The Experts below are selected from a list of 14547 Experts worldwide ranked by ideXlab platform
Daniel A Keim - One of the best experts on this subject based on the ideXlab platform.
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Viewing Visual Analytics as Model Building
Computer Graphics Forum, 2018Co-Authors: Natalia Andrienko, Silvia Miksch, Daniel A Keim, Gennady Andrienko, Tim Lammarsch, Georg Fuchs, Alexander RindAbstract: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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feature driven Visual Analytics of soccer data
Visual Analytics Science and Technology, 2014Co-Authors: Halldor Janetzko, Dominik Sacha, Daniel A Keim, Manuel Stein, Tobias Schreck, Oliver DeussenAbstract:Soccer is one the most popular sports today and also very interesting from an scientific point of view. We present a system for analyzing high-frequency position-based soccer data at various levels of detail, allowing to interactively explore and analyze for movement features and game events. Our Visual Analytics method covers single-player, multi-player and event-based analytical views. Depending on the task the most promising features are semi-automatically selected, processed, and Visualized. Our aim is to help soccer analysts in finding the most important and interesting events in a match. We present a flexible, modular, and expandable layer-based system allowing in-depth analysis. The integration of Visual Analytics techniques into the analysis process enables the analyst to find interesting events based on classification and allows, by a set of custom views, to communicate the found results. The feedback loop in the Visual Analytics pipeline helps to further improve the classification results. We evaluate our approach by investigating real-world soccer matches and collecting additional expert feedback. Several use cases and findings illustrate the capabilities of our approach.
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Knowledge Generation Model for Visual Analytics
Ieee T Vis Comput Gr, 2014Co-Authors: Dominik Sacha, Bum Kwon, Geoffrey Ellis, Florian Stoffel, Bum Chul Kwon, Andreas Stoffel, Daniel A KeimAbstract:Visual Analytics enables us to analyze huge information spaces in order to support complex decision making and data exploration. Humans play a central role in generating knowledge from the snippets of evidence emerging from Visual data analysis. Although prior research provides frameworks that generalize this process, their scope is often narrowly focused so they do not encompass different perspectives at different levels. This paper proposes a knowledge generation model for Visual Analytics that ties together these diverse frameworks, yet retains previously developed models (e.g., {KDD} process) to describe individual segments of the overall Visual analytic processes. To test its utility, a real world Visual Analytics system is compared against the model, demonstrating that the knowledge generation process model provides a useful guideline when developing and evaluating such systems. The model is used to effectively compare different data analysis systems. Furthermore, the model provides a common language and description of Visual analytic processes, which can be used for communication between researchers. At the end, our model reflects areas of research that future researchers can embark on.
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Introduction to the Special Issue on Interactive Computational Visual Analytics
ACM Transactions on Interactive Intelligent Systems, 2014Co-Authors: Remco Chang, David S. Ebert, Daniel A KeimAbstract:This editorial introduction describes the aims and scope of ACM Transactions on Interactive Intelligent Systems's special issue on interactive computational Visual Analytics. It explains why Visual Analytics is crucial to the growing needs surrounding data analysis, and it shows how the four articles selected for this issue reflect this theme.
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Visual Analytics of Movement
2013Co-Authors: Gennady Andrienko, Daniel A Keim, Natalia Andrienko, Peter Bak, Stefan WrobelAbstract:Many important planning decisions in society and business depend on proper knowledge and a correct understanding of movement, be it in transportation, logistics, biology, or the life sciences. Today the widespread use of mobile phones and technologies like GPS and RFID provides an immense amount of data on location and movement. What is needed are new methods of Visualization and algorithmic data analysis that are tightly integrated and complement each other to allow end-users and analysts to extract useful knowledge from these extremely large data volumes. This is exactly the topic of this book. As the authors show, modern Visual Analytics techniques are ready to tackle the enormous challenges brought about by movement data, and the technology and software needed to exploit them are available today. The authors start by illustrating the different kinds of data available to describe movement, from individual trajectories of single objects to multiple trajectories of many objects, and then proceed to detail a conceptual framework, which provides the basis for a fundamental understanding of movement data. With this basis, they move on to more practical and technical aspects, focusing on how to transform movement data to make it more useful, and on the infrastructure necessary for performing Visual Analytics in practice. In so doing they demonstrate that Visual Analytics of movement data can yield exciting insights into the behavior of moving persons and objects, but can also lead to an understanding of the events that transpire when things move. Throughout the book, they use sample applications from various domains and illustrate the examples with graphical depictions of both the interactive displays and the analysis results. In summary, readers will benefit from this detailed description of the state of the art in Visual Analytics in various ways. Researchers will appreciate the scientific precision involved, software technologists will find essential information on algorithms and systems, and practitioners will profit from readily accessible examples with detailed illustrations for practical purposes.
Pak Chung Wong - One of the best experts on this subject based on the ideXlab platform.
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Big graph Visual Analytics
Information Visualization, 2016Co-Authors: David J. Haglin, David Trimm, Pak Chung WongAbstract:This special issue of Information Visualization explores the technical challenges and technology development opportunities of graph Visual Analytics arising from the trend of big data. Big graph Visual Analytics is about applying Visualization and Analytics techniques to gather, analyze, and understand big graphs and the knowledge behind them.
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top ten interaction challenges in extreme scale Visual Analytics
Expanding the Frontiers of Visual Analytics and Visualization, 2012Co-Authors: Pak Chung Wong, Han-wei Shen, Chaomei ChenAbstract:The chapter presents ten selected user interface and interaction challenges in extreme-scale Visual Analytics. The study of Visual Analytics is often referred to as “the science of analytical reasoning facilitated by interactive Visual interfaces” in the literature. The discussion focuses on applying Visual Analytics technologies to extreme-scale scientific and non-scientific data ranging from petabyte to exabyte in sizes. The ten challenges are: in situ interactive analysis, user-driven data reduction, scalability and multi-level hierarchy, representation of evidence and uncertainty, heterogeneous data fusion, data summarization and triage for interactive query, Analytics of temporally evolving features, the human bottleneck, design and engineering development, and the Renaissance of conventional wisdom. The discussion addresses concerns that arise from the different areas of hardware, software, computation, algorithms, and human factors. The chapter also evaluates the likelihood of success in meeting these challenges in the near future.
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expanding the frontiers of Visual Analytics and Visualization
2012Co-Authors: John Dill, Rae A. Earnshaw, David J. Kasik, John Vince, Pak Chung WongAbstract:The field of computer graphics combines display hardware, software, and interactive techniques in order to display and interact with data generated by applications. Visualization is concerned with exploring data and information graphically in such a way as to gain information from the data and determine significance. Visual Analytics is the science of analytical reasoning facilitated by interactive Visual interfaces. Expanding the Frontiers of Visual Analytics and Visualization provides a review of the state of the art in computer graphics, Visualization, and Visual Analytics by researchers and developers who are closely involved in pioneering the latest advances in the field. It is a unique presentation of multi-disciplinary aspects in Visualization and Visual Analytics, architecture and displays, augmented reality, the use of color, user interfaces and cognitive aspects, and technology transfer. It provides readers with insights into the latest developments in areas such as new displays and new display processors, new collaboration technologies, the role of Visual, multimedia, and multimodal user interfaces, Visual analysis at extreme scale, and adaptive Visualization.
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Extreme-scale Visual Analytics
IEEE Computer Graphics and Applications, 2012Co-Authors: Pak Chung Wong, Han-wei Shen, Valerio PascucciAbstract:Extreme-scale Visual Analytics (VA) is about applying VA to extreme-scale data. The articles in this special issue examine advances related to extreme-scale VA problems, their analytical and computational challenges, and their real-world applications.
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Expanding the Frontiers of Visual Analytics and Visualization - Expanding the Frontiers of Visual Analytics and Visualization
2012Co-Authors: John Dill, Rae A. Earnshaw, David J. Kasik, John Vince, Pak Chung WongAbstract:The field of computer graphics combines display hardware, software, and interactive techniques in order to display and interact with data generated by applications. Visualization is concerned with exploring data and information graphically in such a way as to gain information from the data and determine significance. Visual Analytics is the science of analytical reasoning facilitated by interactive Visual interfaces. Expanding the Frontiers of Visual Analytics and Visualization provides a review of the state of the art in computer graphics, Visualization, and Visual Analytics by researchers and developers who are closely involved in pioneering the latest advances in the field. It is a unique presentation of multi-disciplinary aspects in Visualization and Visual Analytics, architecture and displays, augmented reality, the use of color, user interfaces and cognitive aspects, and technology transfer. It provides readers with insights into the latest developments in areas such as new displays and new display processors, new collaboration technologies, the role of Visual, multimedia, and multimodal user interfaces, Visual analysis at extreme scale, and adaptive Visualization.
Gennady Andrienko - One of the best experts on this subject based on the ideXlab platform.
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Viewing Visual Analytics as Model Building
Computer Graphics Forum, 2018Co-Authors: Natalia Andrienko, Silvia Miksch, Daniel A Keim, Gennady Andrienko, Tim Lammarsch, Georg Fuchs, Alexander RindAbstract: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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Steering data quality with Visual Analytics: The complexity challenge
Visual Informatics, 2018Co-Authors: Shixia Liu, Gennady Andrienko, Nan Cao, Liu Jiang, Conglei Shi, Yu-shuen Wang, Seok-hee HongAbstract:Data quality management, especially data cleansing, has been extensively studied for many years in the areas of data management and Visual Analytics. In the paper, we first review and explore the relevant work from the research areas of data management, Visual Analytics and human-computer interaction. Then for different types of data such as multimedia data, textual data, trajectory data, and graph data, we summarize the common methods for improving data quality by leveraging data cleansing techniques at different analysis stages. Based on a thorough analysis, we propose a general Visual Analytics framework for interactively cleansing data. Finally, the challenges and opportunities are analyzed and discussed in the context of data and humans.
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Visual Analytics
1st Europe Summer School on Data Science - SummerSchool '17, 2017Co-Authors: Natalia Andrienko, Gennady AndrienkoAbstract:Visual Analytics aims to combine the strengths of human and computer data processing. Visualization, whereby humans and computers cooperate through graphics, is the means through which this is achieved. Sophisticated synergies are required for analyzing spatio-temporal data and solving spatio-temporal problems. It is necessary to take into account the specifics of the geographic space, time, and spatio-temporal phenomena and processes. While a wide variety of methods and tools are available, it is still hard to find guidelines for considering a data set systematically from multiple perspectives. To fill this gap, we systematically consider the structure of spatio-temporal data, possible transformations, and demonstrate several workflows of comprehensive analysis of different data sets, paying special attention to the investigation of data properties. Using publicly available software prototypes, we shall demonstrate several workflows of analysis of real data sets on human mobility, city traffic, aviation, animal movement, and football. Lecture 1 (09:00 - 10:30) Introduction to Visual Analytics •Definition of Visual Analyticss •Principles of Visualization •Examples of Visualizations and interactions •Data types and structures •Types of data •General analysis tasks: describe, locate, compare, relate •Analysis of multi-dimensional data •Dimensionality reduction •Partition-based clustering •Demo Lecture 2 (11:00 - 12:30) Visual Analytics of spatio-temporal Data •Types and examples of spatio-temporal data •events, spatial time series, trajectories, origin-destination moves, spatial links and flows •properties of the data •Transformation between types •aggregation •event extraction •Transformation of time and space •transformation of times •transformation of space •artificial (semantic) spaces for non-spatial data •Demo Lecture 3 (13:30 - 15:00) Data mining techniques in Visual Analytics •Density-based clustering •events, origin-destination moves, trajectories •clustering of streaming data •Analysis and modelling of spatial time series •two-way partition-based clustering •extracting dependencies between time-variant attributes •modelling •Demo Session 4: hands-on (15:30 - 17:00) *** content is to be defined depending on the number of students and their background
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Visual Analytics of Movement
2013Co-Authors: Gennady Andrienko, Daniel A Keim, Natalia Andrienko, Peter Bak, Stefan WrobelAbstract:Many important planning decisions in society and business depend on proper knowledge and a correct understanding of movement, be it in transportation, logistics, biology, or the life sciences. Today the widespread use of mobile phones and technologies like GPS and RFID provides an immense amount of data on location and movement. What is needed are new methods of Visualization and algorithmic data analysis that are tightly integrated and complement each other to allow end-users and analysts to extract useful knowledge from these extremely large data volumes. This is exactly the topic of this book. As the authors show, modern Visual Analytics techniques are ready to tackle the enormous challenges brought about by movement data, and the technology and software needed to exploit them are available today. The authors start by illustrating the different kinds of data available to describe movement, from individual trajectories of single objects to multiple trajectories of many objects, and then proceed to detail a conceptual framework, which provides the basis for a fundamental understanding of movement data. With this basis, they move on to more practical and technical aspects, focusing on how to transform movement data to make it more useful, and on the infrastructure necessary for performing Visual Analytics in practice. In so doing they demonstrate that Visual Analytics of movement data can yield exciting insights into the behavior of moving persons and objects, but can also lead to an understanding of the events that transpire when things move. Throughout the book, they use sample applications from various domains and illustrate the examples with graphical depictions of both the interactive displays and the analysis results. In summary, readers will benefit from this detailed description of the state of the art in Visual Analytics in various ways. Researchers will appreciate the scientific precision involved, software technologists will find essential information on algorithms and systems, and practitioners will profit from readily accessible examples with detailed illustrations for practical purposes.
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Visual Analytics of Movement
2013Co-Authors: Gennady Andrienko, Daniel Keim, Natalia Andrienko, Peter Bak, Stefan WrobelAbstract:Many important planning decisions in society and business depend on proper knowledge and a correct understanding of movement, be it in transportation, logistics, biology, or the life sciences. Today the widespread use of mobile phones and technologies like GPS and RFID provides an immense amount of data on location and movement. What is needed are new methods of Visualization and algorithmic data analysis that are tightly integrated and complement each other to allow end-users and analysts to extract useful knowledge from these extremely large data volumes. This is exactly the topic of this book. As the authors show, modern Visual Analytics techniques are ready to tackle the enormous challenges brought about by movement data, and the technology and software needed to exploit them are available today. The authors start by illustrating the different kinds of data available to describe movement, from individual trajectories of single objects to multiple trajectories of man y objects, and then proceed to detail a conceptual framework, which provides the basis for a fundamental understanding of movement data. With this basis, they move on to more practical and technical aspects, focusing on how to transform movement data to make it more useful, and on the infrastructure necessary for performing Visual Analytics in practice. In so doing they demonstrate that Visual Analytics of movement data can yield exciting insights into the behavior of moving persons and objects, but can also lead to an understanding of the events that transpire when things move. Throughout the book, they use sample applications from various domains and illustrate the examples with graphical depictions of both the interactive displays and the analysis results. In summary, readers will benefit from this detailed description of the state of the art in Visual Analytics in various ways. Researchers will appreciate the scientific precision involved, software technologists will find essential information
James J. Thomas - One of the best experts on this subject based on the ideXlab platform.
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Visual Analytics: how much Visualization and how much Analytics?
Sigkdd Explorations, 2010Co-Authors: Daniel A Keim, Florian Mansmann, James J. ThomasAbstract:The term Visual Analytics has been around for almost five years by now, but still there are on-going discussions about what it actually is and in particular what is new about it. The core of our view on Visual Analytics is the new enabling and accessible analytic reasoning interactions supported by the combination of automated and Visual analysis. In this paper, we outline the scope of Visual Analytics using two problem and three methodological classes in order to workout the need for and purpose of Visual Analytics. By examples of analytic reasoning interaction, the respective advan- tages and disadvantages of automated and Visual analysis methods are explained leading to a glimpse into the future of how Visual Analytics methods will enable us to go beyond what is possible when separately using the two methods.
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Multimedia analysis + Visual Analytics = multimedia Analytics
IEEE Computer Graphics and Applications, 2010Co-Authors: Nancy A. Chinchor, Michael G. Christel, James J. Thomas, Pak Chung Wong, Jim Thomas, William RibarskyAbstract:To deal with the extent and variety of digital media, researchers are combining multimedia analysis and Visual Analytics to form the new field of multimedia Analytics. This article gives some historical background, discusses surveys of related research, describes initial multimedia Analytics research, and reports on benchmark datasets.
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Challienges for Visual Analytics
Information Visualization, 2009Co-Authors: James J. Thomas, Joe KielmanAbstract:Visual Analytics has seen unprecedented growth in its first 5 years of mainstream existence. Great progress has been made in a short time, yet significant challenges must be met in the next decade to provide new technologies that will be widely accepted throughout the world. This article explains some of those challenges in an effort to provide a stimulus for research, both basic and applied, that can realize or even exceed the potential envisioned for Visual Analytics technologies. We start with a brief summary of the initial challenges, followed by a discussion of the initial driving domains and applications. These are followed by a selection of additional applications and domains that have been a part of recent rapid expansion of Visual Analytics usage. We then look at the common characteristics of several tools illustrating emerging Visual Analytics technologies and conclude with the top 10 challenges for the field of study. We encourage feedback and continued participation by members of the research community, the wide array of user communities and private industry.
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Visual Analytics: building a vibrant and resilient national science
Information Visualization, 2009Co-Authors: Pak Chung Wong, James J. ThomasAbstract:Five years after the science of Visual Analytics was formally established, we attempt to use two different studies to assess the current state of the community and evaluate the progress the community has made in the past few years. The first study involves a comparison analysis of intellectual and scholastic accomplishments recently made by the Visual Analytics community with two other Visualization communities. The second study aims to measure the degree of community reach and internet penetration of Visual-Analytics-related resources. This article describes our efforts to harvest the study data, conduct analysis and make interpretations based on parallel comparisons with five other established computer science areas.
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Visual Analytics: why now?
Information Visualization, 2007Co-Authors: James J. ThomasAbstract:An emerging field of study, Visual Analytics, is briefly described, including its motivations and the emerging partnerships that are bringing the best talents and technologies to missions such as homeland security and human health.
David Gotz - One of the best experts on this subject based on the ideXlab platform.
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Progressive Visual Analytics: User-driven Visual exploration of in-progress Analytics
IEEE Transactions on Visualization and Computer Graphics, 2014Co-Authors: Charles D. Stolper, Adam Perer, David GotzAbstract:As datasets grow and analytic algorithms become more complex, the typical workflow of analysts launching an analytic, waiting for it to complete, inspecting the results, and then re-Iaunching the computation with adjusted parameters is not realistic for many real-world tasks. This paper presents an alternative workflow, progressive Visual Analytics, which enables an analyst to inspect partial results of an algorithm as they become available and interact with the algorithm to prioritize subspaces of interest. Progressive Visual Analytics depends on adapting analytical algorithms to produce meaningful partial results and enable analyst intervention without sacrificing computational speed. The paradigm also depends on adapting information Visualization techniques to incorporate the constantly refining results without overwhelming analysts and provide interactions to support an analyst directing the analytic. The contributions of this paper include: a description of the progressive Visual Analytics paradigm; design goals for both the algorithms and Visualizations in progressive Visual Analytics systems; an example progressive Visual Analytics system (Progressive Insights) for analyzing common patterns in a collection of event sequences; and an evaluation of Progressive Insights and the progressive Visual Analytics paradigm by clinical researchers analyzing electronic medical records.
