Visual Analytics Need, Process, Scope, Tools and Techniques and Challenges

2018 ◽  
Vol 6 (10) ◽  
pp. 153-158
Author(s):  
R. Shankar ◽  
S. Duraisamy
Keyword(s):  
Visual Analytics ◽  
Tools And Techniques

Making the Unseen Visible

2014 ◽  
pp. 277-331
Keyword(s):  
Visual Analytics ◽  
Web Search ◽  
Visual Learning ◽  
Network Visualization ◽  
Knowledge Visualization ◽  
Knowledge Maps ◽  
Tag Cloud ◽  
Visualization Techniques ◽  
Tools And Techniques

Themes and examples examined in this chapter discuss the fast growing field of visualization. First, basic terms: data, information, knowledge, dimensions, and variables are discussed before going into the visualization issues. The next part of the text overviews some of the basics in visualization techniques: data-, information-, and knowledge-visualization, and tells about tools and techniques used in visualization such as data mining, clusters and biclustering, concept mapping, knowledge maps, network visualization, Web-search result visualization, open source intelligence, visualization of the Semantic Web, visual analytics, and tag cloud visualization. This is followed by some remarks on music visualization. The next part of the chapter is about the meaning and the role of visualization in various kinds of presentations. Discussion relates to concept visualization in visual learning, visualization in education, collaborative visualization, professions that employ visualization skills, and well-known examples of visualization that progress science. Comments on cultural heritage knowledge visualization conclude the chapter.

scholarly journals Interfaces to Scripting Languages in Visual Analytics Applications

2020 ◽  
Author(s):  
Johanna Schmidt
Keyword(s):  
Visual Analytics ◽  
Data Science ◽  
Visual Analysis ◽  
Future Research ◽  
Research Directions ◽  
Scripting Languages ◽  
Daily Work ◽  
Future Research Directions ◽  
Tools And Techniques ◽  
Levels Of Integration

The need to use data visualization and visual analysis in various fields has led to the development of feature-rich standalone applications such as Tableau and MS Power BI. These applications provide ready-to-use functionality for loading, analyzing and visualizing data, even for users who are not familiar with programming and scripting. Meanwhile, data scientists have to combine many different tools and techniques in their daily work, since no standalone application can yet cover the entire workflow. As a result, a rich landscape of open source libraries is available today, covering various tasks from data analysis to modeling and visualization. To combine the best of two worlds, interfaces for scripting languages have been integrated into standalone applications in recent years. We analyzed which interfaces to six common scripting languages are offered. The interfaces offer different levels of integration and therefore support different steps of the data science workflow. In this paper we investigated the integration levels of script languages in standalone applications and divided them into four groups. We used this classification to evaluate 13 standalone visual analysis applications currently available on the market. We then analyzed which groups of applications best support which steps in the data science workflow. We found that a tight integration of scripting languages can especially support the explorative analysis and modeling phase of the data science workflow. We also discuss our results in the light of visual analysis research and give suggestions for future research directions.

The VAST Challenge: history, scope, and outcomes: An introduction to the Special Issue

2013 ◽  
Vol 13 (4) ◽  
pp. 301-312 ◽  
Author(s):  
Kristin Cook ◽  
Georges Grinstein ◽  
Mark Whiting
Keyword(s):  
Cyber Security ◽  
Visual Analytics ◽  
Computer Network ◽  
Coast Guard ◽  
Special Issue ◽  
Rfid Tag ◽  
Large Numbers ◽  
History Of ◽  
Tools And Techniques ◽  
Computer Network Attacks

The annual Visual Analytics Science and Technology (VAST) challenge provides Visual Analytics researchers, developers, and designers an opportunity to apply their best tools and techniques against invented problems that include a realistic scenario, data, tasks, and questions to be answered. Submissions are processed much like conference papers, contestants are provided reviewer feedback, and excellence is recognized with awards. A day-long VAST Challenge workshop takes place each year at the IEEE VAST conference to share results and recognize outstanding submissions. Short papers are published each year in the annual VAST proceedings. Over the history of the challenge, participants have investigated a wide variety of scenarios, such as bioterrorism, epidemics, arms smuggling, social unrest, and computer network attacks, among many others. Contestants have been provided with large numbers of realistic but synthetic Coast Guard interdiction records, intelligence reports, hospitalization records, microblog records, personal RFID tag locations, huge amounts of cyber security log data, and several hours of video. This paper describes the process for developing the synthetic VAST Challenge datasets and conducting the annual challenges. This paper also provides an introduction to this special issue of Information Visualization, focusing on the impacts of the VAST Challenge.

A Review on Big Data Analytics in Business

2018 ◽  
pp. 210-214
Author(s):  
Manbir Sandhu ◽  
Purnima, Anuradha Saini
Keyword(s):  
Big Data ◽  
Big Data Analytics ◽  
Smart Devices ◽  
Data Streaming ◽  
Huge Amount ◽  
Business Units ◽  
Enormous Amount ◽  
Tools And Techniques ◽  
Heath Care ◽  
Data Analytic

Big data is a fast-growing technology that has the scope to mine huge amount of data to be used in various analytic applications. With large amount of data streaming in from a myriad of sources: social media, online transactions and ubiquity of smart devices, Big Data is practically garnering attention across all stakeholders from academics, banking, government, heath care, manufacturing and retail. Big Data refers to an enormous amount of data generated from disparate sources along with data analytic techniques to examine this voluminous data for predictive trends and patterns, to exploit new growth opportunities, to gain insight, to make informed decisions and optimize processes. Data-driven decision making is the essence of business establishments. The explosive growth of data is steering the business units to tap the potential of Big Data to achieve fueling growth and to achieve a cutting edge over their competitors. The overwhelming generation of data brings with it, its share of concerns. This paper discusses the concept of Big Data, its characteristics, the tools and techniques deployed by organizations to harness the power of Big Data and the daunting issues that hinder the adoption of Business Intelligence in Big Data strategies in organizations.

Strategies, Tools, and Techniques for Navigating the Crisis

2004 ◽  
Vol 2004 (6) ◽  
pp. 65-71
Author(s):  
Rex W. Holsapple
Keyword(s):  
Tools And Techniques
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