A Review and Characterization of Progressive Visual Analytics

Progressive Visual Analytics (PVA) has gained increasing attention over the past years. It brings the user into the loop during otherwise long-running and non-transparent computations by producing intermediate partial results. These partial results can be shown to the user for early and continuous interaction with the emerging end result even while it is still being computed. Yet as clear-cut as this fundamental idea seems, the existing body of literature puts forth various interpretations and instantiations that have created a research domain of competing terms, various definitions, as well as long lists of practical requirements and design guidelines spread across different scientific communities. This makes it more and more difficult to get a succinct understanding of PVA’s principal concepts, let alone an overview of this increasingly diverging field. The review and discussion of PVA presented in this paper address these issues and provide (1) a literature collection on this topic, (2) a conceptual characterization of PVA, as well as (3) a consolidated set of practical recommendations for implementing and using PVA-based visual analytics solutions

Diseño de Juegos Serios Utilizando los Buenos Principios del Aprendizaje Basados en los Videojuegos y el Modelo de Generación de Conocimiento para Analítica Visual

This article aims to improve learning by proposing a process to design a serious game as an environment where the user deduces knowledge through the challenges of the game, using the knowledge to be acquired as a strategy. The proposed process is a combination of the Good Principles of Learning based on video games [1] and the Knowledge Generation Model for Visual Analysis [2]. Taking into account the proposed method, a design will be generated that uses the game mechanics to collect data, measure and evaluate the change in player behavior within the game in relation to the different game states

Visual Analytics for Network Security and Critical Infrastructures

A comprehensive analysis of cyber attacks is important for better understanding of their nature and their origin. Providing a sufficient insight into such a vast amount of diverse (and sometimes seemingly unrelated) data is a task that is suitable neither for humans nor for fully automated algorithms alone. Not only a combination of the two approaches but also a continuous reasoning process that is capable of generating a sufficient knowledge base is indispensable for a better understanding of the events. Our research is focused on designing new exploratory methods and interactive visualizations in the context of network security. The knowledge generation loop is important for its ability to help analysts to refine the nature of the processes that continuously occur and to offer them a better insight into the network security related events. In this paper, we formulate the research questions that relate to the proposed solution

Uncertainty-Aware Principal Component Analysis

We present a technique to perform dimensionality reduction on data that is subject to uncertainty. Our method is a generalization of traditional principal component analysis (PCA) to multivariate probability distributions. In comparison to non-linear methods, linear dimensionality reduction techniques have the advantage that the characteristics of such probability distributions remain intact after projection. We derive a representation of the PCA sample covariance matrix that respects potential uncertainty in each of the inputs, building the mathematical foundation of our new method: uncertainty-aware PCA. In addition to the accuracy and performance gained by our approach over sampling-based strategies, our formulation allows us to perform sensitivity analysis with regard to the uncertainty in the data. For this, we propose factor traces as a novel visualization that enables to better understand the influence of uncertainty on the chosen principal components. We provide multiple examples of our technique using real-world datasets. As a special case, we show how to propagate multivariate normal distributions through PCA in closed form. Furthermore, we discuss extensions and limitations of our approach

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

Information Sharing for Customized Dynamic Visual Analytics: A Framework

Supply chain activities generate massive amount of data by several actors such as, suppliers, manufacturers, warehouses, distributers, and wholesalers. Visual analytics (VA) plays a key role in knowledge discovery and insight generation from this data and helps various players to enhance their operational and strategic decision making. This is more essential for Fast moving consumer goods (FMCG) industry, given the size of the industry and its sensitivity to the diverse market uncertainties. In this paper, we present a PhD research plan that responds to the requirements of a FMCG supply chain VA system by means of a comprehensive framework. In this regard, the information flow throughout the supply chain is a significant factor for developing a reliable and efficient VA solution and a proper information flow throughout the supply chain can be enhanced with the help of the framework consisting of modules including Data Generation, Data Integration and Management, Data Analytics, Data Visualization, and Data-driven decision making. The aim of the study is to explore the development of a VA framework that acts as a guideline for supply chain players to improve their analytical capabilities.publishedVersio

Towards a Structural Framework for Explicit Domain Knowledge in Visual Analytics

Clinicians and other analysts working with healthcare data are in need for better support to cope with large and complex data. While an increasing number of visual analytics environments integrates explicit domain knowledge as a means to deliver a precise representation of the available data, theoretical work so far has focused on the role of knowledge in the visual analytics process. There has been little discussion about how such explicit domain knowledge can be structured in a generalized framework. This paper collects desiderata for such a structural framework, proposes how to address these desiderata based on the model of linked data, and demonstrates the applicability in a visual analytics environment for physiotherapy.Comment: 8 pages, 5 figure

On Intelligence Augmentation and Visual Analytics to Enhance Clinical Decision Support Systems

Human-in-the-loop intelligence augmentation (IA) methods combined with visual analytics (VA) have the potential to provide additional functional capability and cognitively driven interpretability to Decision Support Systems (DSS) for health risk assessment and patient-clinician shared decision making. This paper presents some key ideas underlying the synthesis of IA with VA (IA/VA) and the challenges in the design, implementation, and use of IA/VA-enabled clinical decision support systems (CDSS) in the practice of medicine through data driven analytical models. An illustrative IA/VA solution provides a visualization of the distribution of health risk, and the impact of various parameters on the assessment, at the population and individual levels. It also allows the clinician to ask “what-if” questions using interactive visualizations that change actionable risk factors of the patient and visually assess their impact. This approach holds promise in enhancing decision support systems design, deployment and use outside the medical sphere as well