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.

Geometry generators are commonly used in video games and evaluation systems for computer vision to create geometric shapes such as terrains, vegetation or airplanes. The parameters of the generator are often sampled automatically which can lead to many similar or unwanted geometric shapes. In this paper, we propose a novel visual exploration approach that combines the abstract parameter space of the geometry generator with the resulting 3D shapes in a composite visualization. Similar geometric shapes are first grouped using hierarchical clustering and then nested within an illustrative parallel coordinates visualization. This helps the user to study the sensitivity of the generator with respect to its parameter space and to identify invalid parameter settings. Starting from a compact overview representation, the user can iteratively drill-down into local shape differences by clicking on the respective clusters. Additionally, a linked radial tree gives an overview of the cluster hierarchy and enables the user to manually split or merge clusters. We evaluate our approach by exploring the parameter space of a cup generator and provide feedback from domain experts.

Several research projects in the past have built intrusion detection systems and honeypot architectures based on virtual machine introspection (VMI). These systems directly benefit from the use of virtualization technology. The VMI approach, however, requires direct interaction with the virtual machine monitor, and typically is not available to clients of current public clouds. Recently, nested virtualization has gained popularity in research as an approach that could enable cloud customers to use virtualization-based solutions within a cloud by nesting two virtual machine monitors, with the inner one under control of the client. In this paper, we compare the performance of existing nested-virtualization solutions and analyze the impact of the performance overhead on VMI-based intrusion detection and honeypot systems.