A Survey of Visual Analytics Techniques and Applications: State-of-the-Art Research and Future Challenges

Sun, Guodao; Wu, Yingcai; Liang, Ronghua; Liu, Shixia

doi:10.1007/s11390-013-1383-8

Cited by 182 publications

(102 citation statements)

References 120 publications

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“…While methods have also been proposed for speeding up selection queries over spatio-temporal data [17,70], these do not support interactive rates for aggregate queries, that slice and summarize the data in different ways, as required by visual analytics systems [4,20,44,51,58,67]. Motivating Application: Visual Exploration of Urban Data Sets.…”

Section: Introductionmentioning

confidence: 99%

GPU rasterization for real-time spatial aggregation over arbitrary polygons

et al. 2017

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Visual exploration of spatial data relies heavily on spatial aggregation queries that slice and summarize the data over different regions. These queries comprise computationally-intensive point-inpolygon tests that associate data points to polygonal regions, challenging the responsiveness of visualization tools. This challenge is compounded by the sheer amounts of data, requiring a large number of such tests to be performed. Traditional pre-aggregation approaches are unsuitable in this setting since they fix the query constraints and support only rectangular regions. On the other hand, query constraints are defined interactively in visual analytics systems, and polygons can be of arbitrary shapes. In this paper, we convert a spatial aggregation query into a set of drawing operations on a canvas and leverage the rendering pipeline of the graphics hardware (GPU) to enable interactive response times. Our technique trades-off accuracy for response time by adjusting the canvas resolution, and can even provide accurate results when combined with a polygon index. We evaluate our technique on two large real-world data sets, exhibiting superior performance compared to index-based approaches.

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Section: Introductionmentioning

confidence: 99%

GPU rasterization for real-time spatial aggregation over arbitrary polygons

et al. 2017

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“…Many works confirm that the involvement of the human factor in the process of data analysis may contribute to revealing new information in a significant way [5,12]. One of the basic principles used in this field is the visual analytics process by Keim et al [7], which is described as an approach that combines data analysis, visualization, and human factor, as well as the areas of cognition and perception.…”

Section: How To Provide Insight Into Cyber Security Processes Via Expmentioning

confidence: 99%

Visual Analytics for Network Security and Critical Infrastructures

Burska

Ošlejšek

2017

Lecture Notes in Computer Science

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Abstract.A comprehensive analysis of cyber attacks is important for better understanding of their nature and their origin. Providing a sufficient insight into such a vast amount of diverse (and sometimes seemingly unrelated) data is a task that is suitable neither for humans nor for fully automated algorithms alone. Not only a combination of the two approaches but also a continuous reasoning process that is capable of generating a sufficient knowledge base is indispensable for a better understanding of the events. Our research is focused on designing new exploratory methods and interactive visualizations in the context of network security. The knowledge generation loop is important for its ability to help analysts to refine the nature of the processes that continuously occur and to offer them a better insight into the network security related events. In this paper, we formulate the research questions that relate to the proposed solution.

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“…Visualization tools can be categorized in different ways [39,43,50,67]. For example, Grammel et al [19] classify tools based on their user interface, labeling Tableau [62] as a "shelf configuration" tool in reference to how it presents and enables interaction with visual properties and data attributes.…”

Section: Visualization Tools: Types and Approachesmentioning

confidence: 99%

iVoLVER

Méndez

Nacenta

Vandenheste

2016

Proceedings of the 2016 CHI Conference on Human Factors in Computing Systems

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Previous research has identified trade-offs when it comes to designing visualization tools. While constructive "bottomup" tools promote a hands-on, user-driven design process that enables a deep understanding and control of the visual mapping, automated tools are more efficient and allow people to rapidly explore complex alternative designs, often at the cost of transparency. We investigate how to design visualization tools that support a user-driven, transparent design process while enabling efficiency and automation, through a series of design workshops that looked at how both visualization experts and novices approach this problem. Participants produced a variety of solutions that range from example-based approaches expanding constructive visualization to solutions in which the visualization tool infers solutions on behalf of the designer, e.g., based on data attributes. On a higher level, these findings highlight agency and granularity as dimensions that can guide the design of visualization tools in this space.

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A Survey of Visual Analytics Techniques and Applications: State-of-the-Art Research and Future Challenges

Cited by 182 publications

References 120 publications

GPU rasterization for real-time spatial aggregation over arbitrary polygons

GPU rasterization for real-time spatial aggregation over arbitrary polygons

Visual Analytics for Network Security and Critical Infrastructures

iVoLVER

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