Visualization of Time-Oriented Data (eBook, PDF)

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Time is an exceptional dimension that is common to many application domains such as medicine, engineering, business, science, biography, history, planning, or project management. Understanding time-oriented data enables us to learn from the past in order to predict, plan, and build the future. Due to the distinct characteristics of time, appropriate visual and analytical methods are required to explore and analyze them.This book starts with an introduction to visualization and a number of historical examples of visual representations. At its core, the book presents and discusses a systematic v...

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Wolfgang Aigner is the scientific director at the Institute of Creative\Media/Technologies at the St. Pölten University of Applied Sciences, Austria and adjunct professor at TU Wien, Austria. In 2013, he received his habilitation in computer science for his work on "Interactive Visualization and Data Analysis: Visual Analytics with a Focus on Time" from TU Wien. From 2006 to 2010, he was deputy head of the Department of Information and Knowledge Engineering at Danube University Krems, Austria and from 2010 to 2013, he was deputy head of the Laura Bassi Centre of Expertise for Visual Analytics Science and Technology (CVAST) at TU Wien, Austria. Dr. Aigner is an expert in information visualization and visual analytics, particularly in the context of time-oriented data, where he has authored and co-authored more than 140 scientific publications. He served as program committee member and chair for various scientific conferences and acts as associate editor for scientific journals. He has received national awards for his research work, was awarded a Top Cited Article 2005-2010 from Pergamon/Elsevier, and received a best paper honorable mention at the IEEE Conference on Visual Analytics Science and Technology (VAST). Since 2002, Wolfgang has been involved in the acquisition and execution of a number of funded basic and applied research projects at the national and international levels. Silvia Miksch is a university professor and head of the research division "Visual Analytics" (CVAST), Institute of Visual Computing and Human-Centered Technology, TU Wien since 2015. She has been head of the Information and Knowledge Engineering research group, Institute of Software Technology & Interactive Systems, TU Wien from 1998 to 2015. From 2006 to 2010 she was university professor and head of the Department of Information and Knowledge Engineering (ike) at Danube University Krems, Austria. In April 2010 she established the awarded Laura Bassi Centre of Expertise for Visual Analytics Science and Technology (CVAST) funded by the Federal Ministry of Economy, Family and Youth of the Republic of Austria. Silvia has acquired, led, and has been involved in several national and international applied and basic research projects. She served as paper co-chair of several conferences including IEEE VAST 2010, 2011, and 2020 and overall papers chair IEEE VIS 2021 as well as EuroVis 2012, and on the editorial board of several journals including IEEE TVCG and CGF. She acts on various strategic committees, such as the VAST and EuroVis steering committees as well as the VIS executive committee. In 2020 she was inducted into the IEEE Visualization Academy. Furthermore, she acts as scientific reporter in the board of the Austrian Research Fund (FwF) and is advisory board member of the Vienna Science and Technology Fund (WWTF). She has more than 300 scientific publications and her main research interests are visualization and visual analytics over time and space with particular focus on interaction techniques, network-based, knowledge-assisted, and guidance-enriched methods. Heidrun Schumann is a professor emeritus at the University of Rostock, Germany, where she was heading the Chair of Computer Graphics at the Institute for Visual & Analytic Computing. She received doctoral degree (Dr.-Ing.) in 1981 and post-doctoral degree (Dr.-Ing. habil.) in 1989. Her research and teaching activities cover a variety of topics related to computer graphics, including information visualization, visual analytics, and rendering. She is interested in visualizing complex data in space and time, combining visualization and terrain rendering, and facilitating visual data analysis with progressive methods. A key focus of Heidrun's work is to intertwine visual, analytic, and interactive methods for making sense of data. Heidrun published more than two hundred articles in top venues and journals. She co-authored the first German textbook on data visualization in 2000 and a textbook specifically on the visualization of time-oriented data in 2011. In 2014, Heidrun was inducted as a Fellow of the Eurographics Association. In 2020, she was awarded with the Fraunhofer Medal. In the same year, she was inducted to the IEEE Visualization Academy. Christian Tominski is a professor at the Institute for Visual & Analytic Computing at the University of Rostock, Germany. He received doctoral (Dr.-Ing.) and post-doctoral (Dr.-Ing. habil.) degrees in 2006 and 2015, respectively. In 2021, he was granted the title of professor (apl. Prof.) for human-data interaction. His main research interests are in visualization of and interaction with data. He is particularly interested in effective and efficient visual analytics techniques for interactively exploring, analyzing, and editing complex data. Christian has published numerous papers on new visualization and interaction techniques for multivariate data, temporal data, geo-spatial data, and graph data. In his work, he emphasizes conceptual aspects and aims to systematically investigate visual analytics challenges. He co-authored three books, including a book on the visualization of time-oriented data in 2011, a book focusing on interaction for visualization in 2015, and a more general book about interactive visual data analysis in 2020. Christian is the maintainer of several visual analytics systems and visualization tools, including the LandVis system for spatio-temporal health data, the VisAxes tool for time-oriented data, the CGV system for coordinated graph visualization, the Responsive Matrix Cells for exploring and editing multivariate graphs, and the TimeViz Browser for visualization techniques for time-oriented data.

Fernando Berzal - ACM Computing Reviews 16 July 2012

There is no doubt about the importance of visualization in data analysis, whether for examining the unknown and suggesting novel approaches to explore, for confirming yet-to-be-proved hypotheses and abandoning future dead ends, or just for supporting someone's ideas with visual aids in a presentation. In most situations, the data being analyzed includes a temporal dimension whose proper treatment might boost (or bust) the resolution of a given data analysis task. Hence a systematic treatise on the visualization of time-oriented data is welcome.

Four researchers from Vienna University of Technology in Austria and the University of Rostock in Germany have compiled this

extensive catalog of visualization techniques where the temporal dimension plays a major role. Their book contains one-page descriptions of 101 different ways to represent the temporal dimension of data in two or three dimensions. The catalogued techniques include literally dozens of different ways to plot time series. Some have been devised for highlighting parallelisms among different time series when analyzing multivariate data, whereas others were designed for bringing the periodic nature of time into the spotlight. Many of them have been incorporated into data visualization tools for making efficient use of the limited screen real estate and some allow for the visualization of spatiotemporal data, where appropriate. The page devoted to each technique is beautifully illustrated with a relevant diagram or screen shot. Each page also includes a concise textual description of the rationale behind the technique under analysis and, almost as a footnote, the most pertinent bibliographic reference (in rare occasions, a handful of them).

The survey of visualization techniques comprises the second half of this nicely illustrated book. The first half tries to cover the intellectual foundation behind the visualization techniques themselves. A short introduction familiarizes us with the reasons behind the use of data visualization tools, that is, why we might need them and how they can support interactive data analysis. The introduction also describes how they are designed as visualization pipelines and the different reference models that have been proposed to describe their essence. A pleasant historical account of the evolution of the pictorial representation of time, from statistical charts to different artistic movements, paves the way for a more technical overview of how time is modeled in information systems to "reflect the phenomena under consideration and support the analysis task at hand."

The visualization aspects are covered first, followed by the interaction and analytical support that current data visualization tools provide. With respect to data visualization, techniques are structured around three simple questions that also help categorize the 101 techniques in the survey: What? Why? How? The data to be represented (what) and the user tasks that must be supported (why) determine the visual representation mechanism (how). Some examples are given to illustrate the proper solution of data visualization problems, from ways to highlight the periodicity in temporal data to the different color- coding strategies that should be used to support different tasks (the same color scheme is not suitable for identification, localization, and comparison, for instance).

This book also includes a couple of chapters on features that every data visualization tool should support. The first of these chapters, on interaction, describes user intents, conceptual and technical considerations that should be taken into account, and methods such as direct manipulation, brushing & linking, or dynamic queries, as well as the use of event-based visualization. The second chapter, on analytics, merely touches the surface of a growing field, which deserves a much more elaborate treatment.

Iagree with the authors' plea for more effort on the integration of visual, interactive, and analytical methods for mining large and complex time-oriented data. For interactive data visualization tools, scalability issues are paramount if we want to study very long time series of large numbers of data in parallel. Newcomers to the field can certainly benefit from the authors' description of the general architecture of visualization tools, the considerations they recommend with respect to visualization and interaction techniques, and the coarse taxonomy of visualization techniques they propose to structure their survey of visualization techniques. Combined, these provide the conceptual framework that could serve as the starting point for the new research that could make the authors' plea a reality.

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