Data, Information, and Knowledge in Visualization

This article proposes to help advance visualization technology from the interactive visualization of today to the knowledge-assisted visualization of tomorrow by defining data, information and knowledge in the context of visualization

What May Visualization Processes Optimize?

In this paper, we present an abstract model of visualization and inference processes and describe an information-theoretic measure for optimizing such processes. In order to obtain such an abstraction, we first examined six classes of workflows in data analysis and visualization, and identified four levels of typical visualization components, namely disseminative, observational, analytical and model-developmental visualization. We noticed a common phenomenon at different levels of visualization, that is, the transformation of data spaces (referred to as alphabets) usually corresponds to the reduction of maximal entropy along a workflow. Based on this observation, we establish an information-theoretic measure of cost-benefit ratio that may be used as a cost function for optimizing a data visualization process. To demonstrate the validity of this measure, we examined a number of successful visualization processes in the literature, and showed that the information-theoretic measure can mathematically explain the advantages of such processes over possible alternatives.Comment: 10 page

Software-Assisted Knowledge Generation in the Archaeological Domain: A Conceptual Framework

Comunicación presentada en la 25th International Conference on Advanced Information Systems Engineering (CAiSE 2013), celebrada en Valencia del 17 al 21 de junio de 2013.Knowledge generation processes are traditionally related to the DIKW (data-information-knowledge-wisdom) hierarchy, a layered model for the classification of human understanding. Software components can be situated in one or several of these layers, or assist in the interfaces between two of them. Most of the knowledge generation processes that occur in the archaeology field involve complex mechanisms of abstraction, relation and interpretation. Is it possible to assist the users in performing these processes? We have detected problems in the archaeological knowledge generation process that could be improved through software assistance. We propose a conceptual framework based on the structure of the data that is being managed by the user, and on the cognitive processes that the user wishes to perform on the data. The proposed framework can, arguably, set the foundation for assisted knowledge generation implemented as software systems.Peer Reviewe

The role of data visualization in Railway Big Data Risk Analysis

Big Data Risk Analysis (BDRA) is one of the possible alleys for the further development of risk models in the railway transport. Big Data techniques allow a great quantity of information to be handled from different types of sources (e.g. unstructured text, signaling and train data). The benefits of this approach may lie in improving the understanding of the risk factors involved in railways, detecting possible new threats or assessing the risk levels for rolling stock, rail infrastructure or railway operations. For the efficient use of BDRA, the conversion of huge amounts of data into a simple and effective display is particularly challenging. Especially because it is presented to various specific target audiences. This work reports a literature review of risk communication and visualization in order to find out its applicability to BDRA, and beyond the visual techniques, what human factors have to be considered in the understanding and risk perception of the infor-mation when safety analysts and decision-makers start basing their decisions on BDRA analyses. It was found that BDRA requires different visualization strategies than those that have normally been carried out in risk analysis up to now

Mineração, estruturação e disseminação de conhecimento especializado em engenharia de petróleo

Orientadores: Celso Kazuyuki Morooka, Kazuo MiuraDissertação (mestrado) - Universidade Estadual de Campinas, Faculdade de Engenharia Mecânica e Instituto de GeociênciasResumo: A aquisição e transmissão de conhecimento são tarefas essenciais que todos os indivíduos e empresas devem enfrentar para subsistir e progredir. Na indústria do petróleo grandes quantidades de textos são estruturados diariamente para facilitar a disseminação de conhecimento, mas o ser humano não tem a habilidade de ler, compreender e lembrar tal quantidade de informação sem ajuda de sistemas computadorizados. Com o propósito de promover a disseminação de conhecimento sobre a engenharia petrolífera a dissertação propõe uma metodologia que permite a aquisição e a disseminação do conhecimento. A metodologia permite extrair os conhecimentos contidos em documentos textuais e mostrá-los graficamente usando mineração de textos e técnicas de visualização. Tal metodologia foi aplicada em duas bases de dados que são Alertas de Segurança da BSEE e teses de doutorado e dissertações de mestrado da UNICAMP as considerando repletas de conhecimento para a indústria de petróleo. A metodologia foi aplicada duas vezes na base de dados da BSEE. A primeira vez para conhecer o conteúdo geral e a segunda para especializar o conhecimento sobre a construção de poços. Os resultados obtidos são "conceitos relevantes" referentes à construção de poços sobre os quais foram construídas três estruturas de conhecimento. Estas estruturas evidenciam as relações existentes e a relevância desses conceitos. Os modelos de conhecimento estruturado obtidos podem ser utilizados para disseminar conhecimento, classificar lições aprendidas, treinar pessoal, visualizar e navegar em conteúdo. O resultado principal desta aplicação é um grafo de conhecimento multicamada que permite a busca por conteúdo e a eficiente recuperação de documentos. A qualidade dos resultados oriundos desta metodologia foram confirmados através de dois testes. O primeiro teste consistiu em buscar dentro da base de dados da UNICAMP, documentos relevantes para estudantes do programa de pós graduação em ciências e engenharia de petróleo (CEP) que estavam realizando trabalhos em diferentes linhas de pesquisa. O segundo teste incidiu em encontrar Alertas de Segurança utilizando palavras chaves idênticas por diferentes motores de busca (motor de busca da BSEE, Google e o método proposto). Os resultados obtidos em ambos os testes mostram a efetividade da metodologia proposta em processar bases de dados locais e especializadasAbstract: Acquisition and transmission of knowledge are essential tasks that all individual and enterprises face to subsist and progress. In the petroleum industry large amounts of texts are daily structured to facilitate the dissemination of knowledge but the human being does not have the ability to read, comprehend and remember such amount of information without the help of computerized systems. With the purpose of promoting the dissemination of knowledge about the petroleum engineering the dissertation proposes a methodology that allows acquisition and dissemination of knowledge. The methodology enables to extract the knowledge contained in textual documents and illustrates it in a graphical format, using text mining and visualization techniques. Such methodology has been applied in two databases, BSEE¿s Safety Alerts and doctoral thesis and master dissertations from CEP-UNICAMP, considering them meaningful sources of knowledge for petroleum industry. On BSEE's database, the methodology has been applied twice. The first time to notice the general content and the second time to specialize the knowledge on well construction. The results obtained are "relevant concepts" about well construction, with which were built three structures of knowledge. Those structures display the relevance and relationship between concepts and can be useful to disseminate knowledge, classify learned lessons, train personnel, visualize and navigate on content. The main result of application is a "Multilayer Knowledge Graph" that allows the research for contents and efficient documents recovery. The quality of results provided by the methodology were confirmed by two tests. The first test consisted to find relevant documents to graduate students of the CEP (Graduate program in petroleum science and engineering) from UNICAMP's database, who were carrying out works in different lines of research. The second test consisted to find Safety Alerts by using identical keywords but different search engines (BSEE's search engine, Google and the proposed method). Results obtained from both tests demonstrated the effectiveness of the proposed methodology in processing local and specialized databasesMestradoExplotaçãoMestra em Ciências e Engenharia de Petróleo190568/2013-5CNP

Reducing Complex Visualizations for Analysis

Data visualization provides a means to present known information in a format that is easily consumable and does not generally require specialized training. It is also well-suited to aid an analyst in discovering previously unknown information [1]. This is possible because visualization techniques can be used to highlight internal relationships and structures within the data, and present them in a graphical manner. Using visualization during the preliminary analysis phase can provide a pathway to enable an analyst to discover patterns or anomalies within the data that might otherwise go undiscovered as humans have an innate ability to visually identify patterns and anomalies. \ \ Even when an analyst has identified a pattern or anomaly within the data, creating an algorithm that allows for automated detection of other occurrences of the same, or similar, patterns is a non-trivial task. While humans are innately skilled at pattern recognition, computers are not, and patterns that might be obvious for a human to identify might be difficult for a computer to detect even when assisted by a skilled analyst [2]. This paper describes a method of taking a complex visualization, and reducing it into several smaller components in order to facilitate computer analysis of the analyst-identified patterns or anomalies in the data. From there, a detection scheme can be generated through an analyst-supervised data analysis process in order to find more occurrences in a larger dataset

Visualization Criteria: supporting knowledge transfer in Incident

Incident Management Systems (IMS) assist in managing resources in order to minimize fatalities and damage. Visual artifacts in an IMS can facilitate knowledge transfer between responders to an incident, however, evidence-based guidance on the design of these visualizations are lacking. The aim of this study is to propose evidence-based knowledge visualization criteria (KVC). Design Science Research (DSR) was the guiding methodology. We abstracted a set of KVC from the academic literature, and then applied said criteria to evaluate a cloud-based prototype IMS. The evaluation included interviews with content experts from the South African Fire Service to establish the relevance of the KVC. The KVC were also used in a heuristic evaluation of the IMS by usability experts. The theoretical contribution of the study is the validated set of KVC based on the triangulation of the findings from the content experts and the usability experts. The study also makes a practical contribution by demonstrating the use of evidencebased visualization criteria in IMS.School of Computin

A system for exploring big data: an iterative k-means searchlight for outlier detection on open health data

The interactive exploration of large and evolving datasets is challenging as relationships between underlying variables may not be fully understood. There may be hidden trends and patterns in the data that are worthy of further exploration and analysis. We present a system that methodically explores multiple combinations of variables using a searchlight technique and identifies outliers. An iterative k-means clustering algorithm is applied to features derived through a split-apply-combine paradigm used in the database literature. Outliers are identified as singleton or small clusters. This algorithm is swept across the dataset in a searchlight manner. The dimensions that contain outliers are combined in pairs with other dimensions using a susbset scan technique to gain further insight into the outliers. We illustrate this system by anaylzing open health care data released by New York State. We apply our iterative k-means searchlight followed by subset scanning. Several anomalous trends in the data are identified, including cost overruns at specific hospitals, and increases in diagnoses such as suicides. These constitute novel findings in the literature, and are of potential use to regulatory agencies, policy makers and concerned citizens.Comment: 2018 International Joint Conference on Neural Networks (IJCNN