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  • 1.
    Bae, Juhee
    et al.
    University of Skövde, School of Informatics. University of Skövde, The Informatics Research Centre.
    Ventocilla, Elio
    University of Skövde, School of Informatics. University of Skövde, The Informatics Research Centre.
    Riveiro, Maria
    University of Skövde, School of Informatics. University of Skövde, The Informatics Research Centre.
    Helldin, Tove
    University of Skövde, School of Informatics. University of Skövde, The Informatics Research Centre.
    Falkman, Göran
    University of Skövde, School of Informatics. University of Skövde, The Informatics Research Centre.
    Evaluating Multi-Attributes on Cause and Effect Relationship Visualization2017In: Proceedings of the 12th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2017): Volumne 3: IVAPP / [ed] Alexandru Telea, Jose Braz, Lars Linsen, SciTePress, 2017, p. 64-74Conference paper (Refereed)
    Abstract [en]

    This paper presents findings about visual representations of cause and effect relationship's direction, strength, and uncertainty based on an online user study. While previous researches focus on accuracy and few attributes, our empirical user study examines accuracy and the subjective ratings on three different attributes of a cause and effect relationship edge. The cause and effect direction was depicted by arrows and tapered lines; causal strength by hue, width, and a numeric value; and certainty by granularity, brightness, fuzziness, and a numeric value. Our findings point out that both arrows and tapered cues work well to represent causal direction. Depictions with width showed higher conjunct accuracy and were more preferred than that with hue. Depictions with brightness and fuzziness showed higher accuracy and were marked more understandable than granularity. In general, depictions with hue and granularity performed less accurately and were not preferred compared to the ones with numbers or with width and brightness.

  • 2.
    Bae, Juhee
    et al.
    University of Skövde, School of Informatics. University of Skövde, The Informatics Research Centre.
    Ventocilla, Elio
    University of Skövde, School of Informatics. University of Skövde, The Informatics Research Centre.
    Riveiro, Maria
    University of Skövde, School of Informatics. University of Skövde, The Informatics Research Centre.
    Torra, Vicenç
    University of Skövde, School of Informatics. University of Skövde, The Informatics Research Centre.
    On the Visualization of Discrete Non-additive Measures2018In: Aggregation Functions in Theory and in Practice AGOP 2017 / [ed] Torra V, Mesiar R, Baets B, Springer, 2018, p. 200-210Conference paper (Refereed)
    Abstract [en]

    Non-additive measures generalize additive measures, and have been utilized in several applications. They are used to represent different types of uncertainty and also to represent importance in data aggregation. As non-additive measures are set functions, the number of values to be considered grows exponentially. This makes difficult their definition but also their interpretation and understanding. In order to support understability, this paper explores the topic of visualizing discrete non-additive measures using node-link diagram representations.

  • 3.
    Ventocilla, Elio
    University of Skövde, School of Informatics. University of Skövde, The Informatics Research Centre.
    Big Data programming with Apache Spark2019In: Data science in Practice / [ed] Alan Said, Vicenç Torra, Springer, 2019, p. 171-194Chapter in book (Refereed)
    Abstract [en]

    In this chapter we give an introduction to Apache Spark, a Big Data programming framework. We describe the framework’s core aspects as well as some of the challenges that parallel and distributed computing entail.

  • 4.
    Ventocilla, Elio
    University of Skövde, School of Informatics. University of Skövde, The Informatics Research Centre.
    On Making Machine Learning Accessible for Exploratory Data Analysis Through Visual Analytics2017Report (Other academic)
    Abstract [en]

    Visual Analytics is a field of study that seeks to aid human cognition in the process of analyzing data. It aims at doing so through visual interfaces and automated computations. Such automated computations often translate to Machine Learning algorithms. Users can leverage from these algorithms in order to find interesting patterns in massive and unstructured data. These algorithms are, however, still often regarded as black-boxes i.e. mathematical models which are hard to interpret and difficult to interact with. In a single Machine Learning library more than 40 variants of algorithms can be found, and some with up to 14 different parameters. The Visual Analytics community has pointed out a lack of research in helping users set parameters and compare results between algorithms. Moreover, it is argued that most technology in the field, which has aimed at bringing transparency to black-boxes, is embedded in domain-specific systems thus making it hard to reach and use for other users and in other domains. In general, Machine Learning has an accessibility challenge: it is hard to use, to interpret and, if not either of these, to reach. This research proposal motivates the need for research in making Machine Learning more accessible for exploratory data analysis, reviews existing work in the field, presents a research plan and, finally, describes preliminary results.

  • 5.
    Ventocilla, Elio
    University of Skövde, School of Informatics. University of Skövde, The Informatics Research Centre.
    Visualizing and Explaining Cluster Patterns: A Framework for the Exploratory Analysis of Large Multidimensional Datasets2019Report (Other academic)
    Abstract [en]

    Large quantities of data are being collected and analyzed by companies and institutions, with the intention of drawing knowledge and value. Advances in storage, computation, automated analysis and visual and interactive techniques have facilitated this process. It is, however, not always transparent on how these can be brought together for the effective and efficient exploration, monitoring and/or processing of data. That is, knowing which automated techniques and frameworks to use for a given task, how to deploy them and integrate them, how to interpret their results and processes, and how to ease their use through visual and interactive techniques.

    In the context of exploratory data analysis, where users approach data without preconceived hypotheses, this thesis proposal argues for the benefit of a framework describing the components, techniques and relations that contribute to the visualization and explanation of cluster patterns in large and multidimensional datasets. That is, a Visual Analytics framework aimed at supporting data scientists in their first steps towards understanding the overall structure of a dataset. The problem area is large and, therefore, the scope is limited to the visualization of cluster patterns in table-like datasets with meaningful attributes.

    This thesis proposal motivates the relevance of conducting research for the development of such framework. It formalizes a set of research questions and presents a research plan based on the design science research method. Moreover, it provides a description of preliminary results as well as related background theory and state-of-the-art research.

    The full text will be freely available from 2021-03-14 16:52
  • 6.
    Ventocilla, Elio
    et al.
    University of Skövde, School of Informatics. University of Skövde, The Informatics Research Centre.
    Bae, Juhee
    University of Skövde, School of Informatics. University of Skövde, The Informatics Research Centre.
    Riveiro, Maria
    University of Skövde, School of Informatics. University of Skövde, The Informatics Research Centre.
    Said, Alan
    University of Skövde, School of Informatics. University of Skövde, The Informatics Research Centre.
    A Billiard Metaphor for Exploring Complex Graphs2017In: Second Workshop on Supporting Complex Search Tasks / [ed] Marijn Koolen, Jaap Kamps, Toine Bogers, Nick Belkin, Diane Kelly, Emine Yilmaz, 2017, Vol. 1798, p. 37-40Conference paper (Refereed)
    Abstract [en]

    Exploring and revealing relations between the elements is a fre-quent task in exploratory analysis and search. Examples includethat of correlations of attributes in complex data sets, or facetedsearch. Common visual representations for such relations are di-rected graphs or correlation matrices. These types of visual encod-ings are often - if not always - fully constructed before being shownto the user. This can be thought of as a top-down approach, whereusers are presented with a full picture for them to interpret andunderstand. Such a way of presenting data could lead to a visualoverload, specially when it results in complex graphs with highdegrees of nodes and edges. We propose a bottom-up alternativecalled Billiard where few elements are presented at rst and fromwhich a user can interactively construct the rest based on whats/he nds of interest. The concept is based on a billiard metaphorwhere a cue ball (node) has an eect on other elements (associatednodes) when stroke against them.

  • 7.
    Ventocilla, Elio
    et al.
    University of Skövde, School of Informatics. University of Skövde, The Informatics Research Centre.
    Riveiro, Maria
    University of Skövde, School of Informatics. University of Skövde, The Informatics Research Centre.
    Visual Analytics Solutions as 'off-the-shelf' Libraries2017In: 2017 21st International Conference Information Visualisation (IV): Computer Graphics, Imaging and Visualisation. Biomedical Visualization, Visualisation on Built and Rural Environments & Geometric Modelling and Imaging, IEETeL2017 / [ed] Ebad Banissi, Mark W. McK. Bannatyne, Fatma Bouali, Nuno Miguel Soares Datia, Georges Grinstein, Dennis Groth, Weidong Huang, Malinka Ivanova, Sarah Kenderdine, Minoru Nakayama, Joao Moura Pires, Muhammad Sarfraz, Marco Temperini, Anna Ursyn, Gilles Venturini, Theodor G. Wyeld, Jian J. Zhang, IEEE Computer Society, 2017, p. 281-287Conference paper (Refereed)
    Abstract [en]

    Visual Analytics has brought forward many solutions to different tasks such as exploring topics, understanding user and customer behavior, comparing genomes, or detecting anomalies. Many of these solutions, if not most, are standalone applications with technological contributions which cannot be easily taken for: reuse in other domains, further improvement, benchmarking, or integration and deployment alongside other solutions. The latter can prove specially helpful for exploratory data analysis. This often leads researchers to re-implement solutions and thus to a suboptimal use of skills and resources. This paper discusses further the lack of off-the-shelf libraries for Visual Analytics, and proposes the creation of pluggable libraries on top of existing technologies such as Spark and Zeppelin. We provide an illustrative example of a pluggable, Visual Analytics library using these technologies.

  • 8.
    Ventocilla, Elio
    et al.
    University of Skövde, School of Informatics. University of Skövde, The Informatics Research Centre.
    Riveiro, Maria
    University of Skövde, School of Informatics. University of Skövde, The Informatics Research Centre.
    Visual Growing Neural Gas for Exploratory Data Analysis2019In: Proceedings of the 14th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications: Volume 3: IVAPP, 58-71, 2019, Prague, Czech Republic / [ed] Andreas Kerren, Christophe Hurter, Jose Braz, SciTePress, 2019, Vol. 3, p. 58-71Conference paper (Refereed)
    Abstract [en]

    This paper argues for the use of a topology learning algorithm, the Growing Neural Gas (GNG), for providing an overview of the structure of large and multidimensional datasets that can be used in exploratory data analysis. We introduce a generic, off-the-shelf library, Visual GNG, developed using the Big Data framework Apache Spark, which provides an incremental visualization of the GNG training process, and enables user-in-the-loop interactions where users can pause, resume or steer the computation by changing optimization parameters. Nine case studies were conducted with domain experts from different areas, each working on unique real-world datasets. The results show that Visual GNG contributes to understanding the distribution of multidimensional data; finding which features are relevant in such distribution; estimating the number of k clusters to be used in traditional clustering algorithms, such as K-means; and finding outliers.

1 - 8 of 8
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