Visual Analytics for Early-Phase Complex Engineered System Design Support
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This article reports on our ongoing experiences in developing visual analytics tools for real-world CESs. Our work focuses on the early design phase during which a large design space is explored, poor alternatives are pruned, and valuable alternatives are considered further. Visual analytics tools can provide interactive discovery, exploration, and understanding of real-world complex engineered systems (CES). The proposed tool, which focuses on the early design phase, can help users perform routine CES design analysis tasks and offer stakeholder-specific visual representations of complex design models.Keywords:
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This special issue is devoted to the new research addressing challenges in the areas of visualization and visual analytics. Visualization and visual analytics are closely related research areas, both concentrating on developing visual techniques to reveal meaningful information out of various data in real-life applications. Visualization as a field has its roots in Computer Graphics and has become a popular research area over the years. The field of visual analytics is relatively young with a concentration on analytical reasoning facilitated by interactive visual interfaces. In general, visualization and visual analytics research is tightly connected with certain types of data or applications and researchers in both fields strive to discover known or unknown data patterns for domain users.
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Abstract To complement the currently existing definitions and conceptual frameworks of visual analytics, which focus mainly on activities performed by analysts and types of techniques they use, we attempt to define the expected results of these activities. We argue that the main goal of doing visual analytics is to build a mental and/or formal model of a certain piece of reality reflected in data. The purpose of the model may be to understand, to forecast or to control this piece of reality. Based on this model‐building perspective, we propose a detailed conceptual framework in which the visual analytics process is considered as a goal‐oriented workflow producing a model as a result. We demonstrate how this framework can be used for performing an analytical survey of the visual analytics research field and identifying the directions and areas where further research is needed.
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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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Visual analytics is science of analytical reasoning facilitated by visual interactive interfaces. It can attack certain problems whose size, complexity, and need for closely coupled human and machine analysis may make them otherwise intractable. Visual analytics advances science and technology developments in analytical reasoning, interaction, data transformations and representations for computation and visualization, analytic reporting, and technology transition. As a research agenda, visual analytics brings together several scientific and technical communities from computer science, information visualization, cognitive and perceptual sciences, interactive design, graphic design, and social sciences. This science of analytical reasoning provides the reasoning framework upon which one can build both strategic and tactical visual analytics technologies for threat analysis, prevention, and response. Visual Analytics is a relatively new multidisciplinary field that combines various research areas including knowledge discovery, data analysis, visualization, human-computer interaction, data management, geo-spatial and temporal data processing and statistics. Visual Analytics has seen unprecedented growth in the past five years and is expected to grow ten folds in the coming future seeing the amount of massive, dynamic and heterogeneous data which concludes nothing unless put to use using the visual analytical techniques. The basic goal of visual analytics includes deriving an insight from the large amount of dynamic data which is from different sources and to analyze them to discover the unexpected and provide timely, defensible, and understandable assessments which can be then communicated effectively for action. An integration of the increasing processing power of computers with the efficient pattern recognition abilities and domain knowledge of human analysts is a challenging and promising road in dealing with large amounts of complex data. It will be also a major driving force for solutions for information overload in many research and commercial areas.
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Visual analytics is the science of analytical reasoning facilitated by interactive visual interfaces. It has grown out of and is strongly related to information visualization. Visual analytic tools assist analysts in detecting the expected and discovering the unexpected from complex, noisy, incomplete, heterogeneous, and sometimes deliberately deceptive data. VR/AR research has claimed for years to provide the potential for more effective environments to understand and explore information spaces. This would seem to make VR technology a natural for application to visual analytics. However, visual analytics is focused on the analytical process not the tools and technology. As such, any new methods, techniques, and technologies need to show benefit to analysts working on real problems. Additionally, the majority of visual analytics research funding is not focused on disruptive physical interface technologies. Generally speaking, new technologies and techniques for visual analytics need to function within the current analytical environment. The purpose of this panel is to introduce the domain of visual analytics to the audience and explore how and where VR/AR research can be adapted for use in visual analytics. The panelists have been selected based on topic areas of research that have potential for near term insertion or impact on visual analytics. This panel will focus on the hard issues of defining what it will take to get VR technology introduced into the visual analytics research funding stream.
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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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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.
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Visualizing big and complex multivariate data is challenging. To address this challenge, we propose flexible visual analytics (FVA) with the aim to mitigate visual complexity and interaction complexity challenges in visual analytics, while maintaining the strengths of multiple perspectives on the studied data. At the heart of our proposed approach are transitions that fluidly transform data between user-relevant views to offer various perspectives and insights into the data. While smooth display transitions have been already proposed, there has not yet been an interdisciplinary discussion to systematically conceptualize and formalize these ideas. As a call to further action, we argue that future research is necessary to develop a conceptual framework for flexible visual analytics. We discuss preliminary ideas for prioritizing multi-aspect visual representations and multi-aspect transitions between them, and consider the display user for whom such depictions are produced and made available for visual analytics. With this contribution we aim to further facilitate visual analytics on complex data sets for varying data exploration tasks and purposes based on different user characteristics and data use contexts.
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ISUAL analytics is the science of analytical reasoning supportedbyhighlyinteractivevisualinterfaces.People use visual analytics tools and techniques to synthesize information; derive insight from massive, dynamic, and often conflicting data; detect the expected and discover the unexpected; provide timely, defensible, and understandable assessments; and communicate assessments effectively for action. The issues stimulating this body of research provide a grandchallengeinscience:turninginformationoverloadinto the opportunity of the decade. Visual analytics requires interdisciplinary science beyond traditional scientific and information visualization to include statistics, mathematics, knowledge representation, management and discovery technologies, cognitive and perceptual sciences, decision sciences, and more. An important research agenda to develop the next generation suite of visual analytics technologies is described in the book Illuminating the Path: The Research and Development Agenda for Visual Analytics, which is available at http:// nvac.pnl.gov/agenda.stm. The papers in this special section address a number of the issues described in the visual analytics research agenda. They are grouped into five major areas: multidimensional data, graphs and networks, communication network analysis, space and time, and fundamentals. The first two papers address issues of visual analysis of multidimensional data. The first paper, “High-Dimensional
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