Finally, visual analytics systems require evaluation. One of the most important reasons for evaluation is to position the visual analytics system in context of its ability to address the other challenges (User, Data and Scale/Complexity). It is therefore suggested that the evaluation should provide clear comparisons with previous methods, to assess adequacy and provide an objective overview of the scenario to which the method may be applied. A particularly interesting approach, suggested by Blascheck et al. [19], is to capture data using questionnaires, interaction logs and modern technologies such as eye tracking, before applying a visual analytics system to this data which is optimised specifically for the evaluation process. Using this approach the analyst tasked with reporting on the evaluation process is able to test theories, explore and extract insight from the multiple data sources, rather than analysing one of the data sources at a time.
3.4
Applications of Visual Analytics
Data exists for all sectors of industry, for example security, medicine, finance or business, each of which have their own unique requirements and characteristics. With the rise of visual analytics there has been a blurring of the lines which separate these sectors and whilst the systems are typically targeted to a single sector, they are required to handle some of the intricacies which relate to the others. The following discussion aims to provide an overview of notable visual analytics techniques, based broadly on their target data and tasks, in order to highlight the similarities and differences that occur. In today’s society there is a huge interest in social media and the insights which can be gained from it. For example, Diakopoulos et al. [32] present a visual analytics tool which is able to identify trends and opinions using social media data related to a current event such as a televised debate. The outcome of this process is insight for a user such as a journalist, which can in turn be used in order to form new media such as news articles. Similarly, when a natural or man-made disaster occurs social media can often provide insights such as the people, regions or infrastructure which have been affected and the aftermath that occurs. MacEachren et al. [82] provide a web-based visual analytics solution which leverages the data from twitter in order to allow an analyst to explore the effects of events such as an earthquake.
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In addition to social media to record and track our lives, there is currently a reliance on digital sensors to record all things physical, such as volcanic activity or the speed of traffic. These data sources are capable of recording at extremely high resolutions, 24 hours a day, therefore providing vast amounts of data. For example, Andrienko et al. [6] provide a visual analytics framework which is targeted towards the analysis of movement data such as that provided by global positioning devices. It can also be extremely useful to provide mechanisms to store, retrieve and analyse scientific data such as the approach suggested by Bernard et al. [17] in their implementation of a digital library for scientific data.
The understanding of textual data and linguistics is a highly popular field, because in a similar way to how measurements of physical phenomena explain the world around us, linguistics and textual documents explain our thoughts and intentions. Text Insight via Automated Responsive Analytics (TIARA) proposed by Wei et al. [131] is a visual analytics system designed to analyse and summarise textual information such as emails or patient records. Firstly, the system summarises the documents into topics for which the temporal evolution may be displayed. Then, through user interaction the analyst is able to explore these topics with a direct link back to the original text. In terms of linguistics, Rohrdantz et al. [107] propose a visual analytics approach to explore and track the changes in word meaning.
Visual analytics ultimately aim to help the analysts to make decisions and gather useful knowledge on a particular event or topic of study. For these reasons, they have become a popular tool for businesses and individuals alike due in part to their flexibility and suitability for the testing of hypotheses. For example, systems such as FinVis by Rudolph et al. [108] provide the ability to analyse financial portfolios in order explore risk, reward and the correlations within.
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Considering all things digital, there are vast amounts of personal and sensitive infor- mation now stored in digital systems, which ultimately requires protecting. Related to these are the visual analytics systems focused towards the field of cyber security, such as SemanticPrism proposed by Chen et al. [27]. This system aims to provide insight and analysis for the large, high-dimensional datasets which can be typical of cyber security. Systems such as Proactive and rEactive attack and Response assessment for Cyber Incidents using Visual AnaLytics (PERCIVAL) by Angelini et al. [8] provide the ability to both monitor an attack in addition to the actions taken in an attempt to prevent it. By providing the ability to analyse the problem from both sides, the system is able to provide an overview of the possible evolution of the attack and the actions that may be performed in order to mitigate it.
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