Challenges in Visual Data Analysis

2011年暑假CAD紫金港这边暑期课程，陈为老师推荐了一篇文章(如题)，看了几遍，现在大致归纳如下：

 

The emerging field of visual analytics focuses on handling massive, heterogeneous, and dynamic volumes of information through integration of human judgement by means of visual representations and interaction techniques in the analysis process.

 

1. Introduction

The basic idea of visual analytics is to visually represent information, allowing the human to directly interact with it, to gain insight, to draw conclusions, and to ultimately make better decisions.

 

The goal of visual analytics research is to turn the information overload into an opportunity.

 

The specific advantage of visual analytics is that decision makers may focus their full cognitive and perceptual capabilities on the analytical proces, while allowing them to apply advanced computational capabilities to augment the discovery process.

 

2. Scope of Visual Analytics

To be more precise, visual analytics is an iterative process that involves collecting information, data preprocessing, knowledge representation, interaction and decision making.

 

The ultimate goal is to gain insight into the problem at hand which is described by vast amounts of scientific, forensic or business data from heterogeneous sources.

 

Scientific visualization examines potentially huge amounts of scientific data obtained from sensors, simulations or laboratory tests with typical applications being flow visualization, volume rendering, and slicing techniques for medical illustrations.

 

We define information visualization more generally as the communication of abstract data relevant in terms of action through the use of interactive visual interfaces.

 

Visual analytics is more than just visualization and can rather be seen as an integrated approach combing visualization, human factors and data analysis.

 

3. Technical Challenges

Having no possibility of adequately exploring the large amouts of data which have been collected due to their potential usefulness, the data becomes useless and the databases become data "dumps".

 

Filtering, aggregation, compression, principle component analysis or other data reduction techniques are then needed to reduce the amount of data as only a small portion of it can be displayed.

 

4.Solutions

Visual analytics combines strengths from information analytics, geospatial analytics, scientific analytics, statistical analytics, knowledge discovery, data management & knowledge representation, presentation, production & dissemination, cognition, perception, and interaction.

 

Visual analytics mantra: "Analyse First----Show the Important---Zoom, Filter and Analyse Further---Details on Demand".

The visual analytics mantra could be exemplarily applied in the context of data analysis fornetwork security. Visualizing the raw data is unfeasible and rarely reveals any insight. Therefore, the data is first analysed and then displayed. The analyst proceeds by choosing a small suspicious subset of the recorded intrusion incidents by applying filters and zoom operations. Finally, this subset is used for a more careful analysis. Insight is gained in the course of the whole visual analytics process.