Exploring chaotic time series and phase spaces:from dynamical systems to visual analytics

The analyses of time series can be enhanced by modeling it in the phase space, where its dynamics are described in a more intuitive manner. In this context, time-series observations are organized in the form of states, which are (hopefully) bounded by a well-defined structure where better insights can be inferred. However, two parameters are needed to perform such a transformation. Based on the limitations of current methods to estimate those parameters, this Ph.D. thesis investigates more robust alternatives to reconstruct phase spaces from time series

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

Analyzing textual data by multiple word clouds

Word clouds are a good way to represent textual data with meta information. However, when it comes to analyzing multiple data sources, they are difficult to use. This is due to their poor comparability. The proposed RadCloud merges multiple standard clouds into a single one, while retaining the information about the origin of the words. Using separate clouds as well as this new visualization technique, a tool for textual data analysis is created: MuWoC. The resulting software is then evaluated by using it with multiple data sources ranging from encyclopedia articles, over literature to custom CSV files

Improved N-dimensional Data Visualization from Hyper-radial Values

Higher-dimensional data, which is becoming common in many disciplines due to big data problems, are inherently difficult to visualize in a meaningful way. While many visualization methods exist, they are often difficult to interpret, involve multiple plots and overlaid points, or require simultaneous interpretations. This research adapts and extends hyper-radial visualization, a technique used to visualize Pareto fronts in multi-objective optimizations, to become an n-dimensional visualization tool. Hyper-radial visualization is seen to offer many advantages by presenting a low-dimensionality representation of data through easily understood calculations. First, hyper-radial visualization is extended for use with general multivariate data. Second, a method is developed by which to optimally determine groupings of the data for use in hyper-radial visualization to create a meaningful visualization based on class separation and geometric properties. Finally, this optimal visualization is expanded from two to three dimensions in order to support even higher-dimensional data. The utility of this work is illustrated by examples using seven datasets of varying sizes, ranging in dimensionality from Fisher Iris with 150 observations, 4 features, and 3 classes to the Mixed National Institute of Standards and Technology data with 60,000 observations, 717 non-zero features, and 10 classes

UnProjection: Leveraging Inverse-Projections for Visual Analytics of High-Dimensional Data

Projection techniques are often used to visualize high-dimensional data, allowing users to better understand the overall structure of multi-dimensional spaces on a 2D screen. Although many such methods exist, comparably little work has been done on generalizable methods of inverse-projection – the process of mapping the projected points, or more generally, the projection space back to the original high-dimensional space. In this article we present NNInv, a deep learning technique with the ability to approximate the inverse of any projection or mapping. NNInv learns to reconstruct high-dimensional data from any arbitrary point on a 2D projection space, giving users the ability to interact with the learned high-dimensional representation in a visual analytics system. We provide an analysis of the parameter space of NNInv, and offer guidance in selecting these parameters. We extend validation of the effectiveness of NNInv through a series of quantitative and qualitative analyses. We then demonstrate the method’s utility by applying it to three visualization tasks: interactive instance interpolation, classifier agreement, and gradient visualization

Adaptive Visualization for Focused Personalized Information Retrieval

The new trend on the Web has totally changed todays information access environment. The traditional information overload problem has evolved into the qualitative level beyond the quantitative growth. The mode of producing and consuming information is changing and we need a new paradigm for accessing information.Personalized search is one of the most promising answers to this problem. However, it still follows the old interaction model and representation method of classic information retrieval approaches. This limitation can harm the potential of personalized search, with which users are intended to interact with the system, learn and investigate the problem, and collaborate with the system to reach the final goal.This dissertation proposes to incorporate interactive visualization into personalized search in order to overcome the limitation. By combining the personalized search and the interac- tive visualization, we expect our approach will be able to help users to better explore the information space and locate relevant information more efficiently.We extended a well-known visualization framework called VIBE (Visual Information Browsing Environment) and implemented Adaptive VIBE, so that it can fit into the per- sonalized searching environment. We tested the effectiveness of this adaptive visualization method and investigated its strengths and weaknesses by conducting a full-scale user study.We also tried to enrich the user models with named-entities considering the possibility that the traditional keyword-based user models could harm the effectiveness of the system in the context of interactive information retrieval.The results of the user study showed that the Adaptive VIBE could improve the precision of the personalized search system and could help the users to find out more diverse set of information. The named-entity based user model integrated into Adaptive VIBE showed improvements of precision of user annotations while maintaining the level of diverse discovery of information

Interactive optimisation for high-lift design.

Interactivity always involves two entities; one of them by default is a human user. The specialised subject of human factors is introduced in the context of computational aerodynamics and optimisation, specifically a high-lift aerofoil. The trial and error nature of a design process hinges on designer’s knowledge, skill and intuition. A basic, important assumption of a man-machine system is that in solving a problem, there are some steps in which the computer has an advantageous edge while in other steps a human has dominance. Computational technologies are now an indispensable part of aerospace technology; algorithms involving significant user interaction, either during the process of generating solutions or as a component of post-optimisation evaluation where human decision making is involved are increasingly becoming popular, multi-objective particle swarm is one such optimiser. Several design optimisation problems in engineering are by nature multi-objective; the interest of a designer lies in simultaneous optimisation against two or more objectives which are usually in conflict. Interactive optimisation allows the designer to understand trade-offs between various objectives, and is generally used as a tool for decision making. The solution to a multi-objective problem, one where betterment in one objective occurs over the deterioration of at least one other objective is called a Pareto set. There are multiple solutions to a problem and multiple betterment ideas to an already existing design. The final responsibility of identifying an optimal solution or idea rests on the design engineers and decision making is done based on quantitative metrics, displayed as numbers or graphs. However, visualisation, ergonomics and human factors influence and impact this decision making process. A visual, graphical depiction of the Pareto front is oftentimes used as a design aid tool for purposes of decision making with chances of errors and fallacies fundamentally existing in engineering design. An effective visualisation tool benefits complex engineering analyses by providing the decision-maker with a good imagery of the most important information. Two high-lift aerofoil data-sets have been used as test-case examples; a multi-element solver, an optimiser based on swarm intelligence technique, and visual techniques which include parallel co-ordinates, heat map, scatter plot, self-organising map and radial coordinate visualisation comprise the module. Factors that affect optima and various evaluation criteria have been studied in light of the human user. This research enquires into interactive optimisation by adapting three interactive approaches: information trade-off, reference point and classification, and investigates selected visualisation techniques which act as chief aids in the context of high-lift design trade studies. Human-in-the-loop engineering, man-machine interaction & interface along with influencing factors, reliability, validation and verification in the presence of design uncertainty are considered. The research structure, choice of optimiser and visual aids adapted in this work are influenced by and streamlined to fit with the parallel on-going development work on Airbus’ Python based tool. Results, analysis, together with literature survey are presented in this report. The words human, user, engineer, aerodynamicist, designer, analyst and decision-maker/ DM are synonymous, and are used interchangeably in this research. In a virtual engineering setting, for an efficient interactive optimisation task, a suitable visualisation tool is a crucial prerequisite. Various optimisation design tools & methods are most useful when combined with a human engineer's insight is the underlying premise of this work; questions such as why, what, how might help aid aeronautical technical innovation.PhD in Aerospac