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Chapter
Visual Analytics for Understanding Texts
Texts are created for humans, who are trained to read and understand them. Texts are poorly suited for machine processing; still, humans need computer help when it is necessary to gain an overall understanding...
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Chapter
Visual Analytics for Understanding Images and Video
Images and video recordings are commonly categorised as unstructured data, which means that they are not primarily suited for computer analysis. The contents of unstructured data cannot be adequately represent...
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Chapter
Conclusion
This chapter very briefly summarises the main ideas and principles of visual analytics, while the main goal is to show by example how to devise new visual analytics approaches and workflows using general techn...
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Chapter
Introduction to Visual Analytics by an Example
An illustrated example of problem solving is meant to demonstrate how visual representations of data support human reasoning and deriving knowledge from data.We argue that human reasoning plays a crucial role ...
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Chapter
Principles of Interactive Visualisation
We introduce the basic principles and rules of the visual representation of information. Any visualisation involves so-called visual variables, such as position along an axis, size, colour hue and lightness, a...
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Chapter
Visual Analytics for Investigating and Processing Data
In this chapter, we discuss how visual analytics techniques can support you in investigating and understanding the properties of your data and in conducting common data processing tasks. We consider several ex...
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Chapter
Visual Analytics for Understanding Relationships between Entities
A graph is a mathematical model for representing a system of pairwise relationships between entities. The term “graph” or “graph data” is quite often used to refer, actually, to a system of relationships, whic...
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Chapter
Visual Analytics for Understanding Spatial Distributions and Spatial Variation
We begin with a simple motivating example that shows how putting spatial data on a map and seeing spatial relationships can help an analyst to make important discoveries. We consider possible contents and form...
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Chapter
Computational Modelling with Visual Analytics
Data scientists usually aim at building computer models. Computeroriented modelling methods and software tools are developed in statistics, machine learning, data mining, and various specialised disciplines, s...
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Chapter
General Concepts
Analysis is always focused on a certain subject, which is a thing or phenomenon that needs to be understood and, possibly, modelled. The data science process involves analysis of three different subjects: data...
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Chapter
Computational Techniques in Visual Analytics
Visual analytics approaches combine interactive visualisations with the use of computational techniques for data processing and analysis. Combining visualisation and computation has two sides. One side is comp...
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Chapter
Visual Analytics for Understanding Multiple Attributes
One very common challenge that every data scientists has to deal with is to make sense of data sets with many attributes, where “many” can sometimes be tens, sometimes hundreds, and even thousands. Whether you...
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Chapter
Visual Analytics for Understanding Temporal Distributions and Variations
There are two major types of temporal data, events and time series of attribute values, and there are methods for transforming one of them into the other. For events, a general analysis task is to understand h...
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Chapter
Visual Analytics for Understanding Phenomena in Space and Time
There are different kinds of spatio-temporal phenomena, including events that occur at different locations, movements of discrete entities, changes of shapes and sizes of entities, changes of conditions at dif...
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Chapter and Conference Paper
On the Challenges and Opportunities in Visualization for Machine Learning and Knowledge Extraction: A Research Agenda
We describe a selection of challenges at the intersection of machine learning and data visualization and outline a subjective research agenda based on professional and personal experience. The unprecedented in...
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Chapter
On Computationally-Enhanced Visual Analysis of Heterogeneous Data and Its Application in Biomedical Informatics
With the advance of new data acquisition and generation technologies, the biomedical domain is becoming increasingly data-driven. Thus, understanding the information in large and complex data sets has been in ...
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Article
Open AccessVisual cavity analysis in molecular simulations
Molecular surfaces provide a useful mean for analyzing interactions between biomolecules; such as identification and characterization of ligand binding sites to a host macromolecule. We present a novel techniq...
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Chapter and Conference Paper
Hypothesis Generation by Interactive Visual Exploration of Heterogeneous Medical Data
High dimensional, heterogeneous datasets are challenging for domain experts to analyze. A very large number of dimensions often pose problems when visual and computational analysis tools are considered. Analys...
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Chapter
An Information Theoretical Approach to Crowd Simulation
In this study, an information theory based framework to automatically construct analytical maps of crowd’s locomotion, called behavior maps, is presented. For these behavior maps, two distinct use cases in crowd ...
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Chapter and Conference Paper
An Information Theory Based Behavioral Model for Agent-Based Crowd Simulations
In this paper, we propose a novel behavioral model which builds analytical maps to control agents’ behavior adaptively with agentcrowd interaction formulations. We introduce information theoretical concepts to...
