
Data Set ◽  
Visualization Technology ◽  
Thematic Data

<p><strong>Abstract.</strong> With the development of geographic information technology, the way to get geographical information is constantly, and the data of space-time is exploding, and more and more scholars have started to develop a field of data processing and space and time analysis. In this, the traditional data visualization technology is high in popularity and simple and easy to understand, through simple pie chart and histogram, which can reveal and analyze the characteristics of the data itself, but still cannot combine with the map better to display the hidden time and space information to exert its application value. How to fully explore the spatiotemporal information contained in massive data and accurately explore the spatial distribution and variation rules of geographical things and phenomena is a key research problem at present. Based on this, this paper designed and constructed a universal thematic data visual analysis system that supports the full functions of data warehousing, data management, data analysis and data visualization. In this paper, Weifang city is taken as the research area, starting from the aspects of rainfall interpolation analysis and population comprehensive analysis of Weifang, etc., the author realizes the fast and efficient display under the big data set, and fully displays the characteristics of spatial and temporal data through the visualization effect of thematic data. At the same time, Cassandra distributed database is adopted in this research, which can also store, manage and analyze big data. To a certain extent, it reduces the pressure of front-end map drawing, and has good query analysis efficiency and fast processing ability.</p>

Visualization of Big Data Sets Using Computer Graphics

2018 ◽  
pp. 578-603
Author(s):  
Anna Ursyn ◽  
Edoardo L'Astorina
Keyword(s):  
Big Data ◽  
Data Visualization ◽  
Visual Analysis ◽  
Data Sets ◽  
Arrival Times ◽  
Learning And Teaching ◽  
Knowledge Domains ◽  
Visualization Of Data ◽  
And Mathematics ◽  
Visualization Techniques

This chapter discusses some possible ways of how professionals, researchers and users representing various knowledge domains are collecting and visualizing big data sets. First it describes communication through senses as a basis for visualization techniques, computational solutions for enhancing senses and ways of enhancing senses by technology. The next part discusses ideas behind visualization of data sets and ponders what is and what not visualization is. Further discussion relates to data visualization through art as visual solutions of science and mathematics related problems, documentation objects and events, and a testimony to thoughts, knowledge and meaning. Learning and teaching through data visualization is the concluding theme of the chapter. Edoardo L'Astorina provides visual analysis of best practices in visualization: An overlay of Google Maps that showed all the arrival times - in real time - of all the buses in your area based on your location and visual representation of all the Tweets in the world about TfL (Transport for London) tube lines to predict disruptions.

Research and Application of Responsive Big Data Visualization Based on HTML5

2014 ◽  
Vol 631-632 ◽  
pp. 1075-1079
Author(s):  
Pei Yang ◽  
Hai Yun Han
Keyword(s):  
Big Data ◽  
Data Visualization ◽  
Mobile Platform ◽  
Technology Research ◽  
Mass Data ◽  
Volume Velocity ◽  
Big Data Visualization ◽  
Visualization Technology ◽  
Special Points ◽  
Meaningful Data

Research the responsive visualization technology of big data based on HTML5. Big data has 4 special points: volume, velocity, variety and value. Our purpose is to mine the value of big data with the visualization technology. There are many platforms such as desktop and mobile platform, and each kind of device may have different resolution, based on HTML5, CSS3 and JavaScript technology, research the responsive visualization technology to fit all platforms, then we can mine meaningful data of the mass data, guide the development of related forecasting and strategy.

Statistical Visualization of Big Data Through Hadoop Streaming in RStudio

2022 ◽  
pp. 758-787
Author(s):  
Chitresh Verma ◽  
Rajiv Pandey
Keyword(s):  
Big Data ◽  
Data Visualization ◽  
Data Analytics ◽  
Big Data Analytics ◽  
Data Streaming ◽  
Data Set ◽  
Graphical Modeling ◽  
Hadoop Mapreduce ◽  
R Packages ◽  
Case Based

Data Visualization enables visual representation of the data set for interpretation of data in a meaningful manner from human perspective. The Statistical visualization calls for various tools, algorithms and techniques that can support and render graphical modeling. This chapter shall explore on the detailed features R and RStudio. The combination of Hadoop and R for the Big Data Analytics and its data visualization shall be demonstrated through appropriate code snippets. The integration perspective of R and Hadoop is explained in detail with the help of a utility called Hadoop streaming jar. The various R packages and their integration with Hadoop operations in the R environment are explained through suitable examples. The process of data streaming is provided using different readers of Hadoop streaming package. A case based statistical project is considered in which the data set is visualized after dual execution using the Hadoop MapReduce and R script.

Statistical Visualization of Big Data Through Hadoop Streaming in RStudio

2018 ◽  
pp. 549-577
Author(s):  
Chitresh Verma ◽  
Rajiv Pandey
Keyword(s):  
Big Data ◽  
Data Visualization ◽  
Data Analytics ◽  
Big Data Analytics ◽  
Data Streaming ◽  
Data Set ◽  
Graphical Modeling ◽  
Hadoop Mapreduce ◽  
R Packages ◽  
Case Based

Data Visualization enables visual representation of the data set for interpretation of data in a meaningful manner from human perspective. The Statistical visualization calls for various tools, algorithms and techniques that can support and render graphical modeling. This chapter shall explore on the detailed features R and RStudio. The combination of Hadoop and R for the Big Data Analytics and its data visualization shall be demonstrated through appropriate code snippets. The integration perspective of R and Hadoop is explained in detail with the help of a utility called Hadoop streaming jar. The various R packages and their integration with Hadoop operations in the R environment are explained through suitable examples. The process of data streaming is provided using different readers of Hadoop streaming package. A case based statistical project is considered in which the data set is visualized after dual execution using the Hadoop MapReduce and R script.

Application of Geographical Information System and Interactive Data Visualization in Healthcare Decision Making

2016 ◽  
Vol 1 (1) ◽  
pp. 49-58 ◽  
Author(s):  
Zhecheng Zhu
Keyword(s):  
Decision Making ◽  
Information System ◽  
Data Visualization ◽  
Spatial Information ◽  
Real Life ◽  
Geographical Information ◽  
Data Set ◽  
Interactive Data ◽  
Visual Impact ◽  
Visualization Techniques

This paper focuses on two techniques and their applications in healthcare systems: geographic information system (GIS) and interactive data visualization. GIS is a type of technique applied to manipulate, analyze and display spatial information. It is a useful tool tackling location related problems. GIS applications in healthcare include evaluation of accessibility to healthcare facilities, site planning of new healthcare services and analysis of risks and spreads of infectious diseases. Interactive data visualization is a collection of techniques translating data from its numeric format to graphic presentation dynamically for easy understanding and visual impact. Compared to conventional static data visualization techniques, interactive data visualization techniques allow user to self-explore the entire data set by instant slice and dice, quick switching among multiple data sources. Adjustable granularity of interactive data visualization allows for both detailed micro information and aggregated macro information displayed in a single chart. Animated transition adds extra visual impact that describes how system transits from one state to another. When applied to healthcare system, interactive visualization techniques are useful in areas such as information integration, flow or trajectory presentation and location related visualization, etc. One area both techniques intersect is location analysis. In this paper, real life case studies will be given to illustrate how these two techniques, when combined together, help in solving quantitative or qualitative location related problem, visualizing geographical information and accelerating decision making procedures.

scholarly journals Noval Stream Data Mining Framework under the Background of Big Data

2016 ◽  
Vol 16 (5) ◽  
pp. 69-77 ◽  
Author(s):  
Wenquan Yi ◽  
Fei Teng ◽  
Jianfeng Xu
Keyword(s):  
Data Mining ◽  
Big Data ◽  
Research Area ◽  
Stream Data ◽  
Data Set ◽  
Stream Data Mining ◽  
Big Data Applications ◽  
Clustering And Classification ◽  
Mining Methods ◽  
New Research

Abstract Stream data mining has been a hot topic for research in the data mining research area in recent years, as it has an extensive application prospect in big data ages. Research on stream data mining mainly focuses on frequent item sets mining, clustering and classification. However, traditional steam data mining methods are not effective enough for handling high dimensional data set because these methods are not fit for the characteristics of stream data. So, these traditional stream data mining methods need to be enhanced for big data applications. To resolve this issue, a hybrid framework is proposed for big steam data mining. In this framework, online and offline model are organized for different tasks, the interior of each model is rationally organized according to different mining tasks. This framework provides a new research idea and macro perspective for stream data mining under the background of big data.

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