Rainer Splechtna

Comprehensive calibration and registration procedures for augmented reality

Augmented Reality - best described as adding computer-generated virtual content to the real environment – needs more adjustments to work properly than immersive virtual environments. To be perceived as an augmentation of reality, the virtual environment has to be properly aligned to the real world. This registration process has to be done at least once for every hardware set-up, but may have to be repeated in part or completely for each user, prop or device to be included both in the real and the virtual world. In this paper, we propose a comprehensive process for registration and calibration tasks necessary to implement correct augmentation. This includes procedures for calibrating projective and head-mounted displays, tracking systems, tracked input devices and props. Our method unifies the necessary tasks of world-toaugmentation alignment, display calibration and registration of tracked and static props in one, interactive set-up process, which can easily be conducted by the untrained user.

Visual Analytics for Complex Engineering Systems: Hybrid Visual Steering of Simulation Ensembles

In this paper we propose a novel approach to hybrid visual steering of simulation ensembles. A simulation ensemble is a collection of simulation runs of the same simulation model using different sets of control parameters. Complex engineering systems have very large parameter spaces so a naïve sampling can result in prohibitively large simulation ensembles. Interactive steering of simulation ensembles provides the means to select relevant points in a multi-dimensional parameter space (design of experiment). Interactive steering efficiently reduces the number of simulation runs needed by coupling simulation and visualization and allowing a user to request new simulations on the fly. As system complexity grows, a pure interactive solution is not always sufficient. The new approach of hybrid steering combines interactive visual steering with automatic optimization. Hybrid steering allows a domain expert to interactively (in a visualization) select data points in an iterative manner, approximate the values in a continuous region of the simulation space (by regression) and automatically find the “best” points in this continuous region based on the specified constraints and objectives (by optimization). We argue that with the full spectrum of optimization options, the steering process can be improved substantially. We describe an integrated system consisting of a simulation, a visualization, and an optimization component. We also describe typical tasks and propose an interactive analysis workflow for complex engineering systems. We demonstrate our approach on a case study from automotive industry, the optimization of a hydraulic circuit in a high pressure common rail Diesel injection system.

Towards Quantitative Visual Analytics with Structured Brushing and Linked Statistics

Until now a lot of visual analytics predominantly delivers qualitative results—based, for example, on a continuous color map or a detailed spatial encoding. Important target applications, however, such as medical diagnosis and decision making, clearly benefit from quantitative analysis results. In this paper we propose several specific extensions to the well‐established concept of linking&brushing in order to make the analysis results more quantitative. We structure the brushing space in order to improve the reproducibility of the brushing operation, e.g., by introducing the percentile grid. We also enhance the linked visualization with overlaid descriptive statistics to enable a more quantitative reading of the resulting focus+context visualization. Additionally, we introduce two novel brushing techniques: the percentile brush and the Mahalanobis brush. Both use the underlying data to support statistically meaningful interactions with the data. We illustrate the use of the new techniques in the context of two case studies, one based on meteorological data and the other one focused on data from the automotive industry where we evaluate a shaft design in the context of mechanical power transmission in cars.

ITEA--interactive trajectories and events analysis: exploring sequences of spatio-temporal events in movement data

Widespread use of GPS and similar technologies makes it possible to collect extensive amounts of trajectory data. These data sets are essential for reasonable decision making in various application domains. Additional information, such as events taking place along a trajectory, makes data analysis challenging, due to data size and complexity. We present an integrated solution for interactive visual analysis and exploration of events along trajectories data. Our approach supports analysis of event sequences at three different levels of abstraction, namely spatial, temporal, and events themselves. Customized views as well as standard views are combined to form a coordinated multiple views system. In addition to trajectories and events, we include on-the-fly derived data in the analysis. We evaluate our integrated solution using the IEEE VAST 2015 Challenge data set. A successful detection and characterization of malicious activity indicate the usefulness and efficiency of the presented approach.

Interactive visual steering of hierarchical simulation ensembles

Multi-level simulation models, i.e., models where different components are simulated using sub-models of varying levels of complexity, belong to the current state-of-the-art in simulation. The existing analysis practice for multi-level simulation results is to manually compare results from different levels of complexity, amounting to a very tedious and error-prone, trial-and-error exploration process. In this paper, we introduce hierarchical visual steering, a new approach to the exploration and design of complex systems. Hierarchical visual steering makes it possible to explore and analyze hierarchical simulation ensembles at different levels of complexity. At each level, we deal with a dynamic simulation ensemble - the ensemble grows during the exploration process. There is at least one such ensemble per simulation level, resulting in a collection of dynamic ensembles, analyzed simultaneously. The key challenge is to map the multi-dimensional parameter space of one ensemble to the multi-dimensional parameter space of another ensemble (from another level). In order to support the interactive visual analysis of such complex data we propose a novel approach to interactive and semi-automatic parameter space segmentation and comparison. The approach combines a novel interaction technique and automatic, computational methods - clustering, concave hull computation, and concave polygon overlapping - to support the analysts in the cross-ensemble parameter space mapping. In addition to the novel parameter space segmentation we also deploy coordinated multiple views with standard plots. We describe the abstract analysis tasks, identified during a case study, i.e., the design of a variable valve actuation system of a car engine. The study is conducted in cooperation with experts from the automotive industry. Very positive feedback indicates the usefulness and efficiency of the newly proposed approach.

Interactive Mixed Brushing: Integrated Text and Visual Based Data Exploration

Linking and brushing is an essential technique for interactive data exploration and analysis that leverages coordinated multiple views to identify, select, and combine data points of interest. We propose to augment this technique by directly exploring data space using textual queries. Textual and visual queries are freely combined and modified during the data exploration process. Visual queries are used to refine the results of textual queries and vice versa. This mixed brushing integrates procedural, textual, and visual based data exploration to provide a unified approach to brushing. We also propose an interface --- the Text Query Browser View, that allows users to specify and edit data queries as well as to browse the data query history. Further, we argue why an interactive, on-demand, data aggregation and derivation is necessary, and we provide a flexible mechanism that supports it. We have implemented the proposed approach within an existing visualization tool using a client-server architecture. The approach was illustrated and evaluated using two example data sets.

Cross-table linking and brushing: interactive visual analysis of multiple tabular data sets

Studying complex problems often requires identifying and exploring connections and dependencies among several, seemingly unrelated, data sets. Those data sets are often represented as data tables. We propose a novel approach to studying such data sets using linking and brushing across multiple data tables in a coordinated multiple views system. We first identify possible mappings from a subset of one data set to a subset of another data set. That collection of mappings is then used to specify linking among data sets and to support brushing across data sets. Brushing in one data set is then mapped to a brush in the destination data set. If the brush is refined in the destination data set, the inverse mapping, or a back-link, is used to determine the refined brush in the original data set. Brushing and back-links make it possible to efficiently create and analyze complex queries interactively in an iterative process. That process is further supported by a user interface that keeps track of the mappings, links and brushes. The proposed approach is evaluated using three data sets.

Bus Lines Explorer: Interactive Exploration of Public Transportation Data

Public transportation movement data provide a wealth of information and insights into many aspects of urban life and human behavior. However, huge amounts of raw data, coupled with incomplete or inconsistent records, may turn into an obstacle for the effective use of the available information. The need for effective movement data analysis has resulted in a large number of visual analytics tools and specialized views. There are still many challenges in public transportation and other kinds of cyclic movement data analysis. In this paper we address some of those challenges by presenting an improvement of the standard map view. This improved view is specifically designed to simplify and make the visual analysis of complex movement data easier to perform, especially when integrated in a coordinated multiple views tool and articulated together with other techniques. We illustrate the effectiveness of the view on public transportation data from Bahía Blanca, Argentina.

Exploring trajectory data using ComVis CMV tool VAST 2015 Mini-Challenge 1

We describe our analysis of VAST 2015 Mini-Challenge 1 data using a coordinated multiple views tool — ComVis [2]. We extend the ComVis data model [1] to support trajectories with events. We also improve user interaction and introduce a new view in order to support analysis. The data is processed so that it follows the new data model and fits in a single table. We answer some of the challenge questions and plan further research on data exploration and analysis based on the newly introduced data model, interaction, and views.

Interactive interaction plot

Design of experiments (DOE) is the study of how to vary control parameters to efficiently design and evaluate experiments. Main effects plot and interaction plot are two data views often used to explore differences between mean values and interactions between the DOE parameters but they are mostly limited to two parameters. We propose a new data view, interactive interaction plot, that supports exploration and analysis of high-dimensional interactions between parameters. The data view is integrated within a coordinated multiple views system. We describe the new data view using an Olympic medals data set. We also describe a case study dealing with initial selection of hybrid vehicle components. Very positive feedback from automotive domain experts demonstrates the usefulness of the newly proposed approach.