Lars Grammel

“The amount of data that is available to us is ever increasing, and thus is the potential to extract information from it. Information visualization, which leverages our perceptual system to enable us to perceive patterns, outliers, trends and anomalies in large amounts of data, is an important technique for exploratory data analysis. As part of a flexible visual data analysis process, the user needs to construct and parametrize visualizations, which is challenging for novice users. In this thesis, I explore how information visualization novices can be supported in visualization construction. First, I identify existing visualization construction approaches in a systematic literature survey and examine their use cases. Second, I conduct a laboratory study to learn about the process and the characteristics of how information visualization novices construct visualization during data analysis. Third, I identify natural language visualization queries as a promising alternative specification approach that I study by analyzing the queries from the laboratory experiment and by conducting an online survey study. Based on my findings, I propose a descriptive model of natural language visualization queries. Fourth, I derive guidelines for visualization construction tools from my studies and from related work. Finally, I show how these guidelines can be applied to existing visualization tools using the example of the Choosel visualization framework.”

“It remains challenging for information visualization novices to rapidly construct visualizations during exploratory data analysis. We conducted an exploratory laboratory study in which information visualization novices explored fictitious sales data by communicating visualization specifications to a human mediator, who rapidly constructed the visualizations using commercial visualization software. We found that three activities were central to the iterative visualization construction process: data attribute selection, visual template selection, and visual mapping specification. The major barriers faced by the participants were translating questions into data attributes, designing visual mappings, and interpreting the visualizations. Partial specification was common, and the participants used simple heuristics and preferred visualizations they were already familiar with, such as bar, line and pie charts. From our observations, we derived abstract models that describe barriers in the data exploration process and uncovered how information visualization novices think about visualization specifications. Our findings support the need for tools that suggest potential visualizations and support iterative refinement, that provide explanations and help with learning, and that are tightly integrated into tool support for the overall visual analytics process.”

“This paper presents a review of six mashup makers from an End User Development (EUD) perspective. The fast-paced development of mashup makers and related research in the last two years has created a wealth of features and approaches. To provide an overview of EUD support in current state-of-the art mashup makers, we explore, summarize and compare their features across six different themes (Levels of Abstraction, Learning Support, Community Support, Searchability, UI Design and Software Engineering Techniques). We found that the mashup makers provide many features to support end users, but there is still much room for further improvement. These results can be used to guide both research and tool design. User studies that reveal the difficulties in using mashup makers and comparisons of different notations are likely to be especially fruitful research opportunities.”

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