We present a new model for creating composite visualizations of multidimensional datasets using simple visual representations such as point charts, scatterplots, and parallel coordinates as components. Each visual representation is contained in a tile, and the tiles are arranged in a mosaic of views using a space-filling slice-and-dice layout. Tiles can be created, resized, split, or merged using a versatile set of interaction techniques, and the visual representation of individual tiles can also be dynamically changed to another representation. Because each tile is self-contained and independent, it can be implemented in any programming language, on any platform, and using any visual representation. We also propose a formalism for expressing visualization mosaics. A web-based implementation called MosaicJS supporting multidimensional visual exploration showcases the versatility of the concept and illustrates how it can be used to integrate visualization components provided by different toolkits.

With more than 4 billion mobile devices in the world today, mobile computing is quickly becoming the universal computational platform of the world. Building on this new wave of mobile devices are personal computing activities such as microblogging, social networking, and photo sharing, which are intrinsically mobile phenomena that occur while on-the-go. Mobility is now propagating to more professional activities such as data analytics, which need no longer be restricted to the workplace. In fact, the rise of big data increasingly demands that we be able to access data resources anytime and anywhere, whether to support decisions and activities for travel, telecommuting, or distributed teamwork. In other words, it is high time to fully realize Mark Weiser’s vision of ubiquitous computing in the realm of data analytics.

Social network analysis (SNA) is becoming increasingly concerned not only with actors and their relations, but also with distinguishing between different types of such entities. For example, social scientists may want to investigate asymmetric relations in organizations with strict chains of command, or incorporate non-actors such as conferences and projects when analyzing co-authorship patterns. Multimodal social networks are those where actors and relations belong to different types, or modes, and multimodal social network analysis (mSNA) is accordingly SNA for such networks. In this paper, we present a design study that we conducted with several social scientist collaborators on how to support mSNA using visual analytics tools. Based on an open-ended, formative design process, we devised a visual representation called parallel node-link bands (PNLBs) that splits modes into separate bands and renders connections between adjacent ones, similar to the list view in Jigsaw. We then used the tool in a qualitative evaluation involving five social scientists whose feedback informed a second design phase that incorporated additional network metrics. Finally, we conducted a second qualitative evaluation with our social scientist collaborators that provided further insights on the utility of the PNLBs representation and the potential of visual analytics for mSNA.

Many 2D visual spaces have a virtually one-dimensional nature with very high aspect ratio between the dimensions: examples include time-series data, multimedia data such as sound or video, text documents, and bipartite graphs. Common among these is that the space can become very large, e.g., temperature measurements could span a long time period, surveillance video could cover entire days or weeks, and documents can have thousands of pages. Many analysis tasks for such spaces require several foci while retaining context and distance awareness. In this extended version of our IEEE PacificVis 2010 paper, we introduce a method for supporting this kind of multi-focus interaction that we call stack zooming. The approach is based on building hierarchies of 1D strips stacked on top of each other, where each subsequent stack represents a higher zoom level, and sibling strips represent branches in the exploration. Correlation graphics show the relation between stacks and strips of different levels, providing context and distance awareness for the foci. The zoom hierarchies can also be used as graphical histories and for communicating insights to stakeholders, and can be further extended with annotation and integrated statistics.

Visual exploration involves using visual representations to investigate data where the goals of the process are unclear and poorly defined. However, this often places unduly high cognitive load on the user, particularly in terms of keeping track of multiple investigative branches, remembering earlier results, and correlating between different views. We propose a new methodology for automatically spatializing the individual steps in visual exploration onto a large visual canvas, allowing users to easily recall, reflect, and assess their progress. We also present a web-based implementation of our methodology called ExPlatesJS where users can manipulate multidimensional data in their browsers, automatically building visual queries as they explore the data.