Agrupamento de dados visual interactivo

Com a crescente geração, armazenamento e disseminação da informação nos últimos anos, o anterior problema de falta de informação transformou-se num problema de extracção do conhecimento útil a partir da informação disponível. As representações visuais da informação abstracta têm sido utilizadas para auxiliar a interpretação os dados e para revelar padrões de outra forma escondidos. A visualização de informação procura aumentar a cognição humana aproveitando as capacidades visuais humanas, de forma a tornar perceptível a informação abstracta, fornecendo os meios necessários para que um humano possa absorver quantidades crescentes de informação, com as suas capacidades de percepção. O objectivo das técnicas de agrupamento de dados consiste na divisão de um conjunto de dados em vários grupos, em que dados semelhantes são colocados no mesmo grupo e dados dissemelhantes em grupos diferentes. Mais especificamente, o agrupamento de dados com restrições tem o intuito de incorporar conhecimento a priori no processo de agrupamento de dados, com o objectivo de aumentar a qualidade do agrupamento de dados e, simultaneamente, encontrar soluções apropriadas a tarefas e interesses específicos. Nesta dissertação é estudado a abordagem de Agrupamento de Dados Visual Interactivo que permite ao utilizador, através da interacção com uma representação visual da informação, incorporar o seu conhecimento prévio acerca do domínio de dados, de forma a influenciar o agrupamento resultante para satisfazer os seus objectivos. Esta abordagem combina e estende técnicas de visualização interactiva de informação, desenho de grafos de forças direccionadas e agrupamento de dados com restrições. Com o propósito de avaliar o desempenho de diferentes estratégias de interacção com o utilizador, são efectuados estudos comparativos utilizando conjuntos de dados sintéticos e reais.

An overview of interactive visual data mining techniques for knowledge discovery

n the past decade, the analysis of data has faced the challenge of dealing with very large and complex datasets and the real-time generation of data. Technologies to store and access these complex and large datasets are in place. However, robust and scalable analysis technologies are needed to extract meaningful information from these datasets. The research field of Information Visualization and Visual Data Analytics addresses this need. Information visualization and data mining are often used complementary to each other. Their common goal is the extraction of meaningful information from complex and possibly large data. However, though data mining focuses on the usage of silicon hardware, visualization techniques also aim to access the powerful image-processing capabilities of the human brain. This article highlights the research on data visualization and visual analytics techniques. Furthermore, we highlight existing visual analytics techniques, systems, and applications including a perspective on the field from the chemical process industry.

VR BioViewer - A new interactive-visual model to represent medical information

Virtual reality (VR) techniques to understand and obtain conclusions of data in an easy way are being used by the scientific community. However, these techniques are not used frequently for analyzing large amounts of data in life sciences, particularly in genomics, due to the high complexity of data (curse of dimensionality). Nevertheless, new approaches that allow to bring out the real important data characteristics, arise the possibility of constructing VR spaces to visually understand the intrinsic nature of data. It is well known the benefits of representing high dimensional data in tridimensional spaces by means of dimensionality reduction and transformation techniques, complemented with a strong component of interaction methods. Thus, a novel framework, designed for helping to visualize and interact with data about diseases, is presented. In this paper, the framework is applied to the Van't Veer breast cancer dataset is used, while oncologists from La Paz Hospital (Madrid) are interacting with the obtained results. That is to say a first attempt to generate a visually tangible model of breast cancer disease in order to support the experience of oncologists is presented.

Interactive visual analysis of population study data

Otto-von-Guericke-Universität Magdeburg, Fakultät für Informatik, Dissertation, 2015

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.

30 years of synoptic observations from Neumayer Station with links to datasets

The analysis of research data plays a key role in data-driven areas of science. Varieties of mixed research data sets exist and scientists aim to derive or validate hypotheses to find undiscovered knowledge. Many analysis techniques identify relations of an entire dataset only. This may level the characteristic behavior of different subgroups in the data. Like automatic subspace clustering, we aim at identifying interesting subgroups and attribute sets. We present a visual-interactive system that supports scientists to explore interesting relations between aggregated bins of multivariate attributes in mixed data sets. The abstraction of data to bins enables the application of statistical dependency tests as the measure of interestingness. An overview matrix view shows all attributes, ranked with respect to the interestingness of bins. Complementary, a node-link view reveals multivariate bin relations by positioning dependent bins close to each other. The system supports information drill-down based on both expert knowledge and algorithmic support. Finally, visual-interactive subset clustering assigns multivariate bin relations to groups. A list-based cluster result representation enables the scientist to communicate multivariate findings at a glance. We demonstrate the applicability of the system with two case studies from the earth observation domain and the prostate cancer research domain. In both cases, the system enabled us to identify the most interesting multivariate bin relations, to validate already published results, and, moreover, to discover unexpected relations.

Java applets para um software educacional distribuído em eletrônica de potência

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

Analgesia for cats after ovariohysterectomy with either buprenorphine or carprofen alone or in combination

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

Post-operative analgesic effects of butorphanol or firocoxib administered to dogs undergoing elective ovariohysterectomy

ObjectiveTo compare the post-operative analgesic effects of butorphanol or firocoxib in dogs undergoing ovariohysterectomy.Study designProspective, randomized, blinded, clinical trial.AnimalsTwenty-five dogs > 1 year of age.MethodsDogs received acepromazine intramuscularly (IM), 0.05 mg kg-1 and either butorphanol IM, 0.2 mg kg-1 (BG, n = 12) or firocoxib orally (PO), 5 mg kg-1 (FG, n = 13), approximately 30 minutes before induction of anesthesia with propofol. Anesthesia was maintained with isoflurane. Ovariohysterectomy was performed by the same surgeon. Pain scores using the dynamic and interactive visual analog scale (DIVAS) were performed before and at 1, 2, 3, 4, 6, 8 and 20 hours after the end of surgery by one observer, blinded to the treatment. Rescue analgesia was provided with morphine (0.5 mg kg-1) IM and firocoxib, 5 mg kg-1 (BG only) PO if DIVAS > 50. Groups were compared using paired t-tests and Fisher's exact test (p < 0.05). Data are presented as mean +/- SD.ResultsThe BG required significantly less propofol (BG: 2.6 +/- 0.59 mg kg-1; FG: 5.39 +/- 0.7 mg kg-1) (p < 0.05) but the anesthesia time was longer (BG: 14 +/- 6, FG: 10 +/- 4 minutes). There were no differences for body weight (BG: 7.9 +/- 5.0, FG: 11.5 +/- 4.6 kg), sedation scores, and surgery and extubation times (BG: 10 +/- 2, 8 +/- 5 minutes; FG: 9 +/- 3, 8 +/- 4 minutes, respectively) (p > 0.05). The FG had significantly lower pain scores than the BG at 1, 2 and 3 hours following surgery (p < 0.05). Rescue analgesia was administered to 11/12 (92%) and 2/13 (15%) dogs in the BG and FG, respectively (p < 0.05).Conclusion and clinical relevanceFirocoxib produced better post-operative analgesia than butorphanol. Firocoxib may be used as part of a multimodal analgesia protocol but may not be effective as a sole analgesic.

Analgesic efficacy of perioperative use of vedaprofen, tramadol or their combination in cats undergoing ovariohysterectomy

The analgesic efficacy of tramadol and/or vedaprofen was evaluated in cats submitted for elective ovariohysterectomy, using a randomised double blind placebo controlled design. Forty adult female cats (3.0 +/- 0.32 kg; 1.8 +/- 0.7 years) were distributed into four groups. Vedaprofen PO (0.5 mg/kg), tramadol SC (2 mg/kg), both, or placebo was administered 1 h before surgery and every 24 and 8 h, respectively, for 72 h after surgery. Pain score evaluated by interactive visual analogue and composite pain score and hyperalgesia by the von Frey filament test were recorded at 1, 2, 4, 6, 8, 12, 24, 28, 32, 48, 52, 56, 72, 96 h and on the 7th day after surgery. Animals treated with combined vedaprofen and tramadol treatment did not need rescue analgesia, did not develop hyperalgesia, and their serum cortisol concentrations and pain scores were lower than placebo until 24 and 72 h after surgery, respectively. Combined vedaprofen and tramadol treatment provided more effective postoperative analgesia and prevented hyperalgesia than when used on their own. Multimodal technique is a superior method of treating pain after feline ovariohysterectomy. This work also provides evidence for the benefits of analgesia for up to 3 days following ovariohysterectomy. (C) 2008 ESFM and AAFP. Published by Elsevier Ltd. All rights reserved.

Prospective Evaluation of Laparoscopic Pancreatic Biopsies in 11 Healthy Cats

BackgroundDefinitive diagnosis of feline pancreatic disease is dependent on histologic examination of biopsies.HypothesisLaparoscopic punch biopsy of the pancreas does not significantly affect pancreatic health or clinical status of healthy cats, and provides an adequate biopsy sample for histopathology.AnimalsEleven healthy female domestic shorthair cats.MethodsEffects of laparoscopic pancreatic visualization alone in 5 cats compared with laparoscopic pancreatic visualization and punch biopsy in 6 cats were studied. Temperature, pulse, and respiratory rate, physical examination, and daily caloric intake were evaluated for 1 week before and 1 week after the procedure. Pain scores (simple descriptive score and dynamic interactive visual assessment score) were evaluated hourly during the 1st 6 hours postprocedure. Complete blood cell counts, serum biochemical profiles, serum feline pancreatic lipase immunoreactivity, and urine specific gravity were evaluated before the procedure and at 6, 24, and 72 hours postprocedure. One month postprocedure, during sterilization, the pancreas was reassessed visually in all cats, and microscopically in the biopsy group.ResultsFor all variables evaluated, there were no significant differences between biopsy and control cats. Re-evaluation of the pancreatic biopsy site 1 month later documented a normal tissue response to biopsy. The laparoscopic punch biopsy forceps provided high-quality pancreatic biopsy samples with an average size of 5 mm x 4 mm on 2-dimensional cut section.Conclusions and Clinical ImportanceLaparoscopic pancreatic biopsy is a useful and safe technique in healthy cats.

Java applets for a WWW-HTML-based course in power electronics

This paper introduces Java applet programs for a WWW (world wide web)-HTML (hypertext markup language)-based multimedia course in Power Electronics. The applet programs were developed with the purpose of providing an interactive visual simulation and analysis of idealized uncontrolled single-phase, and three-phase rectifiers. In addition, this paper discusses the development and utilization of JAVA applet programs to solve some design-oriented equations for rectifier applications. The major goal of these proposed JAVA applets was to provide more facilities for the students increase their pace in Power Electronics course, emphasizing waveforms analysis, and providing conditions for an on-line comparative analysis among different hands-on laboratory experiences, via a normal Internet TCP/IP connection. Therefore, using the proposed JAVA applets, which were embedded in a WWW-HTML-based course in Power Electronics, was observed an important improvement of the apprenticeship for the content of this course. Therefore, the course structure becomes fluid, allowing a true on-line course over the WWW, motivating students to learn its content, and apply it in some applications-oriented projects, and their home-works.

Analgesia pós-operatória em gatas submetidas à ovariohisterectomia tratadas com buprenorfina por diferentes vias de administração

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

Visual analysis, clustering, and classification of contrast-enhanced tumor perfusion MRI data

Magdeburg, Univ., Fak. für Inf., Diss., 2014

BIO-INSPIRED COMPUTATIONAL TECHNIQUES APPLIED TO THE CLUSTERING AND VISUALIZATION OF SPATIO-TEMPORAL GEOSPATIAL DATA

The coverage and volume of geo-referenced datasets are extensive and incessantly¦growing. The systematic capture of geo-referenced information generates large volumes¦of spatio-temporal data to be analyzed. Clustering and visualization play a key¦role in the exploratory data analysis and the extraction of knowledge embedded in¦these data. However, new challenges in visualization and clustering are posed when¦dealing with the special characteristics of this data. For instance, its complex structures,¦large quantity of samples, variables involved in a temporal context, high dimensionality¦and large variability in cluster shapes.¦The central aim of my thesis is to propose new algorithms and methodologies for¦clustering and visualization, in order to assist the knowledge extraction from spatiotemporal¦geo-referenced data, thus improving making decision processes.¦I present two original algorithms, one for clustering: the Fuzzy Growing Hierarchical¦Self-Organizing Networks (FGHSON), and the second for exploratory visual data analysis:¦the Tree-structured Self-organizing Maps Component Planes. In addition, I present¦methodologies that combined with FGHSON and the Tree-structured SOM Component¦Planes allow the integration of space and time seamlessly and simultaneously in¦order to extract knowledge embedded in a temporal context.¦The originality of the FGHSON lies in its capability to reflect the underlying structure¦of a dataset in a hierarchical fuzzy way. A hierarchical fuzzy representation of¦clusters is crucial when data include complex structures with large variability of cluster¦shapes, variances, densities and number of clusters. The most important characteristics¦of the FGHSON include: (1) It does not require an a-priori setup of the number¦of clusters. (2) The algorithm executes several self-organizing processes in parallel.¦Hence, when dealing with large datasets the processes can be distributed reducing the¦computational cost. (3) Only three parameters are necessary to set up the algorithm.¦In the case of the Tree-structured SOM Component Planes, the novelty of this algorithm¦lies in its ability to create a structure that allows the visual exploratory data analysis¦of large high-dimensional datasets. This algorithm creates a hierarchical structure¦of Self-Organizing Map Component Planes, arranging similar variables' projections in¦the same branches of the tree. Hence, similarities on variables' behavior can be easily¦detected (e.g. local correlations, maximal and minimal values and outliers).¦Both FGHSON and the Tree-structured SOM Component Planes were applied in¦several agroecological problems proving to be very efficient in the exploratory analysis¦and clustering of spatio-temporal datasets.¦In this thesis I also tested three soft competitive learning algorithms. Two of them¦well-known non supervised soft competitive algorithms, namely the Self-Organizing¦Maps (SOMs) and the Growing Hierarchical Self-Organizing Maps (GHSOMs); and the¦third was our original contribution, the FGHSON. Although the algorithms presented¦here have been used in several areas, to my knowledge there is not any work applying¦and comparing the performance of those techniques when dealing with spatiotemporal¦geospatial data, as it is presented in this thesis.¦I propose original methodologies to explore spatio-temporal geo-referenced datasets¦through time. Our approach uses time windows to capture temporal similarities and¦variations by using the FGHSON clustering algorithm. The developed methodologies¦are used in two case studies. In the first, the objective was to find similar agroecozones¦through time and in the second one it was to find similar environmental patterns¦shifted in time.¦Several results presented in this thesis have led to new contributions to agroecological¦knowledge, for instance, in sugar cane, and blackberry production.¦Finally, in the framework of this thesis we developed several software tools: (1)¦a Matlab toolbox that implements the FGHSON algorithm, and (2) a program called¦BIS (Bio-inspired Identification of Similar agroecozones) an interactive graphical user¦interface tool which integrates the FGHSON algorithm with Google Earth in order to¦show zones with similar agroecological characteristics.