A Phase Field Model for Continuous Clustering on Vector Fields

A new method for the simplification of flow fields is presented. It is based on continuous clustering. A well-known physical clustering model, the Cahn Hilliard model, which describes phase separation, is modified to reflect the properties of the data to be visualized. Clusters are defined implicitly as connected components of the positivity set of a density function. An evolution equation for this function is obtained as a suitable gradient flow of an underlying anisotropic energy functional. Here, time serves as the scale parameter. The evolution is characterized by a successive coarsening of patterns-the actual clustering-during which the underlying simulation data specifies preferable pattern boundaries. We introduce specific physical quantities in the simulation to control the shape, orientation and distribution of the clusters as a function of the underlying flow field. In addition, the model is expanded, involving elastic effects. In the early stages of the evolution shear layer type representation of the flow field can thereby be generated, whereas, for later stages, the distribution of clusters can be influenced. Furthermore, we incorporate upwind ideas to give the clusters an oriented drop-shaped appearance. Here, we discuss the applicability of this new type of approach mainly for flow fields, where the cluster energy penalizes cross streamline boundaries. However, the method also carries provisions for other fields as well. The clusters can be displayed directly as a flow texture. Alternatively, the clusters can be visualized by iconic representations, which are positioned by using a skeletonization algorithm.

Virtual Reality Visualization by CAVE with VFIVE and VTK

The CAVE-type virtual reality (VR) system was introduced for scientific visualization of large scale data in the plasma simulation community about a decade ago. Since then, we have been developing a VR visualization software, VFIVE, for general CAVE systems. Recently, we have integrated an open source visualization library, the Visualization Toolkit (VTK), into VFIVE. Various visualization methods of VTK can be incorporated and used interactively in VFIVE.Comment: 3 pages, 3 figures, submitted to J. Plasma Physcs (special issue for 19th ICNSP

Online visualization of bibliography Using Visualization Techniques

Visualization is a concept where we can represent some raw data in the form of graphs, images, charts, etc. which will be very helpful for the end-user to correlate and be able to understand the relationships between the data elements in a single screen. Representing the bibliographic information of the computer science journals and proceedings using Visualization technique would help user choose a particular author and navigate through the hierarchy and find out what papers the author has published, the keywords of the papers, what papers cite them, the co-authors along with the main author, and how many papers are published by the author selected by the user and so on in a single page. These information is right now present in a scattered manner and the user has to search on websites like Google Scholar [1], Cite Seer [2] to get these bibliographic records. By the use of visualization techniques, all the information can be accessed on a single page by having a graph like points on the page, where the user can search for a particular author and the author and its co-authors are represented in the form of points. The goal of this project is to enhance current bibliography web services with an intuitive interactive visualization interface and to improve user understanding and conceptualization. In this project, we develop a simple web-interface which will take a search query from the user and find the related information like author\u27s name, the co-authors, number of papers published by him, related keywords, citations referred etc. The project uses the bibliographic records which are available as XML files from the Citeseer database[2], extracts the data into the database and then queries the database for the results using a web service. The data which is extracted is then presented visually to allow the user to conceptualize the results in a better way and help him/her find the articles of interest with utmost ease. In addition the user can interactively navigate the visual results to get more information about any of the article or the author displayed. So here we present both paper centric view and author centric view to the user by representing data in terms of graphs. The nodes in the graphs obtained for paper centric views and author centric views are color coded based on the paper’s weight parameter ( popularity of the paper ). For the paper centric view, the papers which are referring other papers are represented by providing a directed arrow from referred paper to referenced paper. Overall the idea here was to represent this related data in the form of a tree, so that the user can correlate all the data and get the relationships between them

Approximated and User Steerable tSNE for Progressive Visual Analytics

Progressive Visual Analytics aims at improving the interactivity in existing analytics techniques by means of visualization as well as interaction with intermediate results. One key method for data analysis is dimensionality reduction, for example, to produce 2D embeddings that can be visualized and analyzed efficiently. t-Distributed Stochastic Neighbor Embedding (tSNE) is a well-suited technique for the visualization of several high-dimensional data. tSNE can create meaningful intermediate results but suffers from a slow initialization that constrains its application in Progressive Visual Analytics. We introduce a controllable tSNE approximation (A-tSNE), which trades off speed and accuracy, to enable interactive data exploration. We offer real-time visualization techniques, including a density-based solution and a Magic Lens to inspect the degree of approximation. With this feedback, the user can decide on local refinements and steer the approximation level during the analysis. We demonstrate our technique with several datasets, in a real-world research scenario and for the real-time analysis of high-dimensional streams to illustrate its effectiveness for interactive data analysis

Fine-grained visualization pipelines and lazy functional languages

The pipeline model in visualization has evolved from a conceptual model of data processing into a widely used architecture for implementing visualization systems. In the process, a number of capabilities have been introduced, including streaming of data in chunks, distributed pipelines, and demand-driven processing. Visualization systems have invariably built on stateful programming technologies, and these capabilities have had to be implemented explicitly within the lower layers of a complex hierarchy of services. The good news for developers is that applications built on top of this hierarchy can access these capabilities without concern for how they are implemented. The bad news is that by freezing capabilities into low-level services expressive power and flexibility is lost. In this paper we express visualization systems in a programming language that more naturally supports this kind of processing model. Lazy functional languages support fine-grained demand-driven processing, a natural form of streaming, and pipeline-like function composition for assembling applications. The technology thus appears well suited to visualization applications. Using surface extraction algorithms as illustrative examples, and the lazy functional language Haskell, we argue the benefits of clear and concise expression combined with fine-grained, demand-driven computation. Just as visualization provides insight into data, functional abstraction provides new insight into visualization

Creative User-Centered Visualization Design for Energy Analysts and Modelers

We enhance a user-centered design process with techniques that deliberately promote creativity to identify opportunities for the visualization of data generated by a major energy supplier. Visualization prototypes developed in this way prove effective in a situation whereby data sets are largely unknown and requirements open – enabling successful exploration of possibilities for visualization in Smart Home data analysis. The process gives rise to novel designs and design metaphors including data sculpting. It suggests: that the deliberate use of creativity techniques with data stakeholders is likely to contribute to successful, novel and effective solutions; that being explicit about creativity may contribute to designers developing creative solutions; that using creativity techniques early in the design process may result in a creative approach persisting throughout the process. The work constitutes the first systematic visualization design for a data rich source that will be increasingly important to energy suppliers and consumers as Smart Meter technology is widely deployed. It is novel in explicitly employing creativity techniques at the requirements stage of visualization design and development, paving the way for further use and study of creativity methods in visualization design

Network Visualization

Network science has become increasingly popular over the last several years as people have realized that networks have the ability to represent the relationships or connections between any objects. While some networks are small and easy to gather information from, others can be very large. It can be very difficult and time consuming to map out these large networks if we collect data from all the nodes in the network. Instead of examining all nodes, we seek to collect data incrementally from a portion of the network at a time to discover the whole network. This discovery occurs by successively placing monitors which can see a local portion of the graph. We then tested all of our algorithms on four different networks. Although there was no one algorithm that did best overall, we were able to see some of the strengths and weaknesses of each on various structures of networks

Big Data Visualization Tools

Data visualization is the presentation of data in a pictorial or graphical format, and a data visualization tool is the software that generates this presentation. Data visualization provides users with intuitive means to interactively explore and analyze data, enabling them to effectively identify interesting patterns, infer correlations and causalities, and supports sense-making activities.Comment: This article appears in Encyclopedia of Big Data Technologies, Springer, 201