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  • 1.
    Chatzimparmpas, Angelos
    et al.
    Linnaeus University, Faculty of Technology, Department of computer science and media technology (CM).
    Martins, Rafael Messias
    Linnaeus University, Faculty of Technology, Department of computer science and media technology (CM).
    Kerren, Andreas
    Linnaeus University, Faculty of Technology, Department of computer science and media technology (CM).
    t-viSNE: A Visual Inspector for the Exploration of t-SNE2018In: Presented at IEEE Information Visualization  (VIS '18), Berlin, Germany, 21-26 October, 2018, 2018Conference paper (Refereed)
    Abstract [en]

    The use of t-Distributed Stochastic Neighborhood Embedding (t-SNE) for the visualization of multidimensional data has proven to be a popular approach, with applications published in a wide range of domains. Despite their usefulness, t-SNE plots can sometimes be hard to interpret or even misleading, which hurts the trustworthiness of the results. By opening the black box of the algorithm and showing insights into its behavior through visualization, we may learn how to use it in a more effective way. In this work, we present t-viSNE, a visual inspection tool that enables users to explore anomalies and assess the quality of t-SNE results by bringing forward aspects of the algorithm that would normally be lost after the dimensionality reduction process is finished.

  • 2.
    Coimbra, Danilo B.
    et al.
    University of São Paulo, Brazil.
    Martins, Rafael Messias
    University of São Paulo, Brazil.
    Neves, Tácito T. A. T.
    University of São Paulo, Brazil.
    Telea, Alexandru C.
    University of Groningen, The Netherlands.
    Paulovich, Fernando V.
    University of São Paulo, Brazil.
    Explaining three-dimensional dimensionality reduction plots2016In: Information Visualization, ISSN 1473-8716, E-ISSN 1473-8724, Vol. 15, no 2, p. 154-172Article in journal (Refereed)
    Abstract [en]

    Understanding three-dimensional projections created by dimensionality reduction from high-variate datasets is very challenging. In particular, classical three-dimensional scatterplots used to display such projections do not explicitly show the relations between the projected points, the viewpoint used to visualize the projection, and the original data variables. To explore and explain such relations, we propose a set of interactive visualization techniques. First, we adapt and enhance biplots to show the data variables in the projected threedimensional space. Next, we use a set of interactive bar chart legends to show variables that are visible from a given viewpoint and also assist users to select an optimal viewpoint to examine a desired set of variables. Finally, we propose an interactive viewpoint legend that provides an overview of the information visible in a given three-dimensional projection from all possible viewpoints. Our techniques are simple to implement and can be applied to any dimensionality reduction technique. We demonstrate our techniques on the exploration of several real-world high-dimensional datasets.

  • 3.
    de Oliveira, Ricardo Ramos
    et al.
    Univ Sao Paulo, Brazil.
    Martins, Rafael Messias
    Linnaeus University, Faculty of Technology, Department of Computer Science.
    Simao, Adenilso da Silva
    Univ Sao Paulo, Brazil.
    Impact of the Vendor Lock-in Problem on Testing as a Service (TaaS)2017In: 2017 IEEE INTERNATIONAL CONFERENCE ON CLOUD ENGINEERING (IC2E 2017), IEEE, 2017, p. 190-196Conference paper (Refereed)
    Abstract [en]

    Testing as a Service (TaaS) is a new business and service model that provides efficient and effective software quality assurance and enables the use of a cloud for the meeting of quality standards, requirements and consumer's needs. However, problems that limit the effective use of TaaS involve lack of standardization in writing, execution, configuration and management of tests and lack of portability and interoperability among TaaS platforms - the so-called lock-in problem. The lock-in problem is a serious threat to software testing in the cloud and may become critical when a provider decides to suddenly increase prices, or shows serious technical availability problems. This paper proposes a novel approach for solving the lock-in problem in TaaS with the use of design patterns. The aim to assist software engineers and quality control managers in building testing solutions that are both portable and interoperable and promote a more widespread adoption of the TaaS model in cloud computing.

  • 4.
    Felizardo, Katia Romero
    et al.
    Federal Technological University of Paraná, Brazil.
    Barbosa, Ellen Francine
    University of São Paulo, Brazil.
    Martins, Rafael Messias
    University of São Paulo, Brazil.
    Valle, Pedro Henrique Dias
    University of São Paulo, Brazil.
    Maldonado, José Carlos
    University of São Paulo, Brazil.
    Visual Text Mining: Ensuring the Presence of Relevant Studies in Systematic Literature Reviews2015In: International journal of software engineering and knowledge engineering, ISSN 0218-1940, Vol. 25, no 5, p. 909-928Article in journal (Refereed)
    Abstract [en]

    One of the activities associated with the Systematic Literature Review (SLR) process is the selection review of primary studies. When the researcher faces large volumes of primary studies to be analyzed, the process used to select studies can be arduous. In a previous experiment, we conducted a pilot test to compare the performance and accuracy of PhD students in conducting the selection review activity manually and using Visual Text Mining (VTM) techniques. The goal of this paper is to describe a replication study involving PhD and Master students. The replication study uses the same experimental design and materials of the original experiment. This study also aims to investigate whether the researcher's level of experience with conducting SLRs and research in general impacts the outcome of the primary study selection step of the SLR process. The replication results have con¯rmed the outcomes of the original experiment, i.e., VTM is promising and can improve the performance of the selection review of primary studies. We also observed that both accuracy and performance increase in function of the researcher's experience level in conducting SLRs. The use of VTM can indeed be beneficial during the selection review activity.

  • 5.
    Felizardo, Katia Romero
    et al.
    University of São Paulo, Brazil.
    Martins, Rafael Messias
    University of São Paulo, Brazil.
    Maldonadon, José Carlos
    University of São Paulo, Brazil.
    Lopes, Alneu de Andrade
    University of São Paulo, Brazil.
    Minghim, Rosane
    University of São Paulo, Brazil.
    Content based visual mining of document collections using ontologies2009In: II Workshop on Web and Text Intelligence (WTI) 2009, 2009Conference paper (Refereed)
    Abstract [en]

    Document collections are important data sets in many applications. It has been shown that content based visual mappings of documents can be done effectively through projection and point placement strategies. An important step in this process is the creation of a vector space model, in which terms selected from the text and weighted are used as attributes for the vector space. That step in many cases impairs the quality of the projection due to the existence, in the data set, of many terms that are frequent but do not represent important concepts in the user's particular context. This paper proposes and evaluates the use of ontologies for content based visual analysis of textual data sets as a means to improve the displays for the analysis of the collection. The results show that when the ontology effectively represents the data domain it increases quality of maps.

  • 6.
    Felizardo, Katia Romero
    et al.
    University of São Paulo, Brazil.
    Salleh, Norsaremah
    International Islamic University, Malaysia.
    Martins, Rafael Messias
    University of São Paulo, Brazil.
    Mendes, Emilia
    University of Auckland, New Zealand.
    MacDonell, Stephen G.
    University of Auckland, New Zealand.
    Maldonado, José Carlos
    University of São Paulo, Brazil.
    Using visual text mining to support the study selection activity in systematic literature reviews2011In: 2011 Fifth International Symposium on Empirical Software Engineering and Measurement, ESEM 2011: Proceedings, Washington: IEEE, 2011, p. 77-86Conference paper (Refereed)
    Abstract [en]

    Background: A systematic literature review (SLR) is a methodology used to aggregate all relevant existing evidence to answer a research question of interest. Although crucial, the process used to select primary studies can be arduous, time consuming, and must often be conducted manually.

    Objective: We propose a novel approach, known as 'Systematic Literature Review based on Visual Text Mining' or simply SLR-VTM, to support the primary study selection activity using visual text mining (VTM) techniques. Method: We conducted a case study to compare the performance and effectiveness of four doctoral students in selecting primary studies manually and using the SLR-VTM approach. To enable the comparison, we also developed a VTM tool that implemented our approach. We hypothesized that students using SLR-VTM would present improved selection performance and effectiveness.

    Results: Our results show that incorporating VTM in the SLR study selection activity reduced the time spent in this activity and also increased the number of studies correctly included.

    Conclusions: Our pilot case study presents promising results suggesting that the use of VTM may indeed be beneficial during the study selection activity when performing an SLR.

  • 7.
    Kruiger, Johannes F.
    et al.
    University of Groningen, Netherlands.
    Rauber, Paulo E.
    University of Groningen, Netherlands.
    Martins, Rafael Messias
    Linnaeus University, Faculty of Technology, Department of computer science and media technology (CM), Department of Computer Science.
    Kerren, Andreas
    Linnaeus University, Faculty of Technology, Department of computer science and media technology (CM), Department of Computer Science.
    Kobourov, Stephen
    The University of Arizona, USA.
    Telea, Alexandru C.
    University of Groningen, Netherlands.
    Graph Layouts by t-SNE2017In: Computer graphics forum (Print), ISSN 0167-7055, E-ISSN 1467-8659, Vol. 36, no 3, p. 283-294Article in journal (Refereed)
    Abstract [en]

    We propose a new graph layout method based on a modification of the t-distributed Stochastic Neighbor Embedding (t-SNE) dimensionality reduction technique. Although t-SNE is one of the best techniques for visualizing high-dimensional data as 2D scatterplots, t-SNE has not been used in the context of classical graph layout. We propose a new graph layout method, tsNET, based on representing a graph with a distance matrix, which together with a modified t-SNE cost function results in desirable layouts. We evaluate our method by a formal comparison with state-of-the-art methods, both visually and via established quality metrics on a comprehensive benchmark, containing real-world and synthetic graphs. As evidenced by the quality metrics and visual inspection, tsNET produces excellent layouts.

  • 8.
    Kucher, Kostiantyn
    et al.
    Linnaeus University, Faculty of Technology, Department of computer science and media technology (CM).
    Martins, Rafael Messias
    Linnaeus University, Faculty of Technology, Department of computer science and media technology (CM).
    Kerren, Andreas
    Linnaeus University, Faculty of Technology, Department of computer science and media technology (CM).
    Analysis of VINCI 2009–2017 Proceedings2018In: Proceedings of the 11th International Symposium on Visual Information Communication and Interaction (VINCI '18), 13-15 August 2018, Växjö, Sweden / [ed] Karsten Klein, Yi-Na Li, and Andreas Kerren, Association for Computing Machinery (ACM), 2018, p. 97-101Conference paper (Refereed)
    Abstract [en]

    Both the metadata and the textual contents of scientific publications can provide us with insights about the development and the current state of the corresponding scientific community. In this short paper, we take a look at the proceedings of VINCI from the previous years and conduct several types of analyses. We summarize the yearly statistics about different types of publications, identify the overall authorship statistics and the most prominent contributors, and analyze the current community structure with a co-authorship network. We also apply topic modeling to identify the most prominent topics discussed in the publications. We hope that the results of our work will provide insights for the visualization community and will also be used as an overview for researchers previously unfamiliar with VINCI.

  • 9. Laitinen, Mikko
    et al.
    Lundberg, Jonas
    Linnaeus University, Faculty of Technology, Department of computer science and media technology (CM).
    Levin, Magnus
    Linnaeus University, Faculty of Arts and Humanities, Department of Languages.
    Martins, Rafael Messias
    Linnaeus University, Faculty of Technology, Department of computer science and media technology (CM).
    The Nordic Tweet Stream: A Dynamic Real-Time Monitor Corpus of Big and Rich Language Data2018In: DHN 2018 Digital Humanities in the Nordic Countries 3rd Conference: Proceedings of the Digital Humanities in the Nordic Countries 3rd Conference Helsinki, Finland, March 7-9, 2018 / [ed] Eetu Mäkelä, Mikko Tolonen, Jouni Tuominen, CEUR-WS.org , 2018, p. 349-362Conference paper (Refereed)
    Abstract [en]

    This article presents the Nordic Tweet Stream (NTS), a cross-disciplinarycorpus project of computer scientists and a group of sociolinguists interestedin language variability and in the global spread of English. Our research integratestwo types of empirical data: We not only rely on traditional structured corpusdata but also use unstructured data sources that are often big and rich inmetadata, such as Twitter streams. The NTS downloads tweets and associatedmetadata from Denmark, Finland, Iceland, Norway and Sweden. We first introducesome technical aspects in creating a dynamic real-time monitor corpus, andthe following case study illustrates how the corpus could be used as empiricalevidence in sociolinguistic studies focusing on the global spread of English tomultilingual settings. The results show that English is the most frequently usedlanguage, accounting for almost a third. These results can be used to assess howwidespread English use is in the Nordic region and offer a big data perspectivethat complement previous small-scale studies. The future objectives include annotatingthe material, making it available for the scholarly community, and expandingthe geographic scope of the data stream outside Nordic region.

  • 10.
    Martins, Rafael Messias
    University of São Paulo, Brazil ; University of Groningen, The Netherlands.
    Explanatory visualization of multidimensional projections2016Doctoral thesis, monograph (Other academic)
    Abstract [en]

    One way of getting insight into large data collections (known nowadays under the name of ‘big data’) is by depicting them visually and next interactively exploring the resulting visualizations. However, both the number of data points or measurements, and the number of dimensions describing each measurement, can be very large – much like a data table can have many rows and columns. Visualizing such so-called high-dimensional datasets is very challenging. One way to do this is to construct low (two or three) dimensional depictions of the data, and find patterns of interest in these depictions rather than in the original high-dimensional data. Techniques that perform this, called projections, have several advantages – they are visually scalable, work well with noisy data, and are fast to compute. However, a major limitation they have is that they generate hard-to-interpret images for the average user.

    We approach this problem in this thesis from several angles – by showing where errors appear in the projection, and by explaining projections in terms of the original high dimensions both locally and globally. Our proposed mechanisms are simple to learn, computationally scalable, and easy to add to any data exploration pipeline using any type of projection. We demonstrate and validate our proposals on several applications using data from measurements, scientific simulations, software engineering, and networks.

  • 11.
    Martins, Rafael Messias
    et al.
    University of São Paulo, Brazil.
    Andery, Gabriel Faria
    CPM Braxis Capgemini, Brazil.
    Heberle, Henry
    University of São Paulo, Brazil.
    Paulovich, Fernando Vieira
    University of São Paulo, Brazil.
    Lopes, Alneu de Andrade
    University of São Paulo, Brazil.
    Pedrini, Helio
    University of Campinas, Brazil.
    Minghim, Rosane
    University of São Paulo, Brazil.
    Multidimensional Projections for Visual Analysis of Social Networks2012In: Journal of Computer Science and Technology, ISSN 1000-9000, E-ISSN 1860-4749, Vol. 27, no 4, p. 791-810Article in journal (Refereed)
    Abstract [en]

    Visual analysis of social networks is usually based on graph drawing algorithms and tools. However, social networks are a special kind of graph in the sense that interpretation of displayed relationships is heavily dependent on context. Context, in its turn, is given by attributes associated with graph elements, such as individual nodes, edges, and groups of edges, as well as by the nature of the connections between individuals. In most systems, attributes of individuals and communities are not taken into consideration during graph layout, except to derive weights for force-based placement strategies. This paper proposes a set of novel tools for displaying and exploring social networks based on attribute and connectivity mappings. These properties are employed to layout nodes on the plane via multidimensional projection techniques. For the attribute mapping, we show that node proximity in the layout corresponds to similarity in attribute, leading to easiness in locating similar groups of nodes. The projection based on connectivity yields an initial placement that forgoes force-based or graph analysis algorithm, reaching a meaningful layout in one pass. When a force algorithm is then applied to this initial mapping, the final layout presents better properties than conventional force-based approaches. Numerical evaluations show a number of advantages of pre-mapping points via projections. User evaluation demonstrates that these tools promote ease of manipulation as well as fast identification of concepts and associations which cannot be easily expressed by conventional graph visualization alone. In order to allow better space usage for complex networks, a graph mapping on the surface of a sphere is also implemented.

  • 12.
    Martins, Rafael Messias
    et al.
    University of São Paulo, Brazil ; University of Groningen, The Netherlands.
    Coimbra, Danilo Barbosa
    University of São Paulo, Brazil ; University of Groningen, The Netherlands.
    Minghim, Rosane
    University of São Paulo, Brazil.
    Telea, A. C.
    University of Groningen, The Netherlands ; University of Medicine and Pharmacy ‘C. Davila’, Bucharest, Romania.
    Visual Analysis of Dimensionality Reduction Quality for Parameterized Projections2014In: Computers & graphics, ISSN 0097-8493, E-ISSN 1873-7684, Vol. 41, p. 26-42Article in journal (Refereed)
    Abstract [en]

    In recent years, many dimensionality reduction (DR) algorithms have been proposed for visual analysis of multidimensional data. Given a set of n-dimensional observations, such algorithms create a 2D or 3D projection thereof that preserves relative distances or neighborhoods. The quality of resulting projections is strongly influenced by many choices, such as the DR techniques used and their various parameter settings. Users find it challenging to judge the effectiveness of a projection in maintaining features from the original space and to understand the effect of parameter settings on these results, as well as performing related tasks such as comparing two projections. We present a set of interactive visualizations that aim to help users with these tasks by revealing the quality of a projection and thus allowing inspection of parameter choices for DR algorithms, by observing the effects of these choices on the resulting projection. Our visualizations target questions regarding neighborhoods, such as finding false and missing neighbors and showing how such projection errors depend on algorithm or parameter choices. By using several space-filling techniques, our visualizations scale to large datasets. We apply our visualizations on several recent DR techniques and high-dimensional datasets, showing how they easily offer local detail on point and group neighborhood preservation while relieving users from having to understand technical details of projections.

  • 13.
    Martins, Rafael Messias
    et al.
    Linnaeus University, Faculty of Technology, Department of computer science and media technology (CM).
    Kerren, Andreas
    Linnaeus University, Faculty of Technology, Department of computer science and media technology (CM).
    Efficient Dynamic Time Warping for Big Data Streams2019In: Proceedings of the IEEE International Conference on Big Data (Big Data '18): Workshop on Real-time & Stream Analytics in Big Data & Stream Data Management / [ed] Abe, N; Liu, H; Pu, C; Hu, X; Ahmed, N; Qiao, M; Song, Y; Kossmann, D; Liu, B; Lee, K; Tang, J; He, J; Saltz, J, IEEE, 2019, p. 2924-2929Conference paper (Refereed)
    Abstract [en]

    Many common data analysis and machine learning algorithms for time series, such as classification, clustering, or dimensionality reduction, require a distance measurement between pairs of time series in order to determine their similarity. A variety of measures can be found in the literature, each with their own strengths and weaknesses, but the Dynamic Time Warping (DTW) distance measure has occupied an important place since its early applications for the analysis and recognition of spoken word. The main disadvantage of the DTW algorithm is, however, its quadratic time and space complexity, which limits its practical use to relatively small time series. This issue is even more problematic when dealing with streaming time series that are continuously updated, since the analysis must be re-executed regularly and with strict running time constraints. In this paper, we describe enhancements to the DTW algorithm that allow it to be used efficiently in a streaming scenario by supporting an append operation for new time steps with a linear complexity when an exact, error-free DTW is needed, and even better performance when either a Sakoe-Chiba band is used, or when a sliding window is the desired range for the data. Our experiments with one synthetic and four natural data sets have shown that it outperforms other DTW implementations and the potential errors are, in general, much lower than another state-of-the-art approximated DTW technique.

  • 14.
    Martins, Rafael Messias
    et al.
    Linnaeus University, Faculty of Technology, Department of Computer Science.
    Kruiger, Johannes F.
    University of Groningen, The Netherlands ; École Nationale de l’Aviation Civile, France.
    Minghim, Rosane
    University of São Paulo, Brazil.
    Telea, Alexandru C.
    University of Groningen, The Netherlands.
    Kerren, Andreas
    Linnaeus University, Faculty of Technology, Department of Computer Science.
    MVN-Reduce: Dimensionality Reduction for the Visual Analysis of Multivariate Networks2017In: EuroVis 2017 - Short Papers / [ed] Barbora Kozlikova and Tobias Schreck and Thomas Wischgoll, Eurographics - European Association for Computer Graphics, 2017, p. 13-17Conference paper (Refereed)
    Abstract [en]

    The analysis of Multivariate Networks (MVNs) can be approached from two different perspectives: a multidimensional one, consisting of the nodes and their multiple attributes, or a relational one, consisting of the network’s topology of edges. In order to be comprehensive, a visual representation of an MVN must be able to accomodate both. In this paper, we propose a novel approach for the visualization of MVNs that works by combining these two perspectives into a single unified model, which is used as input to a dimensionality reduction method. The resulting 2D embedding takes into consideration both attribute- and edge-based similarities, with a user-controlled trade-off. We demonstrate our approach by exploring two real-world data sets: a co-authorship network and an open-source software development project. The results point out that our method is able to bring forward features of MVNs that could not be easily perceived from the investigation of the individual perspectives only. 

  • 15.
    Martins, Rafael Messias
    et al.
    University of São Paulo, Brazil ; University of Groningen, The Netherlands.
    Minghim, Rosane
    University of São Paulo, Brazil.
    Telea, A. C.
    University of Groningen, The Netherlands.
    Explaining Neighborhood Preservation for Multidimensional Projections2015In: EG UK Computer Graphics & Visual Computing (2015) / [ed] Rita Borgo, Cagatay Turkay, Eurographics - European Association for Computer Graphics, 2015, p. 7-14Conference paper (Refereed)
    Abstract [en]

    Dimensionality reduction techniques are the tools of choice for exploring high-dimensional datasets by means of low-dimensional projections. However, even state-of-the-art projection methods fail, up to various degrees, in perfectly preserving the structure of the data, expressed in terms of inter-point distances and point neighborhoods. To support better interpretation of a projection, we propose several metrics for quantifying errors related to neighborhood preservation. Next, we propose a number of visualizations that allow users to explore and explain the quality of neighborhood preservation at different scales, captured by the aforementioned error metrics. We demonstrate our exploratory views on three real-world datasets and two state-of-the-art multidimensional projection techniques.

  • 16.
    Martins, Rafael Messias
    et al.
    Linnaeus University, Faculty of Technology, Department of computer science and media technology (CM), Department of Computer Science.
    Simaki, Vasiliki
    Linnaeus University, Faculty of Technology, Department of computer science and media technology (CM), Department of Computer Science. Lund University.
    Kucher, Kostiantyn
    Linnaeus University, Faculty of Technology, Department of computer science and media technology (CM), Department of Computer Science.
    Paradis, Carita
    Lund University.
    Kerren, Andreas
    Linnaeus University, Faculty of Technology, Department of computer science and media technology (CM), Department of Computer Science.
    StanceXplore: Visualization for the Interactive Exploration of Stance in Social Media2017Conference paper (Refereed)
    Abstract [en]

    The use of interactive visualization techniques in Digital Humanities research can be a useful addition when traditional automated machine learning techniques face difficulties, as is often the case with the exploration of large volumes of dynamic—and in many cases, noisy and conflicting—textual data from social media. Recently, the field of stance analysis has been moving from a predominantly binary approach—either pro or con—to a multifaceted one, where each unit of text may be classified as one (or more) of multiple possible stance categories. This change adds more layers of complexity to an already hard problem, but also opens up new opportunities for obtaining richer and more relevant results from the analysis of stancetaking in social media. In this paper we propose StanceXplore, a new visualization for the interactive exploration of stance in social media. Our goal is to offer DH researchers the chance to explore stance-classified text corpora from different perspectives at the same time, using coordinated multiple views including user-defined topics, content similarity and dissimilarity, and geographical and temporal distribution. As a case study, we explore the activity of Twitter users in Sweden, analyzing their behavior in terms of topics discussed and the stances taken. Each textual unit (tweet) is labeled with one of eleven stance categories from a cognitive-functional stance framework based on recent work. We illustrate how StanceXplore can be used effectively to investigate multidimensional patterns and trends in stance-taking related to cultural events, their geographical distribution, and the confidence of the stance classifier. 

  • 17.
    Nakagawa, Elisa Yumi
    et al.
    University of São Paulo, Brazil.
    Martins, Rafael Messias
    University of São Paulo, Brazil.
    Felizardo, Katia Romero
    University of São Paulo, Brazil.
    Maldonado, José Carlos
    University of São Paulo, Brazil.
    Towards a process to design aspect-oriented reference architectures2009In: XXXV Latin American Informatics Conference (CLEI) 2009, 2009Conference paper (Refereed)
  • 18.
    Pagliosa, Paulo
    et al.
    Universidade Federal de Mato Grosso do Sul, Brazil.
    Martins, Rafael Messias
    University of São Paulo, Brazil.
    Cedrim, Douglas
    University of São Paulo, Brazil.
    Paiva, Afonso
    University of São Paulo, Brazil.
    Minghim, Rosane
    University of São Paulo, Brazil.
    Nonato, Luis Gustavo
    University of São Paulo, Brazil.
    MIST: Multiscale Information and Summaries of Texts2013In: Proceedings: 2013 XXVI Conference on Graphics, Patterns and Images. SIBGRAPI 2013, IEEE, 2013Conference paper (Refereed)
    Abstract [en]

    Combining distinct visual metaphors has been the mechanism adopted by several systems to enable the simultaneous visualization of multiple levels of information in a single layout. However, providing a meaningful layout while avoiding visual clutter is still a challenge. In this work we combine word clouds and a rigid-body simulation engine into an intuitive visualization tool that allows a user to visualize and interact with the content of document collections using a single overlap-free layout. The proposed force scheme ensures that neighboring documents are kept close to each other during and after layout change. Each group of neighboring documents formed on the layout generates a word cloud. A multi-seeded procedure guarantees a harmonious arrangement of distinct word clouds in visual space. The visual metaphor employs disks to represent document instances where the size of each disk defines the importance of the document in the collection. To keep the visualization clean and intuitive, only the most relevant documents are depicted as disks while the remaining ones are either displayed as smaller glyphs to help convey density information or simply removed from the layout. Hidden instances are moved together with its neighbors during rigid-body simulation, should they become visible later, but are not processed individually. This shadow movement avoids excess calculations by the force-based scheme, thus ensuring scalability and interactivity.

  • 19.
    Silva, Renato R. O.
    et al.
    University of Groningen, The Netherlands ; University of São Paulo, Brazil.
    Rauber, Paulo E.
    University of Groningen, The Netherlands.
    Martins, Rafael Messias
    University of São Paulo, Brazil.
    Minghim, Rosane
    University of São Paulo, Brazil.
    Telea, Alexandru C.
    University of Groningen, The Netherlands.
    Attribute-based Visual Explanation of Multidimensional Projections2015In: EuroVis Workshop on Visual Analytics (2015) / [ed] E. Bertini, J. C. Roberts, Eurographics - European Association for Computer Graphics, 2015Conference paper (Refereed)
    Abstract [en]

    Multidimensional projections (MPs) are key tools for the analysis of multidimensional data. MPs reduce data dimensionality while keeping the original distance structure in the low-dimensional output space, typically shown by a 2D scatterplot. While MP techniques grow more precise and scalable, they still do not show how the original dimensions (attributes) influence the projection's layout. In other words, MPs show which points are similar, but not why. We propose a visual approach to describe which dimensions contribute mostly to similarity relationships over the projection, thus explain the projection's layout. For this, we rank dimensions by increasing variance over each point-neighborhood, and propose a visual encoding to show the least-varying dimensions over each neighborhood. We demonstrate our technique with both synthetic and real-world datasets.

  • 20.
    Ulan, Maria
    et al.
    Linnaeus University, Faculty of Technology, Department of computer science and media technology (CM).
    Hönel, Sebastian
    Linnaeus University, Faculty of Technology, Department of computer science and media technology (CM).
    Martins, Rafael Messias
    Linnaeus University, Faculty of Technology, Department of computer science and media technology (CM).
    Ericsson, Morgan
    Linnaeus University, Faculty of Technology, Department of computer science and media technology (CM).
    Löwe, Welf
    Linnaeus University, Faculty of Technology, Department of computer science and media technology (CM).
    Wingkvist, Anna
    Linnaeus University, Faculty of Technology, Department of computer science and media technology (CM).
    Kerren, Andreas
    Linnaeus University, Faculty of Technology, Department of computer science and media technology (CM).
    Quality Models Inside Out: Interactive Visualization of Software Metrics by Means of Joint Probabilities2018In: Proceedings of the 2018 Sixth IEEE Working Conference on Software Visualization, (VISSOFT), Madrid, Spain, 2018 / [ed] J. Ángel Velázquez Iturbide, Jaime Urquiza Fuentes, Andreas Kerren, and Mircea F. Lungu, IEEE, 2018, p. 65-75Conference paper (Refereed)
    Abstract [en]

    Assessing software quality, in general, is hard; each metric has a different interpretation, scale, range of values, or measurement method. Combining these metrics automatically is especially difficult, because they measure different aspects of software quality, and creating a single global final quality score limits the evaluation of the specific quality aspects and trade-offs that exist when looking at different metrics. We present a way to visualize multiple aspects of software quality. In general, software quality can be decomposed hierarchically into characteristics, which can be assessed by various direct and indirect metrics. These characteristics are then combined and aggregated to assess the quality of the software system as a whole. We introduce an approach for quality assessment based on joint distributions of metrics values. Visualizations of these distributions allow users to explore and compare the quality metrics of software systems and their artifacts, and to detect patterns, correlations, and anomalies. Furthermore, it is possible to identify common properties and flaws, as our visualization approach provides rich interactions for visual queries to the quality models’ multivariate data. We evaluate our approach in two use cases based on: 30 real-world technical documentation projects with 20,000 XML documents, and an open source project written in Java with 1000 classes. Our results show that the proposed approach allows an analyst to detect possible causes of bad or good quality.

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