Matrix visualizations are a useful tool to provide a general overview of a graph's structure. For multivariate graphs, a remaining challenge is to cope with the attributes that are associated with nodes and edges. Addressing this challenge, we propose responsive matrix cells as a focus+context approach for embedding additional interactive views into a matrix. Responsive matrix cells are local zoomable regions of interest that provide auxiliary data exploration and editing facilities for multivariate graphs. They behave responsively by adapting their visual contents to the cell location, the available display space, and the user task. Responsive matrix cells enable users to reveal details about the graph, compare node and edge attributes, and edit data values directly in a matrix without resorting to external views or tools. We report the general design considerations for responsive matrix cells covering the visual and interactive means necessary to support a seamless data exploration and editing. Responsive matrix cells have been implemented in a web-based prototype based on which we demonstrate the utility of our approach. We describe a walk-through for the use case of analyzing a graph of soccer players and report on insights from a preliminary user feedback session.

The word ‘pattern’ frequently appears in the visualisation and visual analytics literature, but what do we mean when we talk about patterns? We propose a practicable definition of the concept of a pattern in a data distribution as a combination of multiple interrelated elements of two or more data components that can be represented and treated as a unified whole. Our theoretical model describes how patterns are made by relationships existing between data elements. Knowing the types of these relationships, it is possible to predict what kinds of patterns may exist. We demonstrate how our model underpins and refines the established fundamental principles of visualisation. The model also suggests a range of interactive analytical operations that can support visual analytics workflows where patterns, once discovered, are explicitly involved in further data analysis.

Over the past years, the visualization of large and complex data sets brought up various Visual Analytics (VA) tools in order to solve domain-specific tasks. These VA tools are typically implemented as individual software components in data-flow-oriented models, meaning that data is transferred from one component to the next. While most VA frameworks rely on a monolithic architecture with features for the integration of specialized analysis methods, we consider a loose coupling of independent applications, where autonomous VA tools are used in predefined analysis sequences. To this end, we provide a characterization of the data exchange process among individual VA tools in the form of a taxonomy. This taxonomy can be used as a checklist to identify characteristics and improve the data flow of one's own multi-tool VA setup. For this purpose, we conducted a systematic investigation of the individual aspects of data exchange that are commonly found across different usage scenarios. We apply our taxonomy to three existing multi-tool frameworks, the open-source library ReVize, the toolchain editor AnyProc, and the visualization and monitoring framework Plant@Hand3D.

Abstract This work presents an approach to support the visual analysis of parameter dependencies of time‐series segmentation. The goal is to help analysts understand which parameters have high influence and which segmentation properties are highly sensitive to parameter changes. Our approach first derives features from the segmentation output and then calculates correlations between the features and the parameters, more precisely, in parameter subranges to capture global and local dependencies. Dedicated overviews visualize the correlations to help users understand parameter impact and recognize distinct regions of influence in the parameter space. A detailed inspection of the segmentations is supported by means of visually emphasizing parameter ranges and segments participating in a dependency. This involves linking and highlighting, and also a special sorting mechanism that adjusts the visualization dynamically as users interactively explore individual dependencies. The approach is applied in the context of segmenting time series for activity recognition. Informal feedback from a domain expert suggests that our approach is a useful addition to the analyst's toolbox for time‐series segmentation.

The visual analysis of multivariate graphs is a challenging problem. We address the particular task of studying relations between the structure of a graph and the multivariate attributes associated with it. To facilitate this task, we propose a novel interactive visualization approach. The core idea is to show structure and calculated attribute similarity in an integrated fashion as a matrix. A table can be attached to the matrix on demand to visualize the underlying attribute values in detail. To support the visual comparison of structure and attributes at different levels, several interaction techniques are provided, including matrix reordering, selection and emphasis of subsets, rearrangement of sub-matrices, and column rotation for detailed comparison. To demonstrate the utility of our techniques, we apply them to explore relations between structure and attributes in a network of soccer players.

Abstract The elegance of using virtual interactive lenses to provide alternative visual representations for selected regions of interest is highly valued, especially in the realm of visualization. Today, more than 50 lens techniques are known in the closer context of visualization, far more in related fields. In this paper, we extend our previous survey on interactive lenses for visualization. We propose a definition and a conceptual model of lenses as extensions of the classic visualization pipeline. An extensive review of the literature covers lens techniques for different types of data and different user tasks and also includes the technologies employed to display lenses and to interact with them. We introduce a taxonomy of lenses for visualization and illustrate its utility by dissecting in detail a multi‐touch lens for exploring large graph layouts. As a conclusion of our review, we identify challenges and unsolved problems to be addressed in future research.

In 3D visualization of terrain, occlusion has detrimental impact on discovering, accessing, and spatially relating embedded data. This can lead to significant loss of information. To address this issue, we present visibility widgets: compact auxiliary views that visualize occluded data, their locations, and related properties, and thus, support users in revealing hidden information. Our widgets comprise different occlusion management strategies for detecting and analyzing invisible data inside as well as outside of the field of view. In addition, each widget is designed complementary and transient to facilitate interactive exploration and navigation while not monopolizing the user's attention. We demonstrate the application of our visibility widgets in a flexible visualization tool, focusing on visual exploration of hazardous weather phenomena in 3D terrain.

With today's technical possibilities, a stable visualization scenario can no longer be assumed as a matter of course, as underlying data and targeted display setup are much more in flux than in traditional scenarios. Incremental visualization approaches are a means to address this challenge, as they permit the user to interact with, steer, and change the visualization at intermediate time points and not just after it has been completed. In this paper, we put forward a model for incremental visualizations that is based on the established Data State Reference Model, but extends it in ways to also represent partitioned data and visualization operators to facilitate intermediate visualization updates. In combination, partitioned data and operators can be used independently and in combination to strike tailored compromises between output quality, shown data quantity, and responsiveness-i.e., frame rates. We showcase the new expressive power of this model by discussing the opportunities and challenges of incremental visualization in general and its usage in a real world scenario in particular.

Abstract Analyzing movements in their spatial and temporal context is a complex task. We are additionally interested in understanding the movements’ dependency on parameters that govern the processes behind the movement. We propose a visual analytics approach combining analytic, visual, and interactive means to deal with the added complexity. The key idea is to perform an analytical extraction of features that capture distinct movement characteristics. Different parameter configurations and extracted features are then visualized in a compact fashion to facilitate an overview of the data. Interaction enables the user to access details about features, to compare features, and to relate features back to the original movement. We instantiate our approach with a repository of more than twenty accepted and novel features to help analysts in gaining insight into simulations of chaotic behavior of thousands of entities over thousands of data points. Domain experts applied our solution successfully to study dynamic groups in such movements in relation to thousands of parameter configurations.

Abstract Usually visualization is applied to gain insight into data. Yet consuming the data in form of visual representation is not always enough. Instead, users need to edit the data, preferably through the same means used to visualize them. In this work, we present a semi‐automatic approach to visual editing of graphs. The key idea is to use an interactive EditLens that defines where an edit operation affects an already customized and established graph layout. Locally optimal node positions within the lens and edge routes to connected nodes are calculated according to different criteria. This spares the user much manual work, but still provides sufficient freedom to accommodate application‐dependent layout constraints. Our approach utilizes the advantages of multi‐touch gestures, and is also compatible with classic mouse and keyboard interaction. Preliminary user tests have been conducted with researchers from bio‐informatics who need to manually maintain a slowly, but constantly growing molecular network. As the user feedback indicates, our solution significantly improves the editing procedure applied so far.

Large dynamic networks are targets of analysis in many fields. Tracking temporal changes at scale in these networks is challenging due in part to the fact that small changes can be missed or drowned-out by the rest of the network. For static networks, current approaches allow the identification of specific network elements within their context. However, in the case of dynamic networks, the user is left alone with finding salient local network elements and tracking them over time. In this work, we introduce a modular DoI specification to flexibly define what salient changes are and to assign them a measure of their importance in a time-varying setting. The specification takes into account neighborhood structure information, numerical attributes of nodes/edges, and their temporal evolution. A tailored visualization of the DoI specification complements our approach. Alongside a traditional node-link view of the dynamic network, it serves as an interface for the interactive definition of a DoI function. By using it to successively refine and investigate the captured details, it supports the analysis of dynamic networks from an initial view until pinpointing a user's analysis goal. We report on applying our approach to scientific coauthorship networks and give concrete results for the DBLP data set.

In conventional Web browsing, to explore contents of interest while browsing a Web page, in many cases users need to leave that Web page and switch to external applications or other task-specific Web sites. To avoid switching between Web pages, we present in this paper an approach to support the handling of data and tasks further added onto the browsed Web pages in terms of augmented Web browsing. Without changing the existing Web contents and page-layouts, two levels of visual supports addable onto any existing Web page are suggested: (1) at micro level, visual cues encoding additional information are directly bound to particular hypermedia items, and (2) at macro level, additional visual layers are flexibly attached onto the browsed Web pages. To illustrate the ideas, the Fire fox add-on VizMe is developed with examples mimicking Google as a browser's extension, and to browse Web snippets with geospatial and temporal references.

Abstract The π -calculus, in particular its stochastic version the stochastic π -calculus, is a common modeling formalism to concisely describe the chemical reactions occurring in biochemical systems. However, it remains largely unexplored how to transform a biochemical model expressed in the stochastic π -calculus back into a set of meaningful reactions. To this end, we present a two step approach of first translating model states to reaction sets and then visualizing sequences of reaction sets, which are obtained from state trajectories, in terms of reaction networks. Our translation from model states to reaction sets is formally defined and shown to be correct, in the sense that it reflects the states and transitions as they are derived from the continuous time Markov chain-semantics of the stochastic π -calculus. Our visualization concept combines high level measures of network complexity with interactive, table-based network visualizations. It directly reflects the structures introduced in the first step and allows modelers to explore the resulting simulation traces by providing both: an overview of a network’s evolution and a detail inspection on demand.

The visual analysis of dynamic networks is a challenging task. In this paper, we introduce a new approach supporting the discovery of substructures sharing a similar trend over time by combining computation, visualization and interaction. With existing techniques, their discovery would be a tedious endeavor because of the number of nodes, edges as well as time points to be compared. First, on the basis of the supergraph, we therefore group nodes and edges according to their associated attributes that are changing over time. Second, the supergraph is visualized to provide an overview of the groups of nodes and edges with similar behavior over time in terms of their associated attributes. Third, we provide specific interactions to explore and refine the temporal clustering, allowing the user to further steer the analysis of the dynamic network. We demonstrate our approach by the visual analysis of a large wireless mesh network.

Knowledge about visualization tasks plays an important role in choosing or building suitable visual representations to pursue them. Yet, tasks are a multi-faceted concept and it is thus not surprising that the many existing task taxonomies and models all describe different aspects of tasks, depending on what these task descriptions aim to capture. This results in a clear need to bring these different aspects together under the common hood of a general design space of visualization tasks, which we propose in this paper. Our design space consists of five design dimensions that characterize the main aspects of tasks and that have so far been distributed across different task descriptions. We exemplify its concrete use by applying our design space in the domain of climate impact research. To this end, we propose interfaces to our design space for different user roles (developers, authors, and end users) that allow users of different levels of expertise to work with it.

Understanding information and gaining insights about information usually means interacting with the displayed information. Utilizing the new capabilities of smart meeting rooms, visual outputs of different information representation applications are presented according to the user's needs. In this paper we present smart interaction management. This interaction approach enables the users to interact with all displayed views, utilizing the novel capabilities of these environments while still being able to interact with applications in a conventional manner using local devices. We further show two use cases demonstrating typical applications of our approach in such multi-display environments: (1) to modify the arrangement and layout of views and (2) to interact with the displayed information within a view.

As heterogeneous data from different sources are being increasingly linked, it becomes difficult for users to understand how the data are connected, to identify what means are suitable to analyze a given data set, or to find out how to proceed for a given analysis task. We target this challenge with a new model-driven design process that effectively codesigns aspects of data, view, analytics, and tasks. We achieve this by using the workflow of the analysis task as a trajectory through data, interactive views, and analytical processes. The benefits for the analysis session go well beyond the pure selection of appropriate data sets and range from providing orientation or even guidance along a preferred analysis path to a potential overall speedup, allowing data to be fetched ahead of time. We illustrate the design process for a biomedical use case that aims at determining a treatment plan for cancer patients from the visual analysis of a large, heterogeneous clinical data pool. As an example for how to apply the comprehensive design approach, we present Stack'n'flip, a sample implementation which tightly integrates visualizations of the actual data with a map of available data sets, views, and tasks, thus capturing and communicating the analytical workflow through the required data sets.

Visualizing trajectory attribute data is challenging because it involves showing the trajectories in their spatio-temporal context as well as the attribute values associated with the individual points of trajectories. Previous work on trajectory visualization addresses selected aspects of this problem, but not all of them. We present a novel approach to visualizing trajectory attribute data. Our solution covers space, time, and attribute values. Based on an analysis of relevant visualization tasks, we designed the visualization solution around the principle of stacking trajectory bands. The core of our approach is a hybrid 2D/3D display. A 2D map serves as a reference for the spatial context, and the trajectories are visualized as stacked 3D trajectory bands along which attribute values are encoded by color. Time is integrated through appropriate ordering of bands and through a dynamic query mechanism that feeds temporally aggregated information to a circular time display. An additional 2D time graph shows temporal information in full detail by stacking 2D trajectory bands. Our solution is equipped with analytical and interactive mechanisms for selecting and ordering of trajectories, and adjusting the color mapping, as well as coordinated highlighting and dedicated 3D navigation. We demonstrate the usefulness of our novel visualization by three examples related to radiation surveillance, traffic analysis, and maritime navigation. User feedback obtained in a small experiment indicates that our hybrid 2D/3D solution can be operated quite well.

Nowadays, a great amount of data is created and distributed on the Internet. Tagging has become common practice to structure these data for easy access. Often the data and the associated tags contain spatial and temporal information. In this paper, we develop general design strategies for visualizing spatially and temporally referenced tags similar to tag clouds on maps. Temporal information of tags is encoded through the visual appearance of text or through additional visual artifacts associated with the tags, whereas the location of tags on a map illustrates the spatial references. We demonstrate our solution based on an interactive visualization prototype for the exploration of both spatial and temporal references of Flickr tags.

The analysis of large dynamic networks poses a challenge in many fields, ranging from large bot-nets to social networks. As dynamic networks exhibit different characteristics, e.g., being of sparse or dense structure, or having a continuous or discrete time line, a variety of visualization techniques have been specifically designed to handle these different aspects of network structure and time. This wide range of existing techniques is well justified, as rarely a single visualization is suitable to cover the entire visual analysis. Instead, visual representations are often switched in the course of the exploration of dynamic graphs as the focus of analysis shifts between the temporal and the structural aspects of the data. To support such a switching in a seamless and intuitive manner, we introduce the concept of in situ visualization--a novel strategy that tightly integrates existing visualization techniques for dynamic networks. It does so by allowing the user to interactively select in a base visualization a region for which a different visualization technique is then applied and embedded in the selection made. This permits to change the way a locally selected group of data items, such as nodes or time points, are shown--right in the place where they are positioned, thus supporting the user's overall mental map. Using this approach, a user can switch seamlessly between different visual representations to adapt a region of a base visualization to the specifics of the data within it or to the current analysis focus. This paper presents and discusses the in situ visualization strategy and its implications for dynamic graph visualization. Furthermore, it illustrates its usefulness by employing it for the visual exploration of dynamic networks from two different fields: model versioning and wireless mesh networks.

Apart from explicit node-link representations, implicit visualizations and especially the Treemap as their frontrunner have acquired a solid position among the available techniques to visualize hierarchies. Their advantage is a highly space-efficient graphical representation that does not require explicit drawing of edges. In this paper, we survey the design space for this class of visualization techniques. We establish the design space along the four axes of dimensionality, edge representation, node representation, and layout by examining existing implicit hierarchy visualization techniques. The survey is completed by casting some light into regions of the design space that have not yet been explored. Our design space is not a mere theoretical construct, but a practically usable tool for rapid visualization development. To that end, we discuss a software implementation of the introduced design space.

Space-filling layout techniques for tree representations are frequently used when the available screen space is small or the data set is large. In this paper, we propose an efficient approach to space-filling tree representations that uses mechanisms from the point-based rendering paradigm. We present helpful interaction techniques and visual cues that tie in with our layout. Additionally, we relate this new layout approach to common layout mechanisms and evaluate the new layout along the lines of a numerical evaluation using the measures of the Ink-Paper Ratio and overplotted%, and in a preliminary user study. The flexibility of the general approach is illustrated by several enhancements of the basic layout, as well as its usage within the context of two software frameworks from different application fields.

The use of transparencies is a common strategy in visual representations to guarantee the visibility of different overlapping graphical objects, especially, if no visibility-deciding order is given (e.g., importance, depth). Alpha-blending, however, could generate new colors that are not specified by the given color scale and overlapping shapes may become difficult to be separated visually and the selection of specific elements would be difficult. In this paper, we present a new approach for representing overlapping regions: Instead of blending different colors, our weaving technique separates the original colors and shapes are easier to differentiate. Due toadeterministicweavingorder, all overlapping objects are visible. We apply our approach to scatter plot visualizations to enhance the communication of overlapping clusters.

Searching and exploring on digital maps are normally performed through simple text boxes and zoom-and-pan interfaces. In this paper, however, we present a novel technique, namely Taggram, which combines texts onto maps to support geo-tagged data exploration. It is designed to show geo-tagged data in form of size-varied and colorized tags, benefiting by the concepts of Tag Clouds, and to support exploring those data interactively through a fisheye menu adaptation. The technique was demonstrated for geo-tagged data exploration; however, as other thematic geo-data can be partially represented in abstract form of texts, Taggram can be a method for the presentation and exploration of such geo-data in other application scenarios.

Visual analytics aims to combine the strengths of human and electronic data processing. Visualisation, whereby humans and computers cooperate through graphics, is the means through which this is achieved. Seamless and sophisticated synergies are required for analysing spatio-temporal data and solving spatio-temporal problems. In modern society, spatio-temporal analysis is not solely the business of professional analysts. Many citizens need or would be interested in undertaking analysis of information in time and space. Researchers should find approaches to deal with the complexities of the current data and problems and find ways to make analytical tools accessible and usable for the broad community of potential users to support spatio-temporal thinking and contribute to solving a large range of problems.

This issue has a specific focus on TIME. The research articles contributed here deal with the temporal nature of geospatial phenomena in novel and sophisticated ways in the context of geospatial vi...

When visualizing data, spatial and temporal references of these data often have to be considered in addition to the actual data attributes. Nowadays, structural information is becoming more and more important. Hierarchies, for instance, are frequently applied to make large and complex data manageable. Hence, a visual depiction of hierarchical structures in space and time is required. Although there are several techniques addressing specific aspects of spatio-temporal visualization, approaches that cope with space, time, data, and structure are rare. With this article we take a step to fill this gap. By combining various well-established concepts, we achieve a reasonably complete visualization of all of the aforementioned aspects, where our focus is on the hierarchical structures. We embed hierarchies directly into the regions of a map display using variants of the point-based layout. Layering and animation are applied to visualize temporal aspects. Based on the analysis goals, users can switch between representations that emphasize data attributes or hierarchical structures. Interaction techniques support users in navigating the data and their visualization. We demonstrate the usefulness of our approach by adapting it to implement a visualization for spatiotemporal human health data.

Space-filling layout techniques for tree representations are frequently used when the available screen space is small or the data set is large. In this paper, we propose a new approach to space-filling tree representations, which uses mechanisms from the point-based rendering paradigm. Additionally, helpful interaction techniques that tie in with our layout are presented. We will relate our new technique to established space-filling techniques along the lines of a newly developed classification and also evaluate it numerically using the measures of theink-paper-ratioandoverplotted%.

Current visualization systems are typically based on the concept of interactive post-processing. This decoupling of data visualization from the process of data generation offers a flexible application of visualization tools. It can also lead, however, to information loss in the visualization. Therefore, a combination of the visualization of the data generating process with the visualization of the produced data offers significant support for the understanding of the abstract data sets as well as the underlying process. Due to the application-specific characteristics of data generating processes, the task requires tailored visualization concepts. In this work, we focus on the application field of simulating biochemical reaction networks as discrete-event systems. These stochastic processes generate multi-run and multivariate time-series, which are analyzed and compared on three different process levels: model, experiment, and the level of multi-run simulation data, each associated with a broad range of analysis goals. To meet these challenging characteristics, we present visualization concepts specifically tailored to all three process levels. The fundament of all three visualization concepts is a compact view that relates the multi-run simulation data to the characteristics of the model structure and the experiment. The view provides the visualization at the experiment level. The visualization at the model level coordinates multiple instances of this view for the comparison of experiments. At the level of multi-run simulation data, the views gives an overview on the data, which can be analyzed in detail in time-series views suited for the analysis goals. Although we derive our visualization concepts for one concrete simulation process, our general concept of tailoring the visualization concepts to process levels is generally applicable for the visualization of simulation processes.

Smart environments consist of several interconnected devices. The device ensemble can change dynamically as mobile devices enter or leave the environment. To utilize such environments efficiently for information visualization, we propose a service-oriented architecture.Various services run on different machines and visualizations are generated dynamically depending on the environment's current situation. The necessary adaptation to available output devices is driven by instantiation of different service implementations, by parameterizing service invocations, and by adapting the visualization pipeline at run-time. We implemented a prototype that provides parallel coordinates, scatter plot matrices, and a map display.

Computational simulation is an established method to gain insight into cellular processes. As the resulting data sets are usually large and complex, visualization can play a significant role in data analysis. In this paper, we focus on the visualization of simulation output from the next sub-volume method, a spatial simulation algorithm. In addition to the spatial context of the simulation output, its heterogeneous data types, multiple variables, and the temporal context make high demands on the visualization. To cope with these challenging characteristics, we systematically explore possible visualization concepts with respect to these characteristics. From these findings, we derive our specific solution to visualize the data from the next sub-volume method, using a framework of multiple coordinated views that emphasize the spatial context of the data. Combining these views with a highly interactive user interface, the user is able to adapt the visualization to his current analysis goals and explore the data in its complexity.

In many interactive scenarios, the fast recognition and localization of crucial information is very important to effectively perform a task. However, in information visualization the visualization of permanently growing large data volumes often leads to a simultaneously growing amount of presented graphical primitives. Besides the fundamental problem of limited screen space, the effective localization of single or multiple items of interest by a user becomes more and more difficult. Therefore, different approaches have been developed to emphasize those items -- mainly by manipulating the items size, by suppressing the whole context or by adding supplemental visual elements (e.g., contours, arrows). This paper introduces the well known illustrative technique of haloing to information visualization to address the localization problem. Applying halos emphasizes items without a manipulation of size or an introduction of additional visual elements and reduces the context suppression to a locally defined region. This paper also presents the results of a first user-study to get an impression of the usefulness of halos for a faster recognition.

Color coding is a widely used visualization method for scalar data. To generate expressive and effective visual representations, it is extremely important to carefully design the mapping from data to color. In this paper, we describe a color coding approach that accounts for the different tasks users might pursue when analyzing data. Our task description is based on the task model of Andrienko & Andrienko. We apply different color scales and introduce strategies to adapt the color mapping function to support tasks like comparison, localization, or identification of data values.

Advances in the field of microarray technology have attracted a lot of attention in recent years. More and more biological experiments are conducted based on microarrays. The challenge researchers face today is to analyze and understand the collected data. We present a visual approach to support understanding microarray data. In contrast to other visualization techniques, which represent expression of genes, we go one step further and make a switch to combinations of genes. Gene combinations bear more information, and hence, can lead to new hypotheses about the data. However, the increased amount of information imposes several challenges to an interactive visualization approach. We propose analytical and visual methods to deal with these challenges.

In many application fields, the statistical properties of data sets are of great interest for data analysts. Since local variations can occur especially in large datasets, it is useful to visualize not only global values, but also the properties of user-defined subsets. Hence, we present in this paper a visualization of the statistical characteristics of subsets, with an emphasis on the temporal development. The visualization is coordinated with other views. They provide details about the data and allow for smooth brushing of subsets, a concept that introduces a continuous transition from data in focus to context data. Our approach accounts for smooth brushing in both the derivation and the visualization of statistical properties in order to visualize variations within subsets. To analyze differences between multiple subsets, our approach further integrates visualization concepts for the comparison of statistical properties. An application example from the simulation of biological systems demonstrates the benefits of our approach.

Providing appropriate methods to facilitate the analysis of time-oriented data is a key issue in many application domains. In this paper, we focus on the unique role of the parameter time in the context of visually driven data analysis. We will discuss three major aspects - visualization, analysis, and the user. It will be illustrated that it is necessary to consider the characteristics of time when generating visual representations. For that purpose we take a look at different types of time and present visual examples. Integrating visual and analytical methods has become an increasingly important issue. Therefore, we present our experiences in temporal data abstraction, principal component analysis, and clustering of larger volumes of time-oriented data. The third main aspect we discuss is supporting user-centered visual analysis. We describe event-based visualization as a promising means to adapt the visualization pipeline to needs and tasks of users.

In many information visualization techniques, labels are an essential part to communicate the visualized data. To preserve the expressiveness of the visual representation, a placed label should neither occlude other labels nor visual representatives (e.g., icons, lines) that communicate crucial information. Optimal, non-overlapping labeling is an NP-hard problem. Thus, only a few approaches achieve a fast non-overlapping labeling in highly interactive scenarios like information visualization. These approaches generally target the point-feature label placement (PFLP) problem, solving only label-label conflicts. This paper presents a new, fast, solid and flexible 2D labeling approach for the PFLP problem that additionally respects other visual elements and the visual extent of labeled features. The results (number of placed labels, processing time) of our particle-based method compare favorably to those of existing techniques. Although the esthetic quality of non-real-time approaches may not be achieved with our method, it complies with practical demands and thus supports the interactive exploration of information spaces. In contrast to the known adjacent techniques, the flexibility of our technique enables labeling of dense point clouds by the use of non-occluding distant labels. Our approach is independent of the underlying visualization technique, which enables us to demonstrate the application of our labeling method within different information visualization scenarios.

Due to complexity, modern visualization techniques for large data volumes and complex interrelationships are difficult to understand for non-expert users and even for expert users the visualization result may be difficult to interpret. Often the limited screen space and the risk of occlusion hinders a meaningful explanation of techniques or datasets by additional visual elements. This paper presents a novel way how views from information visualization can be adapted by the use of the well-known illustrative technique "exploded view", to successfully face the problems described above. The application of exploded views gains screen space for an explanation in a smart way and acts explanatory itself. With our approach of illustrating visual representations, the understanding of complex visualization techniques is eased and new comprehensible views on data are given.

Textual annotations are important elements in all but the simplest visual interfaces. In order to integrate textual annotations smoothly into the dynamic graphical content of interactive information systems, fast yet high-quality label layout algorithms are required. With the ongoing pervasion of mobile applications these requirements are shifted from workstations to comparatively low-performance mobile devices. Fortunately, ubiquitous network access is also on the advance, so that mobile applications can employ remote layout services on external workstations. This paper presents two novel label layout algorithms for relevance-driven dynamic visualizations in interactive information systems. They are employed to generate adaptive visualizations in a mobile maintenance support scenario

The visualization of graphs has proven to be very useful for exploring structures in different application domains. However, in certain fields of computer science, graph visualization is understood and focused quite differently. While "graph drawing" focuses on optimized layouts for node-link-representations of networks, "information visualization" prefers to work on hierarchies focusing on very large structures, different views and interactivity. This paper gives a systematic view of the problem of graph visualization by combining both approaches. We introduce a general view of different visualization methods as well as describe occurring problems and discuss basic constraints. These are used to propose a visualization framework for graphs, whose development motivated this paper

The information revolution is creating and publishing vast data sets, such as records of business transactions, environmental statistics and census demographics. In many application domains, this data is collected and indexed by geo-spatial location. The discovery of interesting patterns in such databases through visual analytics is a key to turn this data into valuable information. Challenges arise because newly available geo-spatial data sets often have millions of records, or even far more, they are from multiple and heterogeneous data sources, and the output devices have significantly changed, e.g. high-resolution pixilated displays are increasingly available in both wall-sized and desktop units. New techniques are needed to cope with this scale. In this paper, we focus on ways to increase the scalability of pixel-based visual interfaces by adding task on hands scenarios that tightly integrate the data analyst into the exploration of geo-spatial data sets

We present interactive visual aids to support the exploration and navigation of graph layouts. They include fisheye tree views and composite lenses. These views provide, in an integrated manner, overview+detail and focus+context. Fisheye tree views are novel applications of the well known fisheye distortion technique. They facilitate the exploration of the hierarchy trees associated with clustered graphs. Composite lenses are the result of the integration of several lens techniques. They facilitate the display of local graph information that may be otherwise difficult to grasp in large and dense graph layouts

In this publication, we apply the concept of foveal imaging to appropriately direct the attention of the viewer during browsing raster image contents. The proposed approaches are based on properties of the human visual system and are rather effective. To meet requirements of mobile environments, we exploit properties of the new and flexible image coding standard JPEG2000 for creation and transmission of the representations. To achieve this, valid data is transcoded from already encoded imagery which is quite fast and requires less bandwidth during image transmission. Regarding the main limitations of mobile devices, the introduced approaches perform much better than traditional techniques, which is mandatory for sophisticated viewer guidance in such environments

This paper introduces a new approach to extend icon-based visualization methods by using a mosaic-based paradigm. We discuss how image metaphors closely related to the application domain can be applied for icon-based representations. Therefore, we enhance visualizations by well-known image mosaic techniques, such as image layouts, image selection and color adaption. Furthermore, we present the results of our approach by discussing an example of a clustered real-world climate data set.

The visual analysis of time dependent data is an essential task in many application fields. However, visualizing large time dependent data collected within a spatial context is still a challenging task. In this paper, we therefore describe an approach for visualizing spatio-temporal data on maps. The approach is based on two commonly used concepts: 3D information visualization and information hiding. These concepts are realized by means of novel embeddings of 3D icons into a map display for representing spatio-temporal data, and an integration of event-based methods for reducing the amount of information to be represented. Our approach is capable of visualizing multiple time dependent attributes on maps, and of emphasizing the characteristics constituted by either linear or cyclic temporal dependencies.

Impostors are image-based primitives commonly used to replace complex geometry in order to reduce the rendering time needed for displaying complex scenes. However, a big problem is the huge amount of memory required for impostors. This paper presents an algorithm that automatically places impostors into a scene so that a desired frame rate and image quality is always met, while at the same time not requiring enormous amounts of impostor memory. The low memory requirements are provided by a new placement method and through the simultaneous use of other acceleration techniques like visibility culling and geometric levels of detail.

The effective visual exploration of large and complexly structured, abstract data requires sophisticated and interactive visualization techniques. Development of these techniques is the major discipline in information visualization. On the other hand, visualization of geospatial data is an important topic in cartography. The necessity to combine expertise from both fields has long been commonly recognized. In this paper, some considerations on the combination of arbitrary multivariate data visualizations, focus & context interaction techniques and thematic map displays are discussed that will allow the efficient combination of established techniques from both information visualization and cartography.

Visualization of large data sets is a demanding task, especially if different users are interested in different aspects of the same data set. Today's visualization techniques often do not distinguish different aspects and thus visualize all aspects of the data, which leads to overcrowded and cluttered representations. Moreover, users are provided with information irrelevant to them. In this paper we describe an event-based approach for visualizing large data sets. The basic idea is to let users describe interesting aspects of the data by means of events and then adapt the visual representation of the data on occurrence of events. We present a formal description of events, assuming that the data are given in relational form. Furthermore, a model for event-based visualization is developed. A brief overview of an early realization of the model along with first visual results is part of this paper.

Image transmission with JPEG2000 can be speeded up by using regions of interest (RoIs). By such a mechanism, parts of an image not belonging to RoIs can be transmitted later or encoded at a lower bit rate. This paper describes a flexible dynamic RoI-scheme which supports the definition and handling of arbitrarily-shaped RoIs in JPEG2000. The scheme supports the dynamic definition and prioritization of new and existing RoIs at any time during image transmission. This can be done without having to decode and re-encode the JPEG2000 datastream. To reach this, we use the flexible precinct/layer-mechanism of the standard. The proposed scheme is fully compliant with the JPEG2000-baseline-codec.

The exploration of heterogenous information spaces requires suitable mining methods as well as effective visual interfaces. Most of the existing systems concentrate either on mining algorithms or on visualization techniques. This paper describes a flexible framework for visual data mining which combines analytical and visual methods to achieve a better understanding of the information space. We provide several pre-processing methods for unstructured information spaces, such as a flexible hierarchy generation with user-controlled refinement. Moreover, we develop new visualization techniques, including an intuitive focus+context technique to visualize complex hierarchical graphs. A special feature of our system is a new paradigm for visualizing information structures within their frame of reference.

Image transmission over low-bandwidth channels can be speeded up if the image coding mechanism supports regions of interest (RoIs). By such a scheme, image parts not belonging to RoIs can be encoded at a lower bitrate. This paper describes a flexible dynamic RoI scheme which supports the definition of arbitrarily-shaped RoIs before the start of the transmission process and the refinement of a partially transmitted image by the definition of new RoIs at any time during image transmission. The overhead to represent RoIs is kept as small as possible by exploiting a polygon-based RoI representation. Span arithmetic on a multiresolution grid is used to support the redundancy-free transmission of overlapping RoIs.