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Brazilian Symposium on Computer Graphics and Image Processing (SIBGRAPI)

作者： Fábio Dias Rosane Minghim Instituto de Ciências Matemáticas e de Computação University of São Paulo São Carlos SP Brazil

The complexity and size of data have created challenges to data analysis. Although point placement strategies have gained popularity in the last decade to yield a global view of multidimensional datasets, few attempts have been made to improve visual scalability and offer multilevel exploration in the context of multidimensional projections and point placement strategies. Such approaches can be helpful in improving the analysis capability both by organizing visual spaces and allowing meaningful partitions of larger datasets. In this paper, we extend the Hierarchy Point Placement (HiPP), a strategy for multi-level point placement, in order to enhance its analytical capabilities and flexibility to handle current trends in visual data science. We have provided several combinations of clustering methods and projection approaches to represent and visualize datasets; added a choice to invert the original processing order from cluster-projection to projection-cluster; proposed a better way to initialize the partitions, and added ways to summarize image, audio, text and general data groups. The tool's code is made available online. In this article, we present the new tool (named xHiPP) and provide validation through case studies with simpler and more complex datasets (text and audio) to illustrate that the capabilities afforded by the extensions have managed to provide analysts with the ability to quickly gain insight and adjust the processing pipeline to their needs.

关键词： visualization data visualization Force Mathematical model data analysis Tools Layout

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The State-of-the-Art in Predictive visual Analytics

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COMPUTER GRAPHICS FORUM 2017年第3期36卷 539-562页

作者： Lu, Yafeng Garcia, Rolando Hansen, Brett Gleicher, Michael Maciejewski, Ross Arizona State Univ Sch Comp Informat & Decis Syst Engn Tempe AZ 85287 USA Univ Wisconsin Dept Comp Sci 1210 W Dayton St Madison WI 53706 USA

Predictive analytics embraces an extensive range of techniques including statistical modeling, machine learning, and data mining and is applied in business intelligence, public health, disaster management and response, and many other fields. To date, visualization has been broadly used to support tasks in the predictive analytics pipeline. Primary uses have been in data cleaning, exploratory analysis, and diagnostics. For example, scatterplots and bar charts are used to illustrate class distributions and responses. More recently, extensive visual analytics systems for feature selection, incremental learning, and various prediction tasks have been proposed to support the growing use of complex models, agent-specific optimization, and comprehensive model comparison and result exploration. Such work is being driven by advances in interactive machine learning and the desire of end-users to understand and engage with the modeling process. In this state-of-the-art report, we catalogue recent advances in the visualization community for supporting predictive analytics. First, we define the scope of predictive analytics discussed in this article and describe how visual analytics can support predictive analytics tasks in a predictive visual analytics (PVA) pipeline. We then survey the literature and categorize the research with respect to the proposed PVA pipeline. Systems and techniques are evaluated in terms of their supported interactions, and interactions specific to predictive analytics are discussed. We end this report with a discussion of challenges and opportunities for future research in predictive visual analytics.

关键词： data mining Emergency management visual analytics Machine learning Public health

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Predicting Semantic Signatures of Fonts

Predicting Semantic Signatures of Fonts

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International conference on Semantic Computing

作者： Tugba Kulahcioglu Gerard de Melo Rutgers University Piscataway NJ USA

Towards the aim of semantic font recommendation, we first analyze the relationship between fonts and semantic attributes using a crowdsourced dataset. We deepen this analysis at the level of font categories and font styles, including via a series of interactive visualizations of the relationships between multiple dimensions of this data. Subsequently, we induce semantic signatures for a large number of fonts by computationally predicting attribute values using a k-NN based approach. We evaluate the effectiveness of this approach, and, through a visual exploration, categorize semantic attributes into three groups based on their potential to be conveyed by the fonts. The resulting resource is made available, and we aim for the findings, visualizations, and data to benefit studies that computationally support the challenging but critical process of font selection.

关键词： Semantics visualization data visualization Market research Time factors Correlation Task analysis

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exploration on Pavement Surface Conditions Attributed To Mineral Freight and Logistics Operations on Kuantan Road Network

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IOP conference Series: Materials Science and Engineering 2020年第1期712卷

作者： A Ismail M R Intan Suhana K A Masri N H Rapar Faculty of Civil Engineering Technology Universiti Malaysia Pahang 26300 Gambang Pahang Malaysia Earth Resources & Sustainability Centre Universiti Malaysia Pahang 26300 Kuantan Pahang Malaysia

The most active mineral mining operations in Malaysia are located in Kuantan, Pahang. The rapid increases of the minerals operations are of concern due to its significant impacts on the environment and human health. While effects to the environment and human health are a great of concern, the effect to the transportation system also should not be neglected. With the continuous growth of these industries, there will be more heavy vehicles generated on road transporting minerals related products leading to several issues especially on road pavement damages. Pavement damages are always associated with its optimum function and may contribute to road crashes. There are several factors that influenced the pavement damages such as weather changes, quality of material use, improper maintenance and traffic operations. This study was conducted to explore the pavement damages occurrence based on traffic operation which focusing on heavy vehicles composition as centre for critical analysis. To achieve this study, the identification of regular routing used for minerals logistic operation from the mining source to the port was chosen as study location. This study was conducted by using visual observation survey and traffic volume survey. A set of historical data on route inspection supplied by Road Care also been used for analysis. Then, data obtained were analyzed using road disability level and road rating assessment. The result shows that the road rating leading to Kuantan Port was less satisfactory (rating C) with the marks of 7.27 and the highest type of damage found was cracking with 36% followed by rutting (33%) and potholes (31%). The outcomes from this study were hoped to bring some understanding on the impact of heavy vehicles towards transportation system within a local road network in Kuantan.

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E-Map: A visual Analytics Approach for Exploring Significant Event Evolutions in Social Media

E-Map: A Visual Analytics Approach for Exploring Significant...

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IEEE conference on visual Analytics Science and Technology (VAST)

作者： Chen, Siming Chen, Shuai Lin, Lijing Yuan, Xiaoru Liang, Jie Zhang, Xiaolong Peking Univ Key Lab Machine Percept Minist Educ Beijing Peoples R China Peking Univ Sch EECS Beijing Peoples R China Univ Technol Fac Engn & Informat Technol Sydney NSW Australia Penn State Univ Coll Informat Sci & Technol University Pk PA 16802 USA

ISBN: (纸本)9781538631638

Significant events are often discussed and spread through social media, involving many people. Reposting activities and opinions expressed in social media offer good opportunities to understand the evolution of events. However, the dynamics of reposting activities and the diversity of user comments pose challenges to understand event-related social media data. We propose E-Map, a visual analytics approach that uses map-like visualization tools to help multi-faceted analysis of social media data on a significant event and in-depth understanding of the development of the event. E-Map transforms extracted keywords, messages, and reposting behaviors into map features such as cities, towns, and rivers to build a structured and semantic space for users to explore. It also visualizes complex posting and reposting behaviors as simple trajectories and connections that can be easily followed. By supporting multi-level spatial temporal exploration, E-Map helps to reveal the patterns of event development and key players in an event, disclosing the ways they shape and affect the development of the event. Two cases analysing real-world events confirm the capacities of E-Map in facilitating the analysis of event evolution with social media data.

关键词： Social Media Event analysis Map-like visual Metaphor Spatial Temporal visual Analytics

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TemporalTracks: visual analytics for exploration of 4D fMRI time-series coactivation 17

TemporalTracks: Visual analytics for exploration of 4D fMRI ...

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34th International conference on Computer Graphics (CGI)

作者： de Ridder, M. Klein, K. Kim, J. Univ Sydney Biomed & Multimedia Informat Technol Res Grp Camperdown NSW Australia Univ Konstanz Dept Comp & Informat Sci Constance Germany

ISBN: (纸本)9781450352284

Functional magnetic resonance imaging (fMRI) is a 4D medical imaging modality that depicts a proxy of neuronal activity in a series of temporal scans. Statistical processing of the modality shows promise in uncovering insights about the functioning of the brain, such as the default mode network, and characteristics of mental disorders. Current statistical processing generally summarises the temporal signals between brain regions into a single data point to represent the 'coactivation' of the regions. That is, how similar are their temporal patterns over the scans. However, the potential of such processing is limited by issues of possible data misrepresentation due to uncertainties, e.g. noise in the data. Moreover, it has been shown that brain signals are characterised by brief traces of coactivation, which are lost in the single value representations. To alleviate the issues, alternate statistical processes have been used, however creating effective techniques has proven difficult due to problems, e.g. issues with noise, which often require user input to uncover. visual analytics, therefore, through its ability to interactively exploit human expertise, presents itself as an interesting approach of benefit to the domain. In this work, we present the conceptual design behind TemporalTracks, our visual analytics system for exploration of 4D fMRI time-series coactivation data, utilising a visual metaphor to effectively present coactivation data for easier understanding. We describe our design with a case study visually analysing Human Connectome Project data, demonstrating that TemporalTracks can uncover temporal events that would otherwise be hidden in standard analysis.

关键词： Functional magnetic resonance imaging temporal data visualization coactivation analysis

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visual Querying of Semantically Enriched Movement data 11

Visual Querying of Semantically Enriched Movement Data

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11th International Joint conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP)

作者： Haag, Florian Krueger, Robert Ertl, Thomas Univ Stuttgart Inst Visualizat & Interact Syst Univ Str 38 D-70569 Stuttgart Germany

ISBN: (纸本)9783319648705;9783319648699

visual data exploration is used to reveal unknown patterns that, however, need to be validated, refined, and extracted for a final presentation and reporting. We contribute VESPa, a pattern-based visual query language for event sequences. With VESPa, analysts can formulate hypotheses gained and query the data for matches. In an interative analysis loop the pattern can be altered with further restrictions to narrow down the result set. Our language allows for (1) hypothesis expression and refinement, (2) visual querying, and (3) knowledge externalization. We focus on semantically enrichend movement data, used in law enforcement, consumer, and traffic analysis. To evaluate the applicability we present two case studies as well as a user study consisting of comprehensive and composition tasks.

关键词： visual query language Semantic movement analysis

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FaceCloud: Heterogeneous Cloud visualization of Multiplex Networks for Multimedia Archive exploration 17

FaceCloud: Heterogeneous Cloud Visualization of Multiplex Ne...

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25th ACM International conference on Multimedia (MM)

作者： Renoust, Benjamin Ren, Haolin Melancon, Guy Viaud, Marie-Luce Satoh, Shin'ichi Natl Inst Informat Tokyo Japan JFLI CNRS UMI 3527 Tokyo Japan Natl Audiovisual Inst INA Paris France Univ Bordeaux CNRS UMR 4800 Bordeaux France

ISBN: (纸本)9781450349062

Multimedia data is by nature heterogeneous, conveying semantic information through multiple cues. Text analysis of closed captions already brought us understanding of the spoken information. Today's advances in computer vision now enable us to look for relevant semantic information from the visual content of real-world archives. Combining these two levels of extracted information to make sense of an archive still remains a challenge. Multiplex networks, which model multiple families of interactions in a graph, can capture and combine both sources of semantics. We can leverage on these objects to extract hierarchies and integrate them in an interactive heterogeneous "visual cloud". Inspired by word clouds, these clouds allow to grasp visual and textual semantic information captured from a multimedia collection all at once. The interaction then enables direct access to the relevant video. We demonstrate our system with the exploration of a Japanese news archive.

关键词： heterogeneous data visual analytics media analytics multiplex networks tag cloud NHK Japan

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QSAnglyzer: visual Analytics for Prismatic analysis of Question Answering System Evaluations

QSAnglyzer: Visual Analytics for Prismatic Analysis of Quest...

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IEEE conference on visual Analytics Science and Technology (VAST)

作者： Chen, Nan-Chen Kim, Been Univ Washington Seattle WA 98195 USA Allen Inst Artificial Intelligence Seattle WA USA

ISBN: (纸本)9781538631638

Developing sophisticated artificial intelligence (AI) systems requires AI researchers to experiment with different designs and analyze results from evaluations (we refer this task as evaluation analysis). In this paper, we tackle the challenges of evaluation analysis in the domain of question-answering (QA) systems. Through in-depth studies with QA researchers, we identify tasks and goals of evaluation analysis and derive a set of design rationales, based on which we propose a novel approach termed prismatic analysis. Prismatic analysis examines data through multiple ways of categorization (referred as angles). Categories in each angle are measured by aggregate metrics to enable diverse comparison scenarios. To facilitate prismatic analysis of QA evaluations, we design and implement the Question Space Anglyzer (QSAnglyzer), a visual analytics (VA) tool. In QSAnglyzer, the high-dimensional space formed by questions is divided into categories based on several angles (e.g., topic and question type). Each category is aggregated by accuracy, the number of questions, and accuracy variance across evaluations. QSAnglyzer visualizes these angles so that QA researchers can examine and compare evaluations from various aspects both individually and collectively. Furthermore, QA researchers filter questions based on any angle by clicking to construct complex queries. We validate QSAnglyzer through controlled experiments and by expert reviews. The results indicate that when using QSAnglyzer, users perform analysis tasks faster (p < 0.01) and more accurately (p < 0.05), and are quick to gain new insight. We discuss how prismatic analysis and QSAnglyzer scaffold evaluation analysis, and provide directions for future research.

关键词： visual analytics visualization interactive visualization question answering multi-experiment analysis visual comparison visual exploration prismatic analysis

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A visual analytics approach for exploration of high-dimensional time series based on neighbor-joining tree 17

A visual analytics approach for exploration of high-dimensio...

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17th IEEE International Symposium on Signal Processing and Information Technology, ISSPIT 2017

作者： Urquiaga, Roberto Rodriguez Valdivia, Ana Maria Cuadros Zapana, Reynaldo Alfonte Universidad Nacional de San Agustín Arequipa Peru Universidad la Salle Arequipa Peru

ISBN: (纸本)9781538646625

High-dimensional time series analysis through visual techniques poses many challenges due to the visualization solutions proposed until now for exploratory tasks are not well-oriented to high volume of data. When the data sets grow large, the visual alternatives do not allow for a good association between similar time series. With the aim to increase more alternatives, we introduce a visual analytic approach based on Neighbor-Joining similarity tree. The proposed approach internally consists of five time series dimension reduction techniques widely used, two well-known similarity measures and interaction mechanisms to do exploratory analysis of high-dimensional time series data interactively. © 2017 IEEE.

关键词： Time series analysis

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