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The enormous amount of data being generated every day is a major issue for organisations. Analysing it and taking decisions from it is a major concern. Visual analytics can be a solution to visualize the data and draw better conclusions from data which was otherwise not possible. Instead of reports and written documents, graphics can play an important role in interpreting results. Visual analytics is a much researched topic nowadays. It is a very emerging field. This paper focuses on this field and the major building blocks of visual analytics on which the concept of visual analytics relies. The three building blocks are namely information visualization, interaction techniques and data analysis.Keywords:
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Visual analytics is the science of analytical reasoning facilitated by interactive visual interfaces. In this paper, we present an adaptation of the visual analytics framework to the context of software understanding for maintenance. We discuss the similarities and differences of the general visual analytics context with the software maintenance context, and present in detail an instance of a visual software analytics application for the build optimization of large-scale code bases. Our application combines and adapts several data mining and information visualization techniques in answering several questions that help developers in assessing and reducing the build cost of such code bases by means of user-driven, interactive analysis techniques.
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SAS® Visual Analytics Explorer is an advanced data visualization and exploratory data analysis application that is a component of the SAS Visual Analytics solution. It excels at handling big data problems like the VAST challenge. With a wide range of visual analytics features and the ability to scale to massive datasets, SAS Visual Analytics Explorer enables analysts to find patt er n s and relationships quickly and easily, no matter the size of their data. In this summary paper, we explain how we used SAS Visual Analytics Explorer to solve the VAST Challenge 2012 minichallenge 1.
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Big data usage evolves from previously looking into the capacity of big data's descriptive and diagnostic perspectives into currently feeding the demands for predictive big data analytics. The needs come about due to organizations that crave predictive analytics capabilities to reduce risk, make intelligent decisions, and generate different customer experiences. Similarly, visual analytics play an essential role in understanding and fitting the analytics prediction in their business decision. Hence, the combination of descriptive, diagnostics and predictive within Visual Analytics emerges as a balanced field to provide understandable predictive insight. Due to the organizational demand and multi-discipline area, the approach to developing visual analytics is still uncertain in the Big Data Project Lifecycle from methodological perspectives. While there are a few potential methodological approaches that could be used for visual analytics, they are scattered across numerous academic research and industrial practice. To date, there is no coherent review and analysis of the work that has been explored specifically for Visual Analytics methodology. This paper reports on a review of previous literature concerning how Visual Analytics has been executed in the big data life cycle to address the gap. The review is organized in this study from three perspectives: i) general ICT -related methodology (e.g. SDLC, Agile, DevOps), ii) Data Science-related methodology (e.g. CRISP-DM, SEMMA, KDD) and iii) Visual Analytics-related methodologies in which each method will be benchmarked based on the Visual Analytics major part of reality, computer and human, in terms of its width, depth, and flows. This study found insufficiencies, non-specific and vague conditions in handling the Visual Analytics when using current methodological approaches based on the review conducted. The paper also highlights the Visual Analytics-related methodological review, which can shed some light on the approaches and ways of implementing analytics in the big data lifecycle, which can be beneficial for future studies in proposing a more comprehensive methodology for Visual Analytics in the big data lifecycle.
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Visual Analytics is successfully employed for an integrated data analysis by means of combining visual and analytical methods. The starting point for current Visual Analytics tools and workflows is usually the readily available data set. Rarely though, Visual Analytics goes beyond the data set and also incorporates the data generating processes that have led to the data in the first place into the analysis. And indeed, in many use case scenarios, this is hardly possible, as these processes cannot be captured as data to be analyzed themselves. Yet for the applications, in which this is feasible, new opportunities and challenges open up.
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Visual analytics, defined as “the science of analytical reasoning facilitated by interactive visual interfaces,” emerged several years ago as a new research field. While it has seen rapid growth for its first five years of existence, the main focus of visual analytics research has been on developing new techniques and systems rather than identifying how people conduct analysis and how visual analytics tools can help the process and the product of sensemaking. The intelligence analysis community in particular has not been fully examined in visual analytics research even though intelligence analysts are one of the major target users for which visual analytics systems are built. The lack of understanding about how analysts work and how they can benefit from visual analytics systems has created a gap between tools being developed and real world practices.
This dissertation is motivated by the observation that existing models of sensemaking/intelligence analysis do not adequately characterize the analysis process and that many visual analytics tools do not truly meet user needs and are not being used effectively by intelligence analysts. I argue that visual analytics research needs to adopt successful HCI practices to better support user tasks and add utility to current work practices. As the first step, my research aims (1) to understand work processes and practices of intelligence analysts and (2) to evaluate a visual analytics system in order to identify where and how visual analytics tools can assist. By characterizing the analysis process and identifying leverage points for future visual analytics tools through empirical studies, I suggest a set of design guidelines and implications that can be used for both designing and evaluating future visual analytics systems.
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SAS Visual Analytics software is an in-memory analytic tool offering an intuitive, drag and drop interface enabling users of all abilities to explore their data effortlessly. Insights are shared easily leading to increased collaboration and improvements in the decision making process. This paper presents an introduction into the SAS Visual Analytics software. Topics include, managing the Visual Analytics environment, the SAS Visual Analytics Data Builder, the SAS Visual Analytics Explorer and the SAS Visual Analytics Reporter. Readers will learn how to load and unload LASR tables, build queries to manipulate tables, create data items, apply filters to data, build interactive charts and tables and learn how to apply chosen properties and styles to their output. This paper is aimed at all individuals who wish to gain a broad understanding of the SAS Visual Analytics software.
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Learning Analytics is the collection, management and analysis of students’ learning. It is used to enable teachers to understand how their students are progressing and for learners to ascertain how well they are performing. Often the data is displayed through dashboards. However, there is a huge opportunity to include more comprehensive and interactive visualizations that provide visual depictions and analysis throughout the lifetime of the learner, monitoring their progress from novices to experts. We therefore encourage researchers to take a comprehensive approach and re-think how visual analytics can be applied to the learning environment, and develop more interactive and exploratory interfaces for the learner and teacher.
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Analysis of movement is currently a hot research topic in visual analytics. A wide variety of methods and tools for analysis of movement data has been developed in recent years. They allow analysts to look at the data from different perspectives and fulfil diverse analytical tasks. Visual displays and interactive techniques are often combined with computational processing, which, in particular, enables analysis of a larger number of data than would be possible with purely visual methods. Visual analytics leverages methods and tools developed in other areas related to data analytics, particularly statistics, machine learning and geographic information science. We present an illustrated structured survey of the state of the art in visual analytics concerning the analysis of movement data. Besides reviewing the existing works, we demonstrate, using examples, how different visual analytics techniques can support our understanding of various aspects of movement.
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Visual analytics has always been an effective analyzing method of structures, dynamics and functions of a wide variety of complex systems. Based on the list of analytic tasks for networks, the fundamental tasks for visual analytics in multilayer networks are introduced, according to the structural multiplexity of multilayer networks, which would motivate further research in visual analytics techniques and contribute to design the visual analytics systems for multilayer networks.
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