A dimensionality reduction algorithm and its application for interactive visualization

Visualization is one of the most effective methods for analyzing how high-dimensional data are distributed. Dimensionality reduction techniques, such as PCA, can be used to map high dimensional data to a two- or three-dimensional space. In this paper, we propose an algorithm called HyperMap that can be effectively applied to visualization. Our algorithm can be seen as a generalization of FastMap. It preserves its linear computation complexity, and overcomes several main shortcomings, especially in visualization. Since there are more than two pivot objects in each axis of a target space, more distance information needs to be preserved in each dimension. Then in visualization, the number of pivot objects can go beyond the limitation of six (2-pivot objects × 3-dimensions). Our HyperMap algorithm also gives more flexibility to the target space, such that the data distribution can be observed from various viewpoints. Its effectiveness is confirmed by empirical evaluations on both real and synthetic datasets.

Simulation and visualization of fire using extended Lindenmayer systems

This research introduces a method of using Lindenmayer Systems to model the spreading and behavior of fire inside a factory building. The research investigates the use of L-System propagated fires for determining factors such as where the fire is most likely to spread first and how fast. It also looks at an alternative way of storing the Lindenmayer System, not in the form of a string but rather as a graph. A variation on the building and traversal process is also investigated, in which the L-System is traversed depth first instead of breadth first. Results of fire propagation are presented and we conclude that L-Systems are a suitable tool for fire propagation.

Visiview : a system for the visualization of multi-dimensional data

Results generated by simulation of computer systems are often presented as a multi-dimensional data set, where the number of dimensions may be greater than 4 if sufficient system parameters are modelled. This paper describes a visualization system intended to assist in understanding the relationship between, and effect upon system behavior of, the different values of the system parameters.

The system is applied to data that cannot be represented using a mesh or isosurface representation, and in general can only be represented as a cloud of points. The use of stereoscopic rendering and rapid interaction with the data are compared with regard to their value in providing insight into the nature of the data.

A number of techniques are implemented for displaying projections of the data set with up to 7 dimensions, and for allowing intuitive manipulation of the remaining dimensions. In this way the effect of changes in one variable in the presence of a number of others can be explored.

The use of these techniques, when applied to data from computer system simulation, results in an intuitive understanding of the effects of the system parameters on system behavior.

Semantic data modelling and visualization using Noetica

Noetica is a tool for structuring knowledge about concepts and the reIationships between them. It differs from typical information systems in that the knowledge it represents is abstract, highly connected, and includes meta-knowledge (knowledge about knowledge). Noetica represents knowledge using a strongly typed graph data model. By providing a rich type system it is possible to represent conceptual information using formalized structures. A class hierarchy provides a basic classification for all objects. This allows for a consistency of representation that is not often found in `free' semantic networks, and gives the ability to easily extend a knowledge model while retaining its semantics. Visualization and query tools are provided for this data model. Visualization can be used to explore complete sets of link-classes, show paths while navigating through the database, or visualize the results of queries. Noetica supports goal-directed queries (a series of user-supplied goals that the system attempts to satisfy in sequence) and pathfinding queries (where the system finds relationships between objects in the database by following links).

A hybrid neural classifier for dimensionality reduction and data visualization and its application to fault detection and classification of induction motors

In this paper, a hybrid neural classifier combining the auto-encoder neural network and the Lattice Vector Quantization (LVQ) model is described. The auto-encoder network is used for dimensionality reduction by projecting high dimensional data into the 2D space. The LVQ model is used for data visualization by forming and adapting the granularity of a data map. The mapped data are employed to predict the target classes of new data samples. To improve classification accuracy, a majority voting scheme is adopted by the hybrid classifier. To demonstrate the applicability of the hybrid classifier, a series of experiments using simulated and real fault data from induction motors is conducted. The results show that the hybrid classifier is able to outperform the Multi-Layer Perceptron neural network, and to produce very good classification accuracy rates for various fault conditions of induction motors.

Real-time motion capture interactive, 3D stereoscopic visualization

Vincs, McCormick and dancers Steph Hutchinson & Megan Beckwith present live motion capture interactive pipelines that visualise the kinematics of a performer’s movement in stereoscopic environments created using the Unity game engine, and discuss their use in Choreotopography (2010) and Choreotopography (2011). This work forms part of Vincs’ ARC Discovery Project Capturing Dance: using motion capture to enhance the creation of innovative Australian dance (DP0987101).

Pre-visualization techniques with live lensing system enhances workflow for interactive performance creation.

Vincs & Divers, with dancer Steph Hutchinson, present a new system for real-time previsualization in Motion Builder that enables choreographers and artists making interactive 3D work to make on-the-fly lensing decisions. Using motion capture to drive a ‘character’ created from a cloth simulation in real time, the presentation highlights the advantage of live lensing for interactive work-flow in creating 3D dance visualizations. This work forms part of Vincs’ ARC Discovery project ‘Building innovative capacity in Australian dance through new visualization technologies’ (DP120101695).

Data warehouse design and legal visualization – the applicability of H2 for reporting

The steady increase of regulations and its acceleration due to the financial crisis heavily affect the management of regulatory compliance. Regulations, such as Basel III and Solvency II particularly impact data warehouses and lead to many organizational and technical changes. From an IS perspective modeling techniques for data warehouse requirement elicitation help to manage conceptual requirements. From a legal perspective attempts to visualize regulatory requirements – so called legal visualization approaches – have been developed. This paper investigates whether a conceptual modeling technique for regulatory-driven data warehouse requirements is applicable for representing data warehouse requirements in a legal environment. Applying the modeling technique H2 for Reporting in three extensive modeling projects provides three contributions. First, evidence for the applicability of a modeling technique for regulatory-driven data warehouse requirements is given. Second, lessons learned for further modeling projects are provided. Third, a discussion towards a combined perspective of information modeling and legal visualization is presented.