The Visualization Input Pipeline - Enabling Semantic Interaction in Scientific Visualization

An important issue in scientific visualization systems is the management of data sets. Most data sets in scientific visualization, whether created by measurement or simulation, are usually voluminous. The goal of data management is to reduce the storage space and the access time of these data sets to speed up the visualization process. A new progressive transmission scheme using spline biorthogonal wavelet bases is proposed in this paper. By exploiting the properties of this set of wavelet bases, a fast algorithm involving only additions and subtractions is developed. Due to the multiresolutional nature of the wavelet transform, this scheme is compatible with hierarchical-structured rendering algorithms. The formula for reconstructing the functional values in a continuous volume space is given in a simple polynomial form. Lossless compression is possible, even when using floating-point numbers. This algorithm has been applied to data from a global ocean model. The lossless compression ratio is about 1.5:1. With a compression ratio of 50:1, the reconstructed data is still of good quality. Several other wavelet bases are compared with the spline biorthogonal wavelet bases. Finally the reconstructed data is visualized using various algorithms and the results are demonstrated. >

In this paper we will discuss a prototype visual programming environment for text applications called Eye-ConTact. Eye- ConTact is modeled on scientific visualization systems such as NAG's Explorer<SUP>TM</SUP>. The user creates a flow chart of how the 'experiment' on a text is to be conducted. The map of this flow of data allows the researcher to see the logic of the study of a text and then reproduce such studies with other texts, or alter the analysis. To adapt the visual programming model to the community of textual scholars, the program must be usable and extendable by a community with limited resources for computing, and not entirely convinced that computationally-based research is relevant. The 'keep it simple' injunction is crucial in such a project. Furthermore, there are no defined or de facto standards for data formats or data representations in this community. We will discuss the original (1997) prototype. Feedback from demonstrations led to changes to the programming interface, and the underlying text analysis engine, in order to make the former more indicative of the steps in the analysis process, and the latter more portable and extensible. We will discuss the design decisions we made to address these two issues.

This book discusses semantic interaction, a user interaction methodology for visual analytic applications that more closely couples the visual reasoning processes of people with the computation. This methodology affords user interaction on visual data representations that are native to the domain of the data. User interaction in visual analytics systems is critical to enabling visual data exploration. Interaction transforms people from mere viewers to active participants in the process of analyzing and understanding data. This discourse between people and data enables people to understand aspects of their data, such as structure, patterns, trends, outliers, and other properties that ultimately result in insight. Through interacting with visualizations, users engage in sensemaking, a process of developing and understanding relationships within datasets through foraging and synthesis. The book provides a description of the principles of semantic interaction, providing design guidelines for the integration of semantic interaction into visual analytics, examples of existing technologies that leverage semantic interaction, and a discussion of how to evaluate these technologies. Semantic interaction has the potential to increase the effectiveness of visual analytic technologies and opens possibilities for a fundamentally new design space for user interaction in visual analytics systems.

Adaptive Scientific Visualization System for Desktop Computers and Mobile Devices

Five high-quality workshops were held at the 13th Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD 2009) in Bangkok, Thailand during April 27-30, 2009. There were 17, 6, 9, 4 and 5 accepted papers to be presented at the Pacific Asia Workshop on Intelligence and Security Informatics (PAISI 2009), the workshop on Advances and Issues in Biomedical Data Mining (AIBDM 2009), the workshop on Data Mining with Imbalanced Classes and Error Cost (ICEC 2009), the workshop on Open Source in Data Mining (OSDM 2009), and the workshop on Quality Issues, Measures of Interestingness and Evaluation of Data Mining Models (QIMIE 2009). One competition, PAKDD 2009 Data Mining Competition, and one local workshop, Thai Track Session, were arranged. From these workshops (except PAISI which published its works in separate LNCS proceedings), we selected two or three best papers for this LNCS publication. PAKDD is a major international conference in the areas of data mining (DM) and knowledge discovery in database (KDD). It provides an international forum for researchers and industry practitioners to share their new ideas, original research results and practical development experiences from all KDD-related areas including data mining, data warehousing, machine learning, databases, statistics, knowledge acquisition and automatic scientific discovery,data visualization, causal induction and knowledge-based systems.

The augmentation of objects by textual annotations provides a powerful means for visual data exploration. Especially in interactive scenarios, where the view on the objects and, thus, the preferred placement of annotations changes continually, efficient labeling procedures are required. As identified by a preliminary study for this paper, these procedures have to consider a number of requirements for achieving an optimal readability, e.g. cartographic principles, visual association and temporal coherence. In this paper, we present a force-based labeling algorithm for 2D and 3D scenes, which can compute the placements of annotations at very high speed and fulfills the identified requirements. The efficient labeling of several hundred annotations is achieved by computing their layout in parallel on the GPU. This allows for a real-time and collision-free arrangement of both dynamically changing and static information. We demonstrate that our method supports a large variety of applications, e.g. geographical information systems, automotive navigation systems, and scientific or information visualization systems. We conclude the paper with an expert study which confirms the enhancements brought by our algorithm with respect to visual association and readability.

Chapter 25 - A Knowledge-based Collaborative Environment for Geovisualization: Ontologies for Multiple Perspectives on Distributed Data Resources

While scientific visualization systems share many requirements with other graphical applications, they also pose special requirements that make solutions based on standard rendering hardware or software not entirely satisfactory. This paper illustrates those requirements by describing the renderer used in a production scientific visualization system, Data Explorer [1].We begin by setting forth the requirements for a visualization renderer derived from our experiences. We then describe implementation techniques used to meet the requirements of parallelism, volume rendering of irregular data, clipping, and integration of rendering modalities. The renderer described is a software renderer, but it is hoped that the requirements and implementation described here might influence the design of future generations of rendering hardware.

In the decade of data explosion, a significant challenge is how to transform data into understandings and insights that are useful to people. Visualization, the use of computer-supported, interactive visual representations of data to amplify cognition <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">[1]</sup> , is an important approach to addressing this challenge. In particular, scientific visualization primarily represents physical or geometric data while information visualization mainly represents abstract data such as text documents, graphs, and multidimensional data. Visual analytics, springing out of the fields of information visualization and scientific visualization, is an emerging research area that targets analyzing massive amounts of information for timely decision making <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">[2]</sup> . Its basic approach is to use interactive visual interfaces to facilitate analytical reasoning so that human perception abilities and domain knowledge can be exploited together with computational powers. Closely related to visualization, computer graphics is primarily about representation and manipulation of image data by a computer.

Chapter 12 - Signature Exploration, a Means to Improve Comprehension and Choice within Complex Visualization Processes: Issues and Opportunities

Augmented Reality is a promising technique for human-machine interaction. Especially in robotics, which always considers systems in their environment, it is highly beneficial to display visualizations and receive user input directly in exactly that environment. We explore this idea using the Microsoft HoloLens, with which we capture indoor environments and display interaction cues with known object classes. The 3D mesh recorded by the HoloLens is annotated online, as the user moves, with semantic classes using a projective approach, which allows us to use a state-of-the-art 2D semantic segmentation method. The results are fused onto the mesh; prominent object segments are identified and displayed in 3D to the user. Finally, the user can trigger actions by gesturing at the object. We both present qualitative results and analyze the accuracy and performance of our method in detail on an indoor dataset.

Geometric structures such as points, lines, and surfaces play a vital role in scientific visualization. However, these visualizations frequently suffer from visual clutter that hinders the inspection of important features behind dense but less important features. In the past few years, geometric cluttering and occlusion avoidance has been addressed in scientific visualization with various approaches such as opacity optimization techniques. In this paper, we present a novel approach for opacity optimization based on recent state-of-the-art moment-based techniques for signal reconstruction. In contrast to truncated Fourier series, momentbased reconstructions of feature importance and optical depth along view rays are highly accurate for sparse regions but also plausible for densely covered regions. At the same time, moment-based methods do not suffer from ringing artifacts. Moreover, this representation enables fast evaluation and compact storage, which is crucial for per-pixel optimization especially for large geometric structures. We also present a fast screen space filtering approach for optimized opacities that works directly on moment buffers. This filtering approach is suitable for real-time visualization applications, while providing comparable quality to object space smoothing. Its implementation is independent of the type of geometry such that it is general and easy to integrate. We compare our technique to recent state of the art techniques for opacity optimization and apply it to real and synthetic data sets in various applications.

The Workshop on Scientific Visualization Environments at Visualization '91 was organized with several purposes in mind. First and foremost was to bring together widely dispersed researchers so they could share information in a relatively informal and interactive environment. The organizers believed this would establish working relationships between the participants, which was a second purpose. It was hoped that a workshop could generate a focus and identify important research directions in the field. Finally, we believed the workshop report could provide a useful overview to a broader audience.With these purposes in mind, we established two specific objectives: (i) review the state of the art and (ii) identify key research issues and directions. Reaching these objectives in a short two day workshop would require an effective mechanism for organizing the discussions. The organizing principle we chose was a software design principle suggested by Dijkstra [ 1 ] some twenty years ago: the notion of an abstract machine. We considered a visualization environment as a special purpose, very high level computer ideally suited to the needs of scientific visualization. We then organized the workshop discussions around the questions of what the memory (data types), instruction set (operations) and control mechanisms (programming interfaces) of this machine might be.Using this organizing principle, we established the following agenda: Monday morning: position statements. Each participant presented a ten minute summary of their position paper Monday afternoon: data types and interfaces What are the (abstract) data types on which a visualization environment is based? What are the interfaces for importing and exporting data from the environment? Tuesday morning: control structures and architecture What are the mechanisms for sequencing and combining the primitive operations provided by the data types. What are the interaction and programming interfaces to the environment? Tuesday afternoon early: reference model Do we understand visualization environments well enough to construct a useful general model of their structure and function? Tuesday afternoon late: other issues and summary What issues are not addressed in the other sections? What have we accomplished? For each section we followed a simple procedure. The organizers presented introductory remarks consisting of a brief review of the state of the art and identification of research issues. The introductory remarks served as a starting point for discussions which followed. Participants were given copies of the organizers' remarks and asked to take notes for contribution to the report. The notes were collected at the end of the workshop and collated into this report. In addition, participants reviewed the first draft of the report and made additional contributions.

Scientific Visualization is currently a very active and vital area of research, teaching and development. The success of Scientific Visualization is mainly due to the soundness of the basic premise behind it, that is, the basic idea of using computer-generated pictures to gain information and understanding from data (geometry) and relationships (topology). This is an extremely intiutive and very important concept which is having a profound and wide spread impact on the methodology of science and engineering. In this survey we are concentrating on three main research areas in Scientific Visualization - Intelligent Visualization Systems - Visualization of Vector- and Tensorfields - Augmented Reality Simulation

scientific visualization system of Peking University