We present a system to analyze time-series data in sensor networks. Our
Color is one of the most important visual variables since it can be combined with any other visual mapping to encode information without using additional space on the display. Encoding one or two dimensions with color is widely explored and discussed in the field. Also mapping multi-dimensional data to color is applied in a vast number of applications, either to indicate similar, or to discriminate between different elements or (multi-dimensional) structures on the screen. A variety of 2D colormaps exists in literature, covering a large variance with respect to different perceptual aspects. Many of the colormaps have a different perspective on the underlying data structure as a consequence of the various analysis tasks that exist for multivariate data. Thus, a large design space for 2D colormaps exists which makes the development and use of 2D colormaps cumbersome. According to our literature research, 2D colormaps have not been subject of in-depth quality assessment. Therefore, we present a survey of static 2D colormaps as applied for information visualization and related fields. In addition, we map seven devised quality assessment measures for 2D colormaps to seven relevant tasks for multivariate data analysis. Finally, we present the quality assessment results of the 2D colormaps with respect to the seven analysis tasks, and contribute guidelines about which colormaps to select or create for each analysis task
In this paper we present a new Focus & Context technique for the exploration of large, abstract graphs. Most Focus & Context techniques present context in a visual way. In contrast, our technique uses a symbolic representation: while the focus is a set of visible nodes, labelled signposts provide cues for the context -off-screen regions of the graph -and indicate the direction of the shortest path linking the visible nodes to these regions. We show how the regions are defined and how they are selected dynamically, depending on the visible nodes. To define the set of visible nodes we use an approach developed by van Ham and Perer that dynamically extracts a subgraph based on an initial focal node and a degree-of-interest function. This approach is extended to support multiple focal nodes. With the symbolic visualization, potentially interesting regions of a graph may be represented with a very small visual footprint. We conclude the paper with an initial user study to evaluate the effectiveness of the signposts for navigation tasks.
The analysis of research data plays a key role in data‐driven areas of science. Varieties of mixed research data sets exist and scientists aim to derive or validate hypotheses to find undiscovered knowledge. Many analysis techniques identify relations of an entire dataset only. This may level the characteristic behavior of different subgroups in the data. Like automatic subspace clustering, we aim at identifying interesting subgroups and attribute sets. We present a visual‐interactive system that supports scientists to explore interesting relations between aggregated bins of multivariate attributes in mixed data sets. The abstraction of data to bins enables the application of statistical dependency tests as the measure of interestingness. An overview matrix view shows all attributes, ranked with respect to the interestingness of bins. Complementary, a node‐link view reveals multivariate bin relations by positioning dependent bins close to each other. The system supports information drill‐down based on both expert knowledge and algorithmic support. Finally, visual‐interactive subset clustering assigns multivariate bin relations to groups. A list‐based cluster result representation enables the scientist to communicate multivariate findings at a glance. We demonstrate the applicability of the system with two case studies from the earth observation domain and the prostate cancer research domain. In both cases, the system enabled us to identify the most interesting multivariate bin relations, to validate already published results, and, moreover, to discover unexpected relations.
Policy design requires the investigation of various data in several design steps for making the right decisions, validating, or monitoring the political environment. The increasing amount of data is challenging for the stakeholders in this domain. One promising way to access the “big data” is by abstracted visual patterns and pictures, as proposed by information visualization. This chapter introduces the main idea of information visualization in policy modeling. First abstracted steps of policy design are introduced that enable the identification of information visualization in the entire policy life-cycle. Thereafter, the foundations of information visualization are introduced based on an established reference model. The authors aim to amplify the incorporation of information visualization in the entire policy design process. Therefore, the aspects of data and human interaction are introduced, too. The foundation leads to description of a conceptual design for social data visualization, and the aspect of semantics plays an important role.
In this paper we analyze different layout algorithms that preserve relative directions in geo-referenced networks. This is an important criterion for many sensor networks such as the electric grid and other supply networks, because it enables the user to match the geographic setting with the drawing on the screen. Even today, the layouts of these networks are often created manually. This is due to the requirement that these layouts must respect geographic references but should still be easy to read and understand. The range of available automatic algorithms spans from general graph layouts over schematic maps to semi-realistic drawings. At first sight, schematics seem to be a promising compromise between geographic correctness and readability. The former property exploits the mental map of the user while the latter makes it easier for the user to learn about the network structure. We investigate different algorithms for such maps together with different visualization techniques. In particular, the group of octi-linear layouts is prominent in handcrafted subway maps. These algorithms have been used extensively to generate drawings for subway maps. Also known as Metro Map layouts, only horizontal, vertical and diagonal directions are allowed. This increases flexibility and makes the resulting layout look similar to the well-known subway maps of large cities. The key difference to general graph layout algorithms is that geographic relations are respected in terms of relative directions. However, it is not clear, whether this metaphor can be transferred from metro maps to other domains. We discuss applicability of these different approaches for geo-based networks in general with the electric grid as a use-case scenario
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