Browsing by Author "Angelini, Marco"

Now showing 1 - 7 of 7
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    CryptoComparator: A Visual Analytics Environment for Cryptocurrencies Analysis
    (The Eurographics Association, 2022) Conforti, Pietro Manganelli; Emanuele, Matteo; Nardelli, Pietro; Santucci, Giuseppe; Angelini, Marco; Bernard, Jürgen; Angelini, Marco
    Cryptocurrencies are a novel phenomenon in the finance world that is gaining more attention from the general public, banks, investors, and lately also academic research. A characteristic of cryptocurrencies is to be the target of investments that, due to the volatility of most of the cryptocurrencies, tends to be at high risk and behave very differently from classic currencies. A way of reducing this risk is to look at the history of existing cryptocurrencies and compare them in order to spot promising trends for increased gain. This paper introduces CryptoComparator, a Visual Analytics tool designed for allowing analysis of correlations and trends of cryptocurrencies. The system exploits an initial proposal for a double elliptic graph layout, reconfigurable with three different ordering functions, in order to support fast visual search of cryptocurrencies by correlation strength. One usecase developed with a domain expert in cryptocurrency financial activities demonstrates qualitatively the usage of the system.
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    EuroVa 2022: Frontmatter
    (The Eurographics Association, 2022) Bernard, Jürgen; Angelini, Marco; Bernard, Jürgen; Angelini, Marco
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    The Human User in Progressive Visual Analytics
    (The Eurographics Association, 2019) Micallef, Luana; Schulz, Hans-Jörg; Angelini, Marco; Aupetit, Michaël; Chang, Remco; Kohlhammer, Jörn; Perer, Adam; Santucci, Giuseppe; Johansson, Jimmy and Sadlo, Filip and Marai, G. Elisabeta
    The amount of generated and analyzed data is ever increasing, and processing such large data sets can take too long in situations where time-to-decision or fluid data exploration are critical. Progressive visual analytics (PVA) has recently emerged as a potential solution that allows users to analyze intermediary results during the computation without waiting for the computation to complete. However, there has been limited consideration on how these techniques impact the user. Based on discussions from a Dagstuhl seminar held in October 2018, this paper characterizes PVA users by their common roles, their main tasks, and their distinct focus of analysis. It further discusses cognitive biases that play a particular role in PVA. This work will help PVA visualization designers in devising systems that are tailored for their specific target users and their characteristics.
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    On Quality Indicators for Progressive Visual Analytics
    (The Eurographics Association, 2019) Angelini, Marco; May, Thorsten; Santucci, Giuseppe; Schulz, Hans-Jörg; Landesberger, Tatiana von and Turkay, Cagatay
    A key component in using Progressive Visual Analytics (PVA) is to be able to gauge the quality of intermediate analysis outcomes. This is necessary in order to decide whether a current partial outcome is already good enough to cut a long-running computation short and to proceed. To aid in this process, we propose ten fundamental quality indicators that can be computed and displayed to gain a better understanding of the progress of the progression and of the stability and certainty of an intermediate outcome. We further highlight the use of these fundamental indicators to derive other quality indicators, and we show how to apply the indicators in two use cases.
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    Toward an Interaction-Driven Framework for Modeling Big Data Visualization Systems
    (The Eurographics Association, 2022) Benvenuti, Dario; Fiordeponti, Giovanni; Cheng, Hao; Catarci, Tiziana; Fekete, Jean-Daniel; Santucci, Giuseppe; Angelini, Marco; Battle, Leilani; Krone, Michael; Lenti, Simone; Schmidt, Johanna
    Designing big data visualization applications is challenging due to their complex yet isolated development. One of the most common issues is an increase in latency that can be experienced while interacting with the system. There exists a variety of optimization techniques to handle this issue in specific scenarios, but we lack models for integrating them in a holistic way, hindering the integration of complementary functionality and hampering consistent evaluation across systems. In response, we present a framework for modeling the big data visualization pipeline which builds a bridge between the Visualization, Human-Computer Interaction, and Database communities by integrating their individual contributions within a single, easily interpretable pipeline. With this framework, visualization applications can become aware of the full end-to-end context, making it easier to determine which subset of optimizations best suits the current context.
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    Toward Disease Diagnosis Visual Support Bridging Classic and Precision Medicine
    (The Eurographics Association, 2022) Palleschi, Alessia; Petti, Manuela; Tieri, Paolo; Angelini, Marco; Bernard, Jürgen; Angelini, Marco
    The traditional approach in medicine starts with investigating patients' symptoms to make a diagnosis. While with the advent of precision medicine, a diagnosis results from several factors that interact and need to be analyzed together. This added complexity asks for increased support for medical personnel in analyzing these data altogether. Our objective is to merge the traditional approach with network medicine to offer a tool to investigate together symptoms, anatomies, diseases, and genes to establish a diagnosis from different points of view. This paper aims to help the clinician with the typical workflow of disease analysis, proposing a Visual Analytics tool to ease this task. A use case demonstrates the benefits of the proposed solution.
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    VisualBib(va): A Visual Analytics Platform for Authoring and Reviewing Bibliographies
    (The Eurographics Association, 2022) Dattolo, Antonina; Corbatto, Marco; Angelini, Marco; Krone, Michael; Lenti, Simone; Schmidt, Johanna
    Researchers are daily engaged in bibliographic tasks concerning literature search and review, both in the role of authors of scientific papers and when they are reviewers or evaluators. Current indexing platforms poorly support the visual exploration and comparative metadata analysis coming from subsequent searches. To address these issues, we designed and realized VisualBib(va), an online visual analytics solution, where a visual environment includes analysis control, bibliography exploration, automatic metadata extraction, and metrics visualization for real-time scenarios. We introduce and discuss here the relevant functions that VisualBib(va) supports through one usage scenarios related to the creation of a bibliography.