VISUAL ANALYTICS

Visual Analytics



Visual Analytics

Introduction

Visual analytics is a field that gives attention to detail by combining different scientific and technical areas. The essential advantage of Visual analytics is the degree to which the Visual analytics allows attention to detail (Rae, 2011; Isenberg & Fisher, 2009; Wang, Dou, Chen, Ribarsky & Chang, 2010; Park, Chun & Johnson, 2010). Development and usage of Visual analytics demands the careful consideration of all factors and variables associated with a project. This allows the user to acquire a full and complete grasp on the project requirements. In addition, Visual analytics draws its demand for precision from the fact that Visual analytics generally involves the usage of computational technology in combination with theory-based tools (Andrienko, Andrienko, Demsar, Dransch, Dykes, Fabrikant, Jern, Kraak, Schumann & Tominski, 2010; Liu, Guo, Wu & Qian, 2012). As a result, the eventual outcome obtained from a project that makes use of Visual analytics allows for the combined implementation of design and perceptual principles based on cognition. The inclusion of theory enables the development of tactical as well as strategic models in the usage of Visual analytics. While the precise boundaries of Visual analytics are still a subject of debate, this ambiguity has only helped to integrate developments in technology and concepts into the field of Visual analytics (Lemieux, 2011; Andrienko, Andrienko, Dykes, Kraak & Schumann, 2010). Considering the rapid pace of technological innovation and models, it has become common for Visual analytics to develop through developments in software designed for Visual analytics. Developments in information visualization are the most influential in this regard; heavily supported by developments in scientific visualization.

It merits highlighting at this point that Visual analytics seeks to go one step beyond scientific visualization and information visualization (Willson, 2011; Kamel Boulos, Viangteeravat, Anyanwu, Nagisetty & Kuscu, 2011). This is true in the fact that scientific visualization seeks to address data through the structure of the data and the geometric patterns that are present in the data set. Furthermore, while information visualization merely handles abstract data and addresses the complex structures that are present in the data, Visual analytics seeks to derive information from data by allowing users to make sense and reason from data sets; may they be complex or not (Bertini & Santucci, 2011; Bottoni, Labella & Kasangian, 2012). Therefore, it would not be unfair to state that the fundamental purpose of Visual analytics is to bring together the advantages of information and scientific visualization.

Discussion & Analyses

The practical implementation of Visual analytics generally involves the combined usage of tasks that include the development of an understanding of the evolution of the data sets for variables (Telea & Voinea, 2011; Hao, Sharma, Keim, Dayal, Patel & Vennelakanti, 2010). This allows the establishment of an insight into patterns and trends that underlie the incidence of events and occurrences. This can prove to be an extremely useful practice in cases where possible future alternatives need to be predicted through the usage of their warning ...