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International Joint conference on Neural Networks (IJCNN)

作者： Rao, A. Ravishankar Clarke, Daniel Fairleigh Dickinson Univ Teaneck NJ 07666 USA

ISBN: (纸本)9781509061822

When analyzing data where the relationships between variables are not fully understood, it is typical to engage in visual exploration. However, this is slow and manually intensive, and interesting trends could potentially be missed. Ideally, we would like to focus on patterns and locations within the data that are interesting, such as significant clusters and outliers. We present a novel iterative k-means clustering algorithm to efficiently identify clusters in large datasets. This facilitates rapid visual exploration of new datasets. We illustrate our framework by performing a detailed analysis of open health care data released by the US Government and New York State. We apply our iterative k-means algorithm to identify clusters of trends in labor force participation to present a unique perspective by area of medical specialty over a 50-year period. Specialties such as nurse practitioners have seen a significant increase in the number of practitioners relative to internal medicine. Our code is open source and available on Github.

关键词： Large dataset

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visual analysis of MOOC Forums with iForum

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IEEE TRANSACTIONS ON visualIZATION AND COMPUTER GRAPHICS 2017年第1期23卷 201-210页

作者： Fu, Siwei Zhao, Jian Cui, Weiwei Qu, Huamin Hong Kong Univ Sci & Technol Hong Kong Hong Kong Peoples R China Autodesk Res Paris France Microsoft Res Beijing Peoples R China

Discussion forums of Massive Open Online Courses (MOOC) provide great opportunities for students to interact with instructional staff as well as other students. exploration of MOOC forum data can offer valuable insights for these staff to enhance the course and prepare the next release. However, it is challenging due to the large, complicated, and heterogeneous nature of relevant datasets, which contain multiple dynamically interacting objects such as users, posts, and threads, each one including multiple attributes. In this paper, we present a design study for developing an interactive visual analytics system, called iForum, that allows for effectively discovering and understanding temporal patterns in MOOC forums. The design study was conducted with three domain experts in an iterative manner over one year, including a MOOC instructor and two official teaching assistants. iForum offers a set of novel visualization designs for presenting the three interleaving aspects of MOOC forums (i. e., posts, users, and threads) at three different scales. To demonstrate the effectiveness and usefulness of iForum, we describe a case study involving field experts, in which they use iForum to investigate real MOOC forum data for a course on JAVA programming.

关键词： Discussion forum MOOC temporal visualization visual analytics

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Voyager 2: Augmenting visual analysis with Partial View Specifications 17

Voyager 2: Augmenting Visual Analysis with Partial View Spec...

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ACM SIGCHI conference on Human Factors in Computing Systems (CHI)

作者： Wongsuphasawat, Kanit Qu, Zening Moritz, Dominik Chang, Riley Ouk, Felix Anand, Anushka Mackinlay, Jock Howe, Bill Heer, Jeffrey Univ Washington Seattle WA 98195 USA Tableau Res Seattle WA USA

ISBN: (纸本)9781450346559

visual data analysis involves both open-ended and focused exploration. Manual chart specification tools support question answering, but are often tedious for early-stage exploration where systematic data coverage is needed. visualization recommenders can encourage broad coverage, but irrelevant suggestions may distract users once they commit to specific questions. We present Voyager 2, a mixed-initiative system that blends manual and automated chart specification to help analysts engage in both open-ended exploration and targeted question answering. We contribute two partial specification interfaces: wildcards let users specify multiple charts in parallel, while related views suggest visualizations relevant to the currently specified chart. We present our interface design and applications of the CompassQL visualization query language to enable these interfaces. In a controlled study we find that Voyager 2 leads to increased data field coverage compared to a traditional specification tool, while still allowing analysts to flexibly drill-down and answer specific questions.

关键词： data visualization exploratory analysis partial specification visualization recommendation mixed-initiative interfaces

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BRCA-Pathway: a structural integration and visualization system of TCGA breast cancer data on KEGG pathways

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BMC BIOINFORMATICS 2018年第Sup1期19卷 42-42页

作者： Kim, Inyoung Choi, Saemi Kim, Sun Seoul Natl Univ Dept Comp Sci & Engn 1 Gwanak Ro Seoul 08826 South Korea Seoul Natl Univ Interdisciplinary Program Bioinformat 1 Gwanak Ro Seoul 08826 South Korea Seoul Natl Univ Bioinformat Inst 1 Gwanak Ro Seoul 08826 South Korea

Background: Bioinformatics research for finding biological mechanisms can be done by analysis of transcriptome data with pathway based interpretation. Therefore, researchers have tried to develop tools to analyze transcriptome data with pathway based interpretation. Over the years, the amount of omics data has become huge, e.g., TCGA, and the data types to be analyzed have come in many varieties, including mutations, copy number variations, and transcriptome. We also need to consider a complex relationship with regulators of genes, particularly Transcription Factors(TF). However, there has not been a system for pathway based exploration and analysis of TCGA multi-omics data. In this reason, We have developed a web based system BRCA-Pathway to fulfill the need for pathway based analysis of TCGA multi-omics data. Results: BRCA-Pathway is a structured integration and visual exploration system of TCGA breast cancer data on KEGG pathways. For data integration, a relational database is designed and used to integrate multi-omics data of TCGA-BRCA, KEGG pathway data, Hallmark gene sets, transcription factors, driver genes, and PAM50 subtypes. For data exploration, multi-omics data such as SNV, CNV and gene expression can be visualized simultaneously in KEGG pathway maps, together with transcription factors-target genes (TF-TG) correlation and relationships among cancer driver genes. In addition, 'Pathways summary' and 'Oncoprint' with mutual exclusivity sort can be generated dynamically with a request by the user. data in BRCA-Pathway can be downloaded by REST API for further analysis. Conclusions: BRCA-Pathway helps researchers navigate omics data towards potentially important genes, regulators, and discover complex patterns involving mutations, CNV, and gene expression data of various patient groups in the biological pathway context. In addition, mutually exclusive genomic alteration patterns in a specific pathway can be generated. BRCA-Pathway can provide an integrative

关键词： TCGA breast cancer KEGG-Pathway Gene expression Mutation Copy number variation

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SPOT: Open Source visual data Analytics Platform for High-Dimensional Scientific data

SPOT: Open Source Visual Data Analytics Platform for High-Di...

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IEEE International conference on e-Science and Grid Computing

作者： F. Diblen J.J. Attema R. Bakhshi B. Stienen L. Hendriks S. Caron Netherlands eScience Center Amsterdam XG The Netherlands Netherlands eScience Center Amsterdam Netherlands Astro- and Particle Physics IMAPP Radboud Universiteit Nijmegen The Netherlands

One of the most challenging tasks in the field of information visualization is to design tools for high-dimensional scientific data. Existing visual data analytics tools either require expert knowledge (e.g. programming) or they are commercial tools such as Dash, Spotfire and Tableau. Spot provides a user-friendly, interactive data exploration environment for high-dimensional data sets. The main focus of Spot is on scientific use, with the aim to facilitate open science, data sharing and reuse. For example, iDARK project, which aims to collect existing dark matter models, will be using Spot in order to allow researchers to compare the particle physics data sets. This abstract gives an overview of main features of Spot, an open source data analytics platform, and its capabilities.

关键词： visualization data analysis data visualization Software databases Tools High energy physics

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visual analysis for Multi-Spectral Images Comparisons

Visual Analysis for Multi-Spectral Images Comparisons

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IEEE conference on visual Analytics Science and Technology (VAST)

作者： Li, Guozheng Chen, Shuai Li, Qiusheng Jiang, Zhibang Shi, Yuening Liu, Qiangqiang Liu, Xi Yuan, Xiaoru Peking Univ Key Lab Machine Percept Minist Educ Beijing Peoples R China Peking Univ Sch EECS Beijing Peoples R China Qihoo 360 Technol Co Ltd Beijing Peoples R China

ISBN: (纸本)9781538631638

The analysis for images helps people to gain insights by extracting the inner features and variances between them. However, it is hard to analyze the underlying events further without users participation. We proposes a visual analytic system based on collaborative tagging techniques to allow users to identify features and changes from multi-spectral images. We evaluate our system with mini challenge 3 of VAST Challenge 2017. The exploration results validate the efficiency and effectiveness of our system.

关键词： Feature extraction Histograms visual analytics Collaboration Pipelines Urban areas

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NEREx: Named-Entity Relationship exploration in Multi-Party Conversations

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COMPUTER GRAPHICS FORUM 2017年第3期36卷 213-225页

作者： El-Assady, Mennatallah Sevastjanova, Rita Gipp, Bela Keim, Daniel Collins, Christopher Univ Konstanz Constance Germany Univ Ontario Inst Technol Oshawa ON Canada

We present NEREx, an interactive visual analytics approach for the exploratory analysis of verbatim conversational transcripts. By revealing different perspectives on multi-party conversations, NEREx gives an entry point for the analysis through high-level overviews and provides mechanisms to form and verify hypotheses through linked detail-views. Using a tailored named-entity extraction, we abstract important entities into ten categories and extract their relations with a distance-restricted entity-relationship model. This model complies with the often ungrammatical structure of verbatim transcripts, relating two entities if they are present in the same sentence within a small distance window. Our tool enables the exploratory analysis of multi-party conversations using several linked views that reveal thematic and temporal structures in the text. In addition to distant-reading, we integrated close-reading views for a text-level investigation process. Beyond the exploratory and temporal analysis of conversations, NEREx helps users generate and validate hypotheses and perform comparative analyses of multiple conversations. We demonstrate the applicability of our approach on real-world data from the 2016 U.S. Presidential Debates through a qualitative study with three domain experts from political science.

关键词： visual analytics Conversation analysis Trial transcripts Political debates & debating Hypothesis

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xHiPP: eXtended Hierarchical Point Placement Strategy

xHiPP: eXtended Hierarchical Point Placement Strategy

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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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