Dominik Sacha

Knowledge Generation Model for Visual Analytics

Visual analytics enables us to analyze huge information spaces in order to support complex decision making and data exploration. Humans play a central role in generating knowledge from the snippets of evidence emerging from visual data analysis. Although prior research provides frameworks that generalize this process, their scope is often narrowly focused so they do not encompass different perspectives at different levels. This paper proposes a knowledge generation model for visual analytics that ties together these diverse frameworks, yet retains previously developed models (e.g., KDD process) to describe individual segments of the overall visual analytic processes. To test its utility, a real world visual analytics system is compared against the model, demonstrating that the knowledge generation process model provides a useful guideline when developing and evaluating such systems. The model is used to effectively compare different data analysis systems. Furthermore, the model provides a common language and description of visual analytic processes, which can be used for communication between researchers. At the end, our model reflects areas of research that future researchers can embark on.

The Role of Uncertainty, Awareness, and Trust in Visual Analytics

Visual analytics supports humans in generating knowledge from large and often complex datasets. Evidence is collected, collated and cross-linked with our existing knowledge. In the process, a myriad of analytical and visualisation techniques are employed to generate a visual representation of the data. These often introduce their own uncertainties, in addition to the ones inherent in the data, and these propagated and compounded uncertainties can result in impaired decision making. The users confidence or trust in the results depends on the extent of users awareness of the underlying uncertainties generated on the system side. This paper unpacks the uncertainties that propagate through visual analytics systems, illustrates how humans perceptual and cognitive biases influence the users awareness of such uncertainties, and how this affects the users trust building. The knowledge generation model for visual analytics is used to provide a terminology and framework to discuss the consequences of these aspects in knowledge construction and though examples, machine uncertainty is compared to human trust measures with provenance. Furthermore, guidelines for the design of uncertainty-aware systems are presented that can aid the user in better decision making.

Visual Interaction with Dimensionality Reduction: A Structured Literature Analysis

Dimensionality Reduction (DR) is a core building block in visualizing multidimensional data. For DR techniques to be useful in exploratory data analysis, they need to be adapted to human needs and domain-specific problems, ideally, interactively, and on-the-fly. Many visual analytics systems have already demonstrated the benefits of tightly integrating DR with interactive visualizations. Nevertheless, a general, structured understanding of this integration is missing. To address this, we systematically studied the visual analytics and visualization literature to investigate how analysts interact with automatic DR techniques. The results reveal seven common interaction scenarios that are amenable to interactive control such as specifying algorithmic constraints, selecting relevant features, or choosing among several DR algorithms. We investigate specific implementations of visual analysis systems integrating DR, and analyze ways that other machine learning methods have been combined with DR. Summarizing the results in a “human in the loop” process model provides a general lens for the evaluation of visual interactive DR systems. We apply the proposed model to study and classify several systems previously described in the literature, and to derive future research opportunities.

Feature-driven visual analytics of soccer data

Soccer is one the most popular sports today and also very interesting from an scientific point of view. We present a system for analyzing high-frequency position-based soccer data at various levels of detail, allowing to interactively explore and analyze for movement features and game events. Our Visual Analytics method covers single-player, multi-player and event-based analytical views. Depending on the task the most promising features are semi-automatically selected, processed, and visualized. Our aim is to help soccer analysts in finding the most important and interesting events in a match. We present a flexible, modular, and expandable layer-based system allowing in-depth analysis. The integration of Visual Analytics techniques into the analysis process enables the analyst to find interesting events based on classification and allows, by a set of custom views, to communicate the found results. The feedback loop in the Visual Analytics pipeline helps to further improve the classification results. We evaluate our approach by investigating real-world soccer matches and collecting additional expert feedback. Several use cases and findings illustrate the capabilities of our approach.

Visual-Interactive Search for Soccer Trajectories to Identify Interesting Game Situations.

Recently, sports analytics has turned into an important research area of visual analytics and may provide interesting findings, such as the best player of the season, for various kinds of sports. Soccer is a very popular and tactical game, which also attracted great attention in the last few years. However, the search for complex game movements is a very crucial and challenging task. We present a system for searching trajectory data in soccer matches by means of an interactive search interface that enables the user to sketch a situation of interest. Furthermore, we apply a domain specific prefiltering process to extract a set of local movement segments, which are similar to a given sketch. Our approach comprises single-trajectory, multi-trajectory, and event-specific search functions based on two different similarity measures. To demonstrate the usefulness of our approach, we define a domain specific task analysis and conduct a case study together with a domain expert from FC Bayern München by investigating a real-world soccer match. Finally, we show that multi-trajectory search in combination with event-specific filtering is needed to describe and retrieve complex moves in soccer matches.

Dynamic Visual Abstraction of Soccer Movement

Trajectory‐based visualization of coordinated movement data within a bounded area, such as player and ball movement within a soccer pitch, can easily result in visual crossings, overplotting, and clutter. Trajectory abstraction can help to cope with these issues, but it is a challenging problem to select the right level of abstraction (LoA) for a given data set and analysis task. We present a novel dynamic approach that combines trajectory simplification and clustering techniques with the goal to support interpretation and understanding of movement patterns. Our technique provides smooth transitions between different abstraction types that can be computed dynamically and on‐the‐fly. This enables the analyst to effectively navigate and explore the space of possible abstractions in large trajectory data sets. Additionally, we provide a proof of concept for supporting the analyst in determining the LoA semi‐automatically with a recommender system. Our approach is illustrated and evaluated by case studies, quantitative measures, and expert feedback. We further demonstrate that it allows analysts to solve a variety of analysis tasks in the domain of soccer.

Enhancing Parallel Coordinates: Statistical Visualizations for Analyzing Soccer Data.

Visualizing multi-dimensional data in an easy and interpretable way is one of the key features of Parallel Coordinate Plots. However, limitations as overplotting or missing density informations have resulted in many enhancements proposed for Parallel Coordinates. In this paper, we will include density information along each axis for clustered data. The main idea is to visually represent the density distribution of each cluster along the axes. We will show the applicability of our method by analyzing the activity phases of professional soccer players. A final discussion and conclusion will complement this paper.

Analytic behavior and trust building in visual analytics

Visual Analytics (VA) is a collaborative process between human and computer, where analysts are performing numerous interactions and reasoning activities. This paper presents our current progress in developing a note taking environment (NTE) that can be plugged to any VA system. The NTE supports the analysis process on the one hand, and captures user interactions on the other hand. Our aim is to integrate human lower- (exploration) with higher- (verification) level analytic processes and to investigate those together related to further human factors, such as trust building. We conducted a user study to collect and investigate analytic provenance data. Our early results reveal that analysis strategies and trust building are very individual. However, we were able to identify significant correlations between trust levels and interactions of particular participants.

Ambient Grids : Maintain Context-Awareness via Aggregated Off-Screen Visualization

When exploring large spatial datasets, zooming and panning interactions often lead to the loss of contextual overview. Existing overview-plus-detail approaches allow users to view context while inspecting details, but they often suffer from distortion or overplotting. In this paper, we present an off-screen visualization method called Ambient Grids that strikes the balance between overview and details by preserving the contextual information as color grids within a designated space around the focal area. In addition, we describe methods to generate Ambient Grids for point data using data aggregation and projection. In a use case, we show the usefulness of our technique in exploring the VAST Challenge 2011 microblog dataset.

Collaborative Data Analysis with Smart Tangible Devices

We present a tangible approach for exploring and comparing multi-dimensional data points collaboratively by combining Sifteo Cubes with glyph visualizations. Various interaction techniques like touching, shaking, moving or rotating the displays support the user in the analysis. Context dependent glyph-like visualization techniques make best use of the available screen space and cube arrangements. As a first proof of concept we apply our approach to real multi-dimensional datasets and show with a coherent use case how our techniques can facilitate the exploration and comparison of data points. Finally, further research directions are shown when combining Sifteo Cubes with glyphs and additional context information provided by multi-touch tables.