Parallel sets: interactive exploration and visual analysis of categorical data

Categorical data dimensions appear in many real-world data sets, but few visualization methods exist that properly deal with them. Parallel Sets are a new method for the visualization and interactive exploration of categorical data that shows data frequencies instead of the individual data points. The method is based on the axis layout of parallel coordinates, with boxes representing the categories and parallelograms between the axes showing the relations between categories. In addition to the visual representation, we designed a rich set of interactions. Parallel Sets allow the user to interactively remap the data to new categorizations and, thus, to consider more data dimensions during exploration and analysis than usually possible. At the same time, a metalevel, semantic representation of the data is built. Common procedures, like building the cross product of two or more dimensions, can be performed automatically, thus complementing the interactive visualization. We demonstrate Parallel Sets by analyzing a large CRM data set, as well as investigating housing data from two US states.     

An ultraprecision six-axis visual servo-control system

This paper presents the development of a novel active visual measurement technique, laterally sampled white-light interferometry (L-SWLI), which is capable of real-time visual tracking of six-degree-of-freedom (6-DOF) rigid body motion with near-nanometer precision. The visual tracking system is integrated with a 6-DOF motion stage to realize an ultraprecision six-axis visual servo-control system. Contrary to conventional interferometric techniques, L-SWLI obtains the complete pose of the target object from a single image frame, therefore allowing real-time tracking. Six-DOF motions are obtained from measuring the fringe pattern on multiple surfaces of the object or from a single surface with additional information gained from conventional image-processing techniques. The feasibility of the visually servoed motion scheme was demonstrated on a micro cantilever. The cantilever was maneuvered in a three-dimensional space with near-nanometer motion resolution in all three translational axes.     

Enabling advanced environmental management via remote and distributed visual data exploration and analysis

We present a methodology that has proven useful in surmounting technical obstacles encountered while producing mobile-enabled, remote visual data exploration and analysis capabilities. Our results focus on a environmental remediation case study. Using a desktop or mobile device, a scientist is able to perform subset selection of a large collection of time-varying observational, well-log data, then to view this data in 3D from arbitrary orientations, and to view output from visualizations of a time-varying 3D fluid flow simulation showing the spread of analytes over time through the same spatial region as the observational data. This infrastructure is capable of delivering imagery to handheld devices at (modestly) interactive rates, supports a variety of different types of mobile devices, and is useful to scientists who may need to access this kind of capability from field locations.     

From uncertainty to visual exploration

The authors attempt to determine what can be inferred from ambiguity in processes of visual interpretation. They discuss this question in a specific context: the interpretation of scene geometry in the form of parametrized volumetric models. Ambiguity is described as a local probabilistic property of the misfit error surface in the parameter space of superellipsoid models, namely, as an ellipsoid of confidence in which there is a given probability that the true parameters can be found. The authors show how to project the ellipsoid of confidence back into 3D space to obtain the shell in which the true 3D surface most probably lies and introduce what they call the uncertainty as a local property of the fitted model's surface. They propose a technique that can use this information to plan a new direction of view that minimizes the ambiguity of subsequent interpretation     

Provenance and annotation for visual exploration systems

Exploring data using visualization systems has been shown to be an extremely powerful technique. However, one of the challenges with such systems is an inability to completely support the knowledge discovery process. More than simply looking at data, users will make a semipermanent record of their visualizations by printing out a hard copy. Subsequently, users will mark and annotate these static representations, either for dissemination purposes or to augment their personal memory of what was witnessed. In this paper, we present a model for recording the history of user explorations in visualization environments, augmented with the capability for users to annotate their explorations. A prototype system is used to demonstrate how this provenance information can be recalled and shared. The prototype system generates interactive visualizations of the provenance data using a spatio-temporal technique. Beyond the technical details of our model and prototype, results from a controlled experiment that explores how different history mechanisms impact problem solving in visualization environments are presented     

基于笔式界面的交互式可视化分析系统

当前可视化分析软件对于人机交互的支持不足,造成分析推理过程效率较低。针对此问题,设计并实现了一个笔式可视化分析系统。首先分析了可视化分析过程的交互任务,然后提出了笔式可视化分析系统的体系结构,研究了基于草图的自适应布局算法,并对分析过程中的交互方式进行设计。最后给出了该系统的应用实例,可用性实验评估结果表明,该系统可以为可视化分析提供一种自然、高效的支持工具。     

INDIANA: An interactive system for assisting database exploration

We propose INDIANA, a system conceived to support a novel paradigm of database exploration. INDIANA assists the users who are interested in gaining insights about a database though an interactive and incremental process, like a conversation that does not happen in natural language. During this process, the system iteratively provides the user with some features of the data that might be “interesting” from the statistical viewpoint, receiving some feedbacks that are later used by the system to refine the features provided to the user in the next step. A key ability of INDIANA is to assist “data enthusiastic” users (i.e., inexperienced or casual users) in the exploration of transactional databases in an interactive way. For this purpose, we develop a number of novel, statistically-grounded algorithms to support the interactive exploration of the database. We report an in-depth experimental evaluation to show that the proposed system guarantees a very good trade-off between accuracy and scalability, and a user study that supports the claim that the system is effective in real-world database-exploration tasks.     

Augmenting process elicitation with visual priming: An empirical exploration of user behaviour and modelling outcomes

Business process models have become an effective way of examining business practices to identify areas for improvement. While common information gathering approaches are generally efficacious, they can be quite time consuming and have the risk of developing inaccuracies when information is forgotten or incorrectly interpreted by analysts. In this study, the potential of a role-playing approach to process elicitation and specification has been examined. This method allows stakeholders to enter a virtual world and role-play actions similarly to how they would in reality. As actions are completed, a model is automatically developed, removing the need for stakeholders to learn and understand a modelling grammar. An empirical investigation comparing both the modelling outputs and participant behaviour of this virtual world role-play elicitor with an S-BPM process modelling tool found that while the modelling approaches of the two groups varied greatly, the virtual world elicitor may not only improve both the number of individual process task steps remembered and the correctness of task ordering, but also provide a reduction in the time required for stakeholders to model a process view.     

From visual data exploration to visual data mining: a survey

We survey work on the different uses of graphical mapping and interaction techniques for visual data mining of large data sets represented as table data. Basic terminology related to data mining, data sets, and visualization is introduced. Previous work on information visualization is reviewed in light of different categorizations of techniques and systems. The role of interaction techniques is discussed, in addition to work addressing the question of selecting and evaluating visualization techniques. We review some representative work on the use of information visualization techniques in the context of mining data. This includes both visual data exploration and visually expressing the outcome of specific mining algorithms. We also review recent innovative approaches that attempt to integrate visualization into the DM/KDD process, using it to enhance user interaction and comprehension.     

An industrial visual inspection system that uses inductive learning

This paper presents an industrial visual inspection system that uses inductive learning. The system employs RULES-3 inductive learning algorithm to extract the necessary set of rules and template matching technique to process an image. Twenty 3×3 masks are used to represent an image. Each example consists of 20 frequencies of each mask. The system was tested on five different types of tea or water cups in order to classify the good and bad items. The system was trained using five good cups and then tested for 113 unseen examples. The results obtained showed the high performance of the system: the efficiency of the system for correctly classifying unseen examples was 100%. The system can also decide what type of the cup is being processed.     

Data processing software for electrophysiological visual exploration

The software we have developed originated after careful study of a routine functional visual processing session. It has three objectives: (i) to have available a large library of data processing programs; (ii) to add to these electrophysiological results, automatically transferred into a file, a clinical record card, creating a personalized file for study of visual characteristics of each patient; (iii) to allow programs providing statistical studies, creation of groups based on the same clinical features and various comparisons between files or groups. The database now contains 5000 different files and the results of its use are very encouraging. Some results are presented, especially those of a study on multiple sclerosis. The goal of the operation is the creation, in the future, of an expert system.     

Interactive and iterative visual exploration of knowledge graphs based on shareable and reusable visual configurations

Knowledge graphs denote structured data which represent entities and relationships between them in a form of a graph, often expressed in the RDF data model. It may be hard for lay users to explore existing knowledge graphs, especially when graphs from different data sources need to be integrated. In this paper, we present an approach to knowledge graph visual exploration based on the concept of shareable and reusable visual configurations. A visual configuration comprises domain specific views on a knowledge graph which define operations such as node detail or expansion. These operations are easy to understand for lay users who can use them to explore a graph while complexities unnecessary in a given application context remain hidden. We introduce an ontology which enables to express and publish visual configurations and reuse their components in other configurations. We also provide an experimental implementation called KGBrowser. We evaluate the proposed approach with real users. We also compare our implementation KGBrowser with other existing tools for knowledge graph visualization and exploration.     

可视分析增强的平行智能交通系统框架

随着人工智能2.0时代的到来,可视分析方法作为一种重要的人机耦合方法受到越发广泛的关注.其是大数据分析的利器,也是理解数据的"导航仪",能够有效地将三元空间结构(CPH)中的数据转换为知识系统中的服务与决策,从而进一步提升交通系统智能化水平.为此,提出人工交通系统、计算实验和平行执行相融合的平行智能交通系统,为智能交通...     

An ubiquitous 3D visual analysis platform of seabed

Multimedia Tools and Applications - We built a ‘virtual-world’ of real seabed for the visual analysis. Sub-bottom profile is imported in the 3D environment.”...     

Deploying Web-based visual exploration tools on the grid

Grid-based computing facilitates access to different resources. But management in a grid-based environment isn't centralized. To use grid resources effectively, researchers need a central access point to manage the resources, provide a visual means to explore the data, and record these explorations for further investigation and dissemination. This article describes such a system that's being developed jointly by the University of California, Davis, and the Lawrence Berkeley National Laboratory (LBNL). The centralized system acts as a portal into grid-enabled visualization systems. Scientists using the portal can focus on the important task of extracting insights from their data through visualization instead of having to worry about process management. Because scientists at LBNL and their collaborators require access to the portal from around the world, the portal's interface is entirely Web-based. Authenticated users only need a standards-compliant Web browser to explore their data from anywhere in the world. The portal provides a Web-based interface not just for exploring but also for encapsulating visualization data. Encapsulating the process lets users reproduce the visualization results for validation or extend those results by continuing data exploration. We discuss the integration of our grid-enabled visualization server, the visualization Web application that performs the visualization session management, and the Web-based interface.     

Coupling geometric analysis and viability theory for system exploration: Application to a living food system

This paper addresses the issue of studying a food complex system in a reverse engineering manner with the aim of identifying the set of all possible actions that makes it reach a quality target with respect to manufacturing constraints. Once the set of actions is identified, several criteria can be considered to identify interesting trajectories and control policies. A viability approach, coupling the viability theory and a geometric approach of robustness, is proposed to study complex dynamical systems. It can be implemented for several types of systems, from linear to non linear or hybrid systems. The proposed framework was adapted to a living food system: a ripening model of Camembert cheese to identify the set of states and actions (capture basin) from which it is possible to reach a predefined quality target. Within the set of viable trajectories, particular trajectories that improve the Camembert cheese ripening process are identified using the proposed approach. The results are applied at a pilot scale and are discussed in this paper.