VISUAL ANALYTICS

Visual Analytics

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Table of Contents

Introduction2

Methodology3

Literature Review3

Findings & Conclusions8

References9

Visual Analytics

Introduction

Visual analytics is the incorporation of entertaining creation with research methods to answer a growing range of questions in technology, business, and intellect. It can deal with certain problems that differ in size, complexness, and need for carefully paired individual and machine research that would have otherwise been intractable.

Visual Analytics change information into a visual form that best parts important features, such as parallels and flaws. The Visual Analytics allow customers to understand clear factors of their information. One of these information factors is Information Quality, or “the fitness for use of the information provided”. Offering an appropriate counsel is the focus of visual analytics; it combines new methods and visual Analytics to assist in human-information discussion and improve knowing of Information Quality.

Visualization and visual analytics depend intensely on fast discovery and a variety of various information measurements and resources conducted by a specialist. Developing Information Quality includes mixing Information Quality living in different resources and providing customers with a specific view of this data quality. This process becomes significant in a variety of circumstances both professional and medical. Developing information seems to be with improving regularity as the amount and the need to discuss current information develops.

Information Quality is not a simple scalar evaluate, but can be described on several measurements, with each aspect producing different symbolism to different information customers and procedures. Each aspect can be calculated and evaluated diversely. Information Quality evaluation indicates providing a value for each aspect about how much of the aspect or quality function is obtained in order to allow sufficient knowing and management.

Methodology

This analysis concentrates on the quality of information acquired from multiple sources and on how the put together Information Quality should be calculated. The purpose of this analysis is to understand and establish rules/principles to enable customers to calculate or determine the quality of the visible information. The guidelines can therefore be used to calculate these quality measurements, which can be provided to the customers in near real-time to help them understand the very subjective quality of the put together information and the major components of that information. As another concern, this work will provide simple means of introducing the Subjective Information Quality measurements through visualizations, so that customers can better take advantage of visible statistics.

Literature Review

Scalability and avoiding visual clutters remains an important issue in graph and network visualization, because the scale of graph for representing real-world applications keeps increasing. Simple graph drawing algorithms are not usually scaling well. So in many cases the nodes in graph are first clustered to create a hierarchy for overview navigations, and then can be interactively explored. Existed agglomerative and divisive hierarchical clustering, can merge nodes into subgroups or communities based on the connectivity of nodes. In addition, other graph features, for example, semantics, topological and geometric features of the networks are studied and extracted by statistical analysis methods to highlight relevant network structure. In this way the presentations of ...