Conceptual model for adaptable and extensible visual data exploration

Interactive visual representations complement traditional statistical and machine learning techniques for data analysis, allowing users to play a more active role in a knowledge discovery process and making the whole process more understandable. Though visual representations are applicable to several stages of the knowledge discovery process, a common use of visualization is in the initial stages to explore and organize a sometimes unknown and complex data set. In this context, the integrated and coordinated - that is, user actions should be capable of affecting multiple visualizations when desired - use of multiple graphical representations allows data to be observed from several perspectives and offers richer information than isolated representations. In this paper we propose an underlying model for an extensible and adaptable environment that allows independently developed visualization components to be gradually integrated into a user configured knowledge discovery application. Because a major requirement when using multiple visual techniques is the ability to link amongst them, so that user actions executed on a representation propagate to others if desired, the model also allows runtime configuration of coordinated user actions over different visual representations. We illustrate how this environment is being used to assist data exploration and organization in a climate classification problem.

Semi-supervised dimensionality reduction based on partial least squares for visual analysis of high dimensional data

Dimensionality reduction is employed for visual data analysis as a way to obtaining reduced spaces for high dimensional data or to mapping data directly into 2D or 3D spaces. Although techniques have evolved to improve data segregation on reduced or visual spaces, they have limited capabilities for adjusting the results according to user's knowledge. In this paper, we propose a novel approach to handling both dimensionality reduction and visualization of high dimensional data, taking into account user's input. It employs Partial Least Squares (PLS), a statistical tool to perform retrieval of latent spaces focusing on the discriminability of the data. The method employs a training set for building a highly precise model that can then be applied to a much larger data set very effectively. The reduced data set can be exhibited using various existing visualization techniques. The training data is important to code user's knowledge into the loop. However, this work also devises a strategy for calculating PLS reduced spaces when no training data is available. The approach produces increasingly precise visual mappings as the user feeds back his or her knowledge and is capable of working with small and unbalanced training sets.

Engineering knowledge-based variance-reduction simulation and G-dominance for structural frame robust optimization

[EN] This paper proposes the incorporation of engineering knowledge through both (a) advanced state-of-the-art preference handling decision-making tools integrated in multiobjective evolutionary algorithms and (b) engineering knowledge-based variance reduction simulation as enhancing tools for the robust optimum design of structural frames taking uncertainties into consideration in the design variables.The simultaneous minimization of the constrained weight (adding structuralweight and average distribution of constraint violations) on the one hand and the standard deviation of the distribution of constraint violation on the other are handled with multiobjective optimization-based evolutionary computation in two different multiobjective algorithms. The optimum design values of the deterministic structural problem in question are proposed as a reference point (the aspiration level) in reference-point-based evolutionary multiobjective algorithms (here g-dominance is used). Results including

Higher Visual Functions in the Upper and Lower Visual Fields: a pilot study in healthy subjects

Visual perception is not identical in the upper and lower visual hemifields. The mechanisms behind this difference can be found at the retinal, cortical, or higher attentional level. In this study, a new visual test battery, that involves real-time comparisons of complex visual stimuli, such as shape of objects, and speed of moving dot patterns, in the upper and lower visual hemifields, is presented. This study represents, to our knowledge, the first to implement such a visual test battery in an immersive environment composed of a hemisphere, in order to present visual stimuli in precise regions of the visual field. Ten healthy volunteers were tested in this pilot study. The results showed a higher accuracy in the image matching when the visual test was performed in the lower visual hemifield.

A New End-User Composition Model to Empower Knowledge Workers to Develop Rich Internet Applications

Enabling real end-user programming development is the next logical stage in the evolution of Internetwide service-based applications. Even so, the vision of end users programming their own web-based solutions has not yet materialized. This will continue to be so unless both industry and the research community rise to the ambitious challenge of devising an end-to-end compositional model for developing a new age of end-user web application development tools. This paper describes a new composition model designed to empower programming-illiterate end users to create and share their own off-the-shelf rich Internet applications in a fully visual fashion. This paper presents the main insights and outcomes of our research and development efforts as part of a number of successful European Union research projects. A framework implementing this model was developed as part of the European Seventh Framework Programme FAST Project and the Spanish EzWeb Project and allowed us to validate the rationale behind our approach.

A visual framework to accelerate knowledge discovery based on dimensionality reduction minimizing degradation of quality

Tradicionalmente, el uso de técnicas de análisis de datos ha sido una de las principales vías para el descubrimiento de conocimiento oculto en grandes cantidades de datos, recopilados por expertos en diferentes dominios. Por otra parte, las técnicas de visualización también se han usado para mejorar y facilitar este proceso. Sin embargo, existen limitaciones serias en la obtención de conocimiento, ya que suele ser un proceso lento, tedioso y en muchas ocasiones infructífero, debido a la dificultad de las personas para comprender conjuntos de datos de grandes dimensiones. Otro gran inconveniente, pocas veces tenido en cuenta por los expertos que analizan grandes conjuntos de datos, es la degradación involuntaria a la que someten a los datos durante las tareas de análisis, previas a la obtención final de conclusiones. Por degradación quiere decirse que los datos pueden perder sus propiedades originales, y suele producirse por una reducción inapropiada de los datos, alterando así su naturaleza original y llevando en muchos casos a interpretaciones y conclusiones erróneas que podrían tener serias implicaciones. Además, este hecho adquiere una importancia trascendental cuando los datos pertenecen al dominio médico o biológico, y la vida de diferentes personas depende de esta toma final de decisiones, en algunas ocasiones llevada a cabo de forma inapropiada. Ésta es la motivación de la presente tesis, la cual propone un nuevo framework visual, llamado MedVir, que combina la potencia de técnicas avanzadas de visualización y minería de datos para tratar de dar solución a estos grandes inconvenientes existentes en el proceso de descubrimiento de información válida. El objetivo principal es hacer más fácil, comprensible, intuitivo y rápido el proceso de adquisición de conocimiento al que se enfrentan los expertos cuando trabajan con grandes conjuntos de datos en diferentes dominios. Para ello, en primer lugar, se lleva a cabo una fuerte disminución en el tamaño de los datos con el objetivo de facilitar al experto su manejo, y a la vez preservando intactas, en la medida de lo posible, sus propiedades originales. Después, se hace uso de efectivas técnicas de visualización para representar los datos obtenidos, permitiendo al experto interactuar de forma sencilla e intuitiva con los datos, llevar a cabo diferentes tareas de análisis de datos y así estimular visualmente su capacidad de comprensión. De este modo, el objetivo subyacente se basa en abstraer al experto, en la medida de lo posible, de la complejidad de sus datos originales para presentarle una versión más comprensible, que facilite y acelere la tarea final de descubrimiento de conocimiento. MedVir se ha aplicado satisfactoriamente, entre otros, al campo de la magnetoencefalografía (MEG), que consiste en la predicción en la rehabilitación de lesiones cerebrales traumáticas (Traumatic Brain Injury (TBI) rehabilitation prediction). Los resultados obtenidos demuestran la efectividad del framework a la hora de acelerar y facilitar el proceso de descubrimiento de conocimiento sobre conjuntos de datos reales. ABSTRACT Traditionally, the use of data analysis techniques has been one of the main ways of discovering knowledge hidden in large amounts of data, collected by experts in different domains. Moreover, visualization techniques have also been used to enhance and facilitate this process. However, there are serious limitations in the process of knowledge acquisition, as it is often a slow, tedious and many times fruitless process, due to the difficulty for human beings to understand large datasets. Another major drawback, rarely considered by experts that analyze large datasets, is the involuntary degradation to which they subject the data during analysis tasks, prior to obtaining the final conclusions. Degradation means that data can lose part of their original properties, and it is usually caused by improper data reduction, thereby altering their original nature and often leading to erroneous interpretations and conclusions that could have serious implications. Furthermore, this fact gains a trascendental importance when the data belong to medical or biological domain, and the lives of people depends on the final decision-making, which is sometimes conducted improperly. This is the motivation of this thesis, which proposes a new visual framework, called MedVir, which combines the power of advanced visualization techniques and data mining to try to solve these major problems existing in the process of discovery of valid information. Thus, the main objective is to facilitate and to make more understandable, intuitive and fast the process of knowledge acquisition that experts face when working with large datasets in different domains. To achieve this, first, a strong reduction in the size of the data is carried out in order to make the management of the data easier to the expert, while preserving intact, as far as possible, the original properties of the data. Then, effective visualization techniques are used to represent the obtained data, allowing the expert to interact easily and intuitively with the data, to carry out different data analysis tasks, and so visually stimulating their comprehension capacity. Therefore, the underlying objective is based on abstracting the expert, as far as possible, from the complexity of the original data to present him a more understandable version, thus facilitating and accelerating the task of knowledge discovery. MedVir has been succesfully applied to, among others, the field of magnetoencephalography (MEG), which consists in predicting the rehabilitation of Traumatic Brain Injury (TBI). The results obtained successfully demonstrate the effectiveness of the framework to accelerate and facilitate the process of knowledge discovery on real world datasets.

Some Reflections on Engineering Education as a Tool for the next Knowledge Society

The aim of this paper is to offer a broad panorama of some aspects related with Engineering Education in a Knowledge Society.

Pictured knowledge; visual instruction practically applied for the home and school ...

Mode of access: Internet.

A knowledge-based system for liquid retaining structure design with blackboard architecture

Owing to the high degree of vulnerability of liquid retaining structures to corrosion problems, there are stringent requirements in its design against cracking. In this paper, a prototype knowledge-based system is developed and implemented for the design of liquid retaining structures based on the blackboard architecture. A commercially available expert system shell VISUAL RULE STUDIO working as an ActiveX Designer under the VISUAL BASIC programming environment is employed. Hybrid knowledge representation approach with production rules and procedural methods under object-oriented programming are used to represent the engineering heuristics and design knowledge of this domain. It is demonstrated that the blackboard architecture is capable of integrating different knowledge together in an effective manner. The system is tailored to give advice to users regarding preliminary design, loading specification and optimized configuration selection of this type of structure. An example of application is given to illustrate the capabilities of the prototype system in transferring knowledge on liquid retaining structure to novice engineers. (C) 2004 Elsevier Ltd. All rights reserved.

An industry/university collaboration to upgrade software engineering knowledge and skills in industry

This paper describes an ongoing collaboration between Boeing Australia Limited and the University of Queensland to develop and deliver an introductory course on software engineering. The aims of the course are to provide a common understanding of the nature of software engineering for all Boeing Australia's engineering staff, and to ensure they understand the practices used throughout the company. The course is designed so that it can be presented to people with varying backgrounds, such as recent software engineering graduates, systems engineers, quality assurance personnel, etc. The paper describes the structure and content of the course, and the evaluation techniques used to collect feedback from the participants and the corresponding results. The immediate feedback on the course indicates that it has been well received by the participants, but also indicates a need for more advanced courses in specific areas. The long-term feedback from participants is less positive, and the long-term feedback from the managers of the course participants indicates a need to expand on the coverage of the Boeing-specific processes and methods. (C) 2004 Elsevier Inc. All rights reserved.

Improving the use of visual interactive simulation as a knowledge elicitation tool

Knowledge elicitation is a well-known bottleneck in the production of knowledge-based systems (KBS). Past research has shown that visual interactive simulation (VIS) could effectively be used to elicit episodic knowledge that is appropriate for machine learning purposes, with a view to building a KBS. Nonetheless, the VIS-based elicitation process still has much room for improvement. Based in the Ford Dagenham Engine Assembly Plant, a research project is being undertaken to investigate the individual/joint effects of visual display level and mode of problem case generation on the elicitation process. This paper looks at the methodology employed and some issues that have been encountered to date. Copyright © 2007 Inderscience Enterprises Ltd.

Associative knowledge controls deployment of visual selective attention

According to some models of visual selective attention, objects in a scene activate corresponding neural representations, which compete for perceptual awareness and motor behavior. During a visual search for a target object, top-down control exerted by working memory representations of the target's defining properties resolves competition in favor of the target. These models, however, ignore the existence of associative links among object representations. Here we show that such associations can strongly influence deployment of attention in humans. In the context of visual search, objects associated with the target were both recalled more often and recognized more accurately than unrelated distractors. Notably, both target and associated objects competitively weakened recognition of unrelated distractors and slowed responses to a luminance probe. Moreover, in a speeded search protocol, associated objects rendered search both slower and less accurate. Finally, the first saccades after onset of the stimulus array were more often directed toward associated than control items.

Simulation based knowledge elicitation:effect of visual representation and model parameters

Since much knowledge is tacit, eliciting knowledge is a common bottleneck during the development of knowledge-based systems. Visual interactive simulation (VIS) has been proposed as a means for eliciting experts’ decision-making by getting them to interact with a visual simulation of the real system in which they work. In order to explore the effectiveness and efficiency of VIS based knowledge elicitation, an experiment has been carried out with decision-makers in a Ford Motor Company engine assembly plant. The model properties under investigation were the level of visual representation (2-dimensional, 2½-dimensional and 3-dimensional) and the model parameter settings (unadjusted and adjusted to represent more uncommon and extreme situations). The conclusion from the experiment is that using a 2-dimensional representation with adjusted parameter settings provides the better simulation-based means for eliciting knowledge, at least for the case modelled.