NetTimeView: Applying Spatio-temporal Data Visualization Techniques to DDoS Attack Analysis

2014 ◽  
pp. 357-366 ◽  
Author(s):  
Ayush Shrestha ◽  
Ying Zhu ◽  
Kebina Manandhar
Keyword(s):  
Data Visualization ◽  
Temporal Data ◽  
Ddos Attack ◽  
Spatio Temporal ◽  
Visualization Techniques

Building a Visual Analytics Tool for Location-Based Services

2016 ◽  
pp. 620-642 ◽  
Author(s):  
Erdem Kaya ◽  
Mustafa Tolga Eren ◽  
Candemir Doger ◽  
Selim Saffet Balcisoy
Keyword(s):  
Visual Analytics ◽  
3D Visualization ◽  
Location Based Services ◽  
Task Completion ◽  
Temporal Data ◽  
Density Map ◽  
Spatio Temporal ◽  
Visualization Techniques ◽  
Analytical Tools ◽  
2D And 3D

Conventional visualization techniques and tools may need to be modified and tailored for analysis purposes when the data is spatio-temporal. However, there could be a number of pitfalls for the design of such analysis tools that completely rely on the well-known techniques with well-known limitations possibly due to the multidimensionality of spatio-temporal data. In this chapter, an experimental study to empirically testify whether widely accepted advantages and limitations of 2D and 3D representations are valid for the spatio-temporal data visualization is presented. The authors implemented two simple representations, namely density map and density cube, and conducted a laboratory experiment to compare these techniques from task completion time and correctness perspectives. Results of the experiment revealed that the validity of the generally accepted properties of 2D and 3D visualization needs to be reconsidered when designing analytical tools to analyze spatio-temporal data.

Data Analysis through Visualization

2021 ◽  
pp. 107-132
Author(s):  
Magy Seif El-Nasr ◽  
Truong Huy Nguyen Dinh ◽  
Alessandro Canossa ◽  
Anders Drachen
Keyword(s):  
Data Analysis ◽  
Data Science ◽  
University Of California ◽  
Science Class ◽  
Temporal Data ◽  
Santa Cruz ◽  
State Action ◽  
Spatio Temporal ◽  
Visualization Systems ◽  
Visualization Techniques

This chapter discusses the topic of how one can use visualization techniques to analyze game data. Specifically, the chapter delves into the development of heatmaps to analyze spatio-temporal data. The chapter also discusses spatio-temporal visualizations and state-action transition visualizations. We also discuss two visualization systems that we have developed within the GUII lab: Stratmapper and Glyph. We provide you with a link that allows you to explore the use of these visualizations with real game data. This chapter is written in collaboration with Riddhi Padte and Varun Sriram, based on their work in Dr. Seif El-Nasr’s game data science class at Northeastern University; Erica Kleinman, PhD student at University of California at Santa Cruz; and Andy Bryant, software engineer at GUII Lab. The chapter also includes labs where you get to experience the analysis of game data through visualization.

Building a Visual Analytics Tool for Location-Based Services

Big Data ◽  
2016 ◽  
pp. 615-637
Author(s):  
Erdem Kaya ◽  
Mustafa Tolga Eren ◽  
Candemir Doger ◽  
Selim Saffet Balcisoy
Keyword(s):  
Visual Analytics ◽  
3D Visualization ◽  
Location Based Services ◽  
Task Completion ◽  
Temporal Data ◽  
Density Map ◽  
Spatio Temporal ◽  
Visualization Techniques ◽  
Analytical Tools ◽  
2D And 3D

Conventional visualization techniques and tools may need to be modified and tailored for analysis purposes when the data is spatio-temporal. However, there could be a number of pitfalls for the design of such analysis tools that completely rely on the well-known techniques with well-known limitations possibly due to the multidimensionality of spatio-temporal data. In this chapter, an experimental study to empirically testify whether widely accepted advantages and limitations of 2D and 3D representations are valid for the spatio-temporal data visualization is presented. The authors implemented two simple representations, namely density map and density cube, and conducted a laboratory experiment to compare these techniques from task completion time and correctness perspectives. Results of the experiment revealed that the validity of the generally accepted properties of 2D and 3D visualization needs to be reconsidered when designing analytical tools to analyze spatio-temporal data.

Building a Visual Analytics Tool for Location-Based Services

2015 ◽  
pp. 150-180
Author(s):  
Erdem Kaya ◽  
Mustafa Tolga Eren ◽  
Candemir Doger ◽  
Selim Saffet Balcisoy
Keyword(s):  
Visual Analytics ◽  
3D Visualization ◽  
Location Based Services ◽  
Task Completion ◽  
Temporal Data ◽  
Density Map ◽  
Spatio Temporal ◽  
Visualization Techniques ◽  
Analytical Tools ◽  
2D And 3D

Conventional visualization techniques and tools may need to be modified and tailored for analysis purposes when the data is spatio-temporal. However, there could be a number of pitfalls for the design of such analysis tools that completely rely on the well-known techniques with well-known limitations possibly due to the multidimensionality of spatio-temporal data. In this chapter, an experimental study to empirically testify whether widely accepted advantages and limitations of 2D and 3D representations are valid for the spatio-temporal data visualization is presented. The authors implemented two simple representations, namely density map and density cube, and conducted a laboratory experiment to compare these techniques from task completion time and correctness perspectives. Results of the experiment revealed that the validity of the generally accepted properties of 2D and 3D visualization needs to be reconsidered when designing analytical tools to analyze spatio-temporal data.

Outlier highlighting for spatio-temporal data visualization

2013 ◽  
Vol 40 (3) ◽  
pp. 165-171 ◽  
Author(s):  
Ulla Pyysalo ◽  
Juha Oksanen
Keyword(s):  
Data Visualization ◽  
Temporal Data ◽  
Spatio Temporal
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