Carlos Eduardo Scheidegger

VisTrails: visualization meets data management

Scientists are now faced with an incredible volume of data to analyze. To successfully analyze and validate various hypothesis, it is necessary to pose several queries, correlate disparate data, and create insightful visualizations of both the simulated processes and observed phenomena. Often, insight comes from comparing the results of multiple visualizations. Unfortunately, today this process is far from interactive and contains many error-prone and time-consuming tasks. As a result, the generation and maintenance of visualizations is a major bottleneck in the scientific process, hindering both the ability to mine scientific data and the actual use of the data. The VisTrails system represents our initial attempt to improve the scientific discovery process and reduce the time to insight. In VisTrails, we address the problem of visualization from a data management perspective: VisTrails manages the data and metadata of a visualization product. In this demonstration, we show the power and flexibility of our system by presenting actual scenarios in which scientific visualization is used and showing how our system improves usability, enables reproducibility, and greatly reduces the time required to create scientific visualizations.

VisTrails: enabling interactive multiple-view visualizations

VisTrails is a new system that enables interactive multiple-view visualizations by simplifying the creation and maintenance of visualization pipelines, and by optimizing their execution. It provides a general infrastructure that can be combined with existing visualization systems and libraries. A key component of VisTrails is the visualization trail (vistrail), a formal specification of a pipeline. Unlike existing dataflow-based systems, in VisTrails there is a clear separation between the specification of a pipeline and its execution instances. This separation enables powerful scripting capabilities and provides a scalable mechanism for generating a large number of visualizations. VisTrails also leverages the vistrail specification to identify and avoid redundant operations. This optimization is especially useful while exploring multiple visualizations. When variations of the same pipeline need to be executed, substantial speedups can be obtained by caching the results of overlapping subsequences of the pipelines. In this paper, we describe the design and implementation of VisTrails, and show its effectiveness in different application scenarios.

Managing rapidly-evolving scientific workflows

We give an overview of VisTrails, a system that provides an infrastructure for systematically capturing detailed provenance and streamlining the data exploration process. A key feature that sets VisTrails apart from previous visualization and scientific workflow systems is a novel action-based mechanism that uniformly captures provenance for data products and workflows used to generate these products. This mechanism not only ensures reproducibility of results, but it also simplifies data exploration by allowing scientists to easily navigate through the space of workflows and parameter settings for an exploration task.

Querying and Creating Visualizations by Analogy

While there have been advances in visualization systems, particularly in multi-view visualizations and visual exploration, the process of building visualizations remains a major bottleneck in data exploration. We show that provenance metadata collected during the creation of pipelines can be reused to suggest similar content in related visualizations and guide semi-automated changes. We introduce the idea of query-by-example in the context of an ensemble of visualizations, and the use of analogies as first-class operations in a system to guide scalable interactions. We describe an implementation of these techniques in VisTrails, a publicly-available, open-source system.

Managing the Evolution of Dataflows with VisTrails

Scientists are now faced with an incredible volume of data to analyze. To successfully analyze and validate various hypotheses, it is necessary to pose several queries, correlate disparate data, and create insightful visualizations of both the simulated processes and observed phenomena. Data exploration through visualization requires scientists to go through several steps. In essence, they need to assemble complex workflows that consist of dataset selection, specification of series of operations that need to be applied to the data, and the creation of appropriate visual representations, before they can finally view and analyze the results. Often, insight comes from comparing the results of multiple visualizations that are created during the data exploration process.

Multilevel agglomerative edge bundling for visualizing large graphs

Graphs are often used to encapsulate relationships between objects. Node-link diagrams, commonly used to visualize graphs, suffer from visual clutter on large graphs. Edge bundling is an effective technique for alleviating clutter and revealing high-level edge patterns. Previous methods for general graph layouts either require a control mesh to guide the bundling process, which can introduce high variation in curvature along the bundles, or all-to-all force and compatibility calculations, which is not scalable. We propose a multilevel agglomerative edge bundling method based on a principled approach of minimizing ink needed to represent edges, with additional constraints on the curvature of the resulting splines. The proposed method is much faster than previous ones, able to bundle hundreds of thousands of edges in seconds, and one million edges in a few minutes.

Querying and re-using workflows with VsTrails

We show how work flow systems can be augmented to leverage provenance information to enhance usability. In particular, we will demonstrate new mechanisms and intuitive user interfaces designed to allow users to query work flows by example and to refine work flows by analogies. These techniques are implemented in VisTrails, an open-source provenance-enabled scientific work flow system that can be combined with a wide range of tools, libraries, and visualization systems. We will show di erent scenarios where these techniques can be used to simplify the notoriously hard tasks of creating and refining work flows.

VisComplete: Automating Suggestions for Visualization Pipelines

Building visualization and analysis pipelines is a large hurdle in the adoption of visualization and workflow systems by domain scientists. In this paper, we propose techniques to help users construct pipelines by consensus-automatically suggesting completions based on a database of previously created pipelines. In particular, we compute correspondences between existing pipeline subgraphs from the database, and use these to predict sets of likely pipeline additions to a given partial pipeline. By presenting these predictions in a carefully designed interface, users can create visualizations and other data products more efficiently because they can augment their normal work patterns with the suggested completions. We present an implementation of our technique in a publicly-available, open-source scientific workflow system and demonstrate efficiency gains in real-world situations.

Triangulating point set surfaces with bounded error

We introduce an algorithm for constructing a high-quality triangulation directly from Point Set Surfaces. Our algorithm requires no intermediate representation and no post-processing of the output, and naturally handles noisy input data, typically in the form of a set of registered range scans. It creates a triangulation where triangle size respects the geometry of the surface rather than the sampling density of the range scans. Our technique does not require normal information, but still produces a consistent orientation of the triangles, assuming the sampled surface is an orientable two-manifold. Our work is based on using Moving Least-Squares (MLS) surfaces as the underlying representation. Our technique is a novel advancing front algorithm, that bounds the Hausdorff distance to within a user-specified limit. Specifically, we introduce a way of augmenting advancing front algorithms with global information, so that triangle size adapts gracefully even when there are large changes in surface curvature. Our results show that our technique generates high-quality triangulations where other techniques fail to reconstruct the correct surface due to irregular sampling on the point cloud, noise, registration artifacts, and underlying geometric features, such as regions with high curvature gradients.

Direct (Re)Meshing for Efficient Surface Processing

We propose a novel surface remeshing algorithm. While many remeshing algorithms are based on global parametrization or local mesh optimization, our algorithm is closely related to surface reconstruction techniques and it requires no explicit parameterization. Our approach is based on the advancing‐front paradigm, and it can be used to both incrementally remesh the complete surface, or simply to remesh a portion of it with a high‐quality mesh. It is accurate, fast, robust, and suitable for use with interactive mesh processing applications that require local remeshing. We show a number of applications, including matching the resolution of meshes when doing Boolean operations such as unions and intersections. We also show how to adapt the algorithm to blend and merge mixed‐mode objects — for example, to compute the union of a point‐set surface and a triangle mesh.