Understanding perception is critical to effective visualization design. With its low cost and scalability, crowdsourcing presents an attractive option for evaluating the large design space of visualizations; however, it first requires validation. In this paper, we assess the viability of Amazon’s Mechanical Turk as a platform for graphical perception experiments. We replicate previous studies of spatial encoding and luminance contrast and compare our results. We also conduct new experiments on rectangular area perception (as in treemaps or cartograms) and on chart size and gridline spacing. Our results demonstrate that crowdsourced perception experiments are viable and contribute new insights for visualization design. Lastly, we report cost and performance data from our experiments and distill recommendations for the design of crowdsourced studies. ACM Classification: H5.2 [Information interfaces and presentation]:

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Abstract—We present a nested model for the visualization design and validation with four layers: characterize the task and data in the vocabulary of the problem domain, abstract into operations and data types, design visual encoding and interaction techniques, and create algorithms to execute techniques efficiently. The output from a level above is input to the level below, bringing attention to the design challenge that an upstream error inevitably cascades to all downstream levels. This model provides prescriptive guidance for determining appropriate evaluation approaches by identifying threats to validity unique to each level. We also provide three recommendations motivated by this model: authors should distinguish between these levels when claiming contributions at more than one of them, authors should explicitly state upstream assumptions at levels above the focus of a paper, and visualization venues should accept more papers on domain characterization. Index Terms—Models, frameworks, design, evaluation.

analysis pipeline view of (D) the currently selected lens; (E) a property panel showing details of the currently selected lens. A context menu (F) can be invoked to perform lens-based operations, and (G) the lens toolbar allows quick access to a lens ’ parameters. Abstract—Visual representations of time-series are useful for tasks such as identifying trends, patterns and anomalies in the data. Many techniques have been devised to make these visual representations more scalable, enabling the simultaneous display of multiple variables, as well as the multi-scale display of time-series of very high resolution or that span long time periods. There has been comparatively little research on how to support the more elaborate tasks associated with the exploratory visual analysis of timeseries, e.g., visualizing derived values, identifying correlations, or discovering anomalies beyond obvious outliers. Such tasks typically require deriving new time-series from the original data, trying different functions and parameters in an iterative manner. We introduce a novel visualization technique called ChronoLenses, aimed at supporting users in such exploratory tasks. ChronoLenses perform on-the-fly transformation of the data points in their focus area, tightly integrating visual analysis with user actions, and enabling the progressive construction of advanced visual analysis pipelines.

This project aims at improving the user experience regarding multimedia content annotation. We evaluated and compared current timeline-based annotation tools, so as to elicit user requirements. We address two issues: 1) adapting the user interface, by supporting more input modalities through a rapid prototyping tool and by offering alternative visualization techniques of temporal signals; and 2) covering more steps of the annotation workflow besides the task of annotation itself: notably recording multimodal signals. We developed input devices components for the OpenInterface (OI) platform for rapid prototyping of multimodal interfaces: multitouch screen, jog wheels and pen-based solutions. We modified an annotation tool created with the Smart Sensor Integration (SSI) toolkit and componentized it in OI so as to bind its controls to different input devices. We produced mockups sketches towards a new design of an improved user interface for multimedia content annotation, and started developing a rough prototype using the Processing Development Environment. Our solution allows to produce several prototypes by varying the interaction pipeline: changing input modalities and using either the initial GUI of the annotation tool, or the newly-designed one. We target usability testing to validate our solution and determine which input modalities combination best suits given use cases.

Both policymakers and academics have recently been studying whether graphical, as opposed to numerical, presentation of financial information to investors affects investors ’ decisions. Of particular interest is the usefulness of graphical representations of assets ’ performance via temporal charts of asset prices. Such charts are prevalent in financial media and investment disclosures, and are studied routinely, with the naked eye, by both casual and professional investors. This brings to the forefront a fundamental question: just what information can human beings extract from such charts? Although some anecdotal evidence has suggested that humans cannot even distinguish price charts from charts generated via a synthetic random walk, to our knowledge the above question has not previously been addressed scientifically. We make a step towards answering this question by running an experiment to test whether human subjects can differentiate between actual vs. randomized asset returns. Our experiment consists of an online video game

Ephemeral highlighting uses the temporal dimension to draw the user's attention to specific interface elements through a combination of abrupt onset and gradual fade-in. This technique has shown promise in adaptive interfaces, but has not been tested as a dynamic visual encoding to support information visualization. We conducted a study with 32 participants using subgraph highlighting to support path tracing in node-link graphs, a task abstracting a large class of visual queries. The study compared multiple highlighting techniques, including traditional static highlighting (using color and size), ephemeral highlighting (where the subgraph is emphasized by appearing first, and the rest of the graph fades in gradually), and a combination of static and ephemeral. The combination was the most effective visual cue: it always performed at least as well or better than static highlighting. Ephemeral on its own was sometimes faster than the combined technique, but it was also more error prone. Self-reported workload and preference followed these performance results. Author Keywords Information visualization, subgraph highlighting, path tracing,

The need for pattern discovery in long time-series data led researchers to develop interactive visualization tools and analytical algorithms for gaining insight into the data. Most of the literature on time-series data visualization either focus on a small number of tasks or a specific domain. We propose KronoMiner, a tool that embeds new interaction and visualization techniques as well as analytical capabilities for the visual exploration of time-series data. The interface’s design has been iteratively refined based on feedback from expert users. Qualitative evaluation with an expert user not involved in the design process indicates that our prototype is promising for further research.