Markus John

VA 2 : A Visual Analytics Approach for // Evaluating Visual Analytics Applications

Evaluation has become a fundamental part of visualization research and researchers have employed many approaches from the field of human-computer interaction like measures of task performance, thinking aloud protocols, and analysis of interaction logs. Recently, eye tracking has also become popular to analyze visual strategies of users in this context. This has added another modality and more data, which requires special visualization techniques to analyze this data. However, only few approaches exist that aim at an integrated analysis of multiple concurrent evaluation procedures. The variety, complexity, and sheer amount of such coupled multi-source data streams require a visual analytics approach. Our approach provides a highly interactive visualization environment to display and analyze thinking aloud, interaction, and eye movement data in close relation. Automatic pattern finding algorithms allow an efficient exploratory search and support the reasoning process to derive common eye-interaction-thinking patterns between participants. In addition, our tool equips researchers with mechanisms for searching and verifying expected usage patterns. We apply our approach to a user study involving a visual analytics application and we discuss insights gained from this joint analysis. We anticipate our approach to be applicable to other combinations of evaluation techniques and a broad class of visualization applications.

Triangulating user behavior using eye movement, interaction, and think aloud data

In information visualization, evaluation plays a crucial role during the development of a new visualization technique. In recent years, eye tracking has become one means to analyze how users perceive and understand a new visualization system. Since most visualizations are highly interactive nowadays, a study should take interaction, in terms of user-input, into account as well. In addition, think aloud data gives insights into cognitive processes of participants using a visualization system. Typically, researchers evaluate these data sources separately. However, we think it is beneficial to correlate eye tracking, interaction, and think aloud data for deeper analyses. In this paper, we present challenges and possible solutions in triangulating user behavior using multiple evaluation data sources. We describe how the data is collected, synchronized, and analyzed using a string-based and a visualization-based approach founded on experiences from our current research. We suggest methods how to tackle these issues and discuss benefits and disadvantages. Thus, the contribution of our work is twofold. On the one hand, we present our approach and the experiences we gained during our research. On the other hand, we investigate additional methods that can be used to analyze this multi-source data.

Visual Movie Analytics

The analysis of inherent structures of movies plays an important role in studying stylistic devices and specific, content-related questions. Examples are the analysis of personal constellations in movie scenes, dialogue-based content analysis, or the investigation of image-based features. We provide a visual analytics approach that supports the analytical reasoning process to derive higher level insights about the content on a semantic level. Combining automatic methods for semantic scene analysis based on script and subtitle text, we perform a low-level analysis of the data automatically. Our approach features an interactive visualization that allows a multilayer interpretation of descriptive features to characterize movie content. For semantic analysis, we extract scene information from movie scripts and match them with the corresponding subtitles. With text- and image-based query techniques, we facilitate an interactive comparison of different movie scenes on an image and on a semantic level. We demonstrate how our approach can be applied for content analysis on a popular Hollywood movie.

Visual Analytics for Narrative Text - Visualizing Characters and their Relationships as Extracted from Novels

The study of novels and the analysis of their plot, characters and other entities are time-consuming and complex tasks in literary science. The digitization of literature and the proliferation of electronic books provide new opportunities to support these tasks with visual abstractions. Methods from the fields of computational linguistics can be used to automatically extract entities and their relations from digitized novels, which can then be visualized to ease exploration and analysis tasks. This paper presents a web-based approach that combines automatic analysis methods with effective visualization techniques. Different views on the extracted entities are provided and relations between them across the plot are indicated. Two usage scenarios show successful applications of the approach and demonstrate its benefits and limitations.

Visual Analysis and Exploration of Entity Relations in Document Collections

Interactive text visualization can help users explore and gain insights into complex and often large document sets. One popular visualization strategy to represent such collections is to depict each document as a glyph in 2D space. These spaces have proven effective, especially when combined with interactive exploration methods. However, current exploratory approaches are largely limited to single areas of a 2D spatialization, lacking support for important comparative exploration and analysis tasks. In this paper, we extend a flexible focus+context exploration technique to tackle this challenge. In particular, based on practical tasks from the digital humanities, we focus on exploring and investigating relationships between entities in large document collections. Our approach uses natural language processing to extract characters and places, including information about their relationships. We then use linked views to facilitate visual analysis of extracted information artifacts. Based on two usage scenarios, we demonstrate successful applications of the approach and discuss its benefits and limitations.

Visual Analysis of Character and Plot Information Extracted from Narrative Text

The study of novels and the analysis of their plot, characters and other information entities are complex and time-consuming tasks in literary science. The digitization of literature and the proliferation of electronic books provide new opportunities to support these tasks with visual abstractions. Methods from the field of computational linguistics can be used to automatically extract entities and their relations from digitized novels. However, these methods have known limitations, especially when applied to narrative text that does often not follow a common schema but can have various forms. Visualizations can address the limitations by providing visual clues to show the uncertainty of the extracted information, so that literary scholars get a better idea of the accuracy of the methods. In addition, interaction can be used to let users control and adapt the extraction and visualization methods according to their needs. This paper presents ViTA, a web-based approach that combines automatic analysis methods with effective visualization techniques. Different views on the extracted entities are provided and relations between them across the plot are indicated. Two usage scenarios show successful applications of the approach and demonstrate its benefits and limitations. Furthermore, the paper discusses how uncertainty might be represented in the different views and how users can be enabled to adapt the automatic methods.