Visualization of Practices and Metrics (Workpackage 1.2)

Measuring applications is a challenge and one of the goal of the Squale project is to propose a sound quality model. Now presenting the results of such analysis is also a challenge since it is complex to output and present to the user for the following rea- sons: first a lot of data should be presented and at different audience. Second displaying information is one aspect another one is navigating the information. Finally it is im- portant not to overwhelm the users with too much visualizations. This workpackage presents a state of the art in terms of software visualization approaches that are specif- ically designed to display metrics. In addition it sets up the context for the application of such visualization to practices

Automating User-Preferred Camera Placement for Volume Rendered Scientific Visualization

37 pagesCamera placement is essential in the world of scientific visualization. Different camera placements expose different information about the data. Viewpoint quality (VQ) metrics are one method of evaluating the quality of camera placement in visualizations. VQ metrics also have the potential to direct the automation of camera selection in scientific visualization, an important issue as the computational capacity to produce data is far outpacing the capacity to save data to storage. Previous research has used VQ metrics to successfully predict camera placements that match user preferences in images generated by a popular visualization technique, called isosurfacing. With this study, we extend the previous research and investigate the efficacy of VQ metrics in predicting user preferred camera placements for images created using another popular visualization method, known as volume rendering. This study involves two main components: (1) gathering user preferences of camera placements and (2) a performance analysis of how accurately VQ metrics were able to predict user preferences. We found that the top performing VQ metric was able to correctly predict user preferences of volume rendered images up to 66% of the time. This result supports previous findings about the efficacy of VQ metrics in predicting user preferred images generated using isosurfaces. Together, these findings provide further evidence that VQ metrics are a promising approach for guiding the automation of selecting camera placements for scientific visualization methods

CloudHealth: A Model-Driven Approach to Watch the Health of Cloud Services

Cloud systems are complex and large systems where services provided by different operators must coexist and eventually cooperate. In such a complex environment, controlling the health of both the whole environment and the individual services is extremely important to timely and effectively react to misbehaviours, unexpected events, and failures. Although there are solutions to monitor cloud systems at different granularity levels, how to relate the many KPIs that can be collected about the health of the system and how health information can be properly reported to operators are open questions. This paper reports the early results we achieved in the challenge of monitoring the health of cloud systems. In particular we present CloudHealth, a model-based health monitoring approach that can be used by operators to watch specific quality attributes. The CloudHealth Monitoring Model describes how to operationalize high level monitoring goals by dividing them into subgoals, deriving metrics for the subgoals, and using probes to collect the metrics. We use the CloudHealth Monitoring Model to control the probes that must be deployed on the target system, the KPIs that are dynamically collected, and the visualization of the data in dashboards.Comment: 8 pages, 2 figures, 1 tabl

A Stable Greedy Insertion Treemap Algorithm for Software Evolution Visualization

Computing treemap layouts for time-dependent (dynamic) trees is an open problem in information visualization. In particular, the constraints of spatial quality (cell aspect ratio) and stability (small treemap changes mandated by given tree-data changes) are hard to satisfy simultaneously. Most existing treemap methods focus on spatial quality, but are not inherently designed to address stability. We propose here a new treemapping method that aims to jointly optimize both these constraints. Our method is simple to implement, generic (handles any types of dynamic hierarchies), and fast. We compare our method with 14 state of the art treemaping algorithms using four quality metrics, over 28 dynamic hierarchies extracted from evolving software codebases. The comparison shows that our proposal jointly optimizes spatial quality and stability better than existing methods

Drawing Big Graphs using Spectral Sparsification

Spectral sparsification is a general technique developed by Spielman et al. to reduce the number of edges in a graph while retaining its structural properties. We investigate the use of spectral sparsification to produce good visual representations of big graphs. We evaluate spectral sparsification approaches on real-world and synthetic graphs. We show that spectral sparsifiers are more effective than random edge sampling. Our results lead to guidelines for using spectral sparsification in big graph visualization.Comment: Appears in the Proceedings of the 25th International Symposium on Graph Drawing and Network Visualization (GD 2017