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Towards a Conceptual Model for data Narratives 39th

Towards a Conceptual Model for Data Narratives

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39th International conference on Conceptual Modeling (ER)

作者： El Outa, Faten Francia, Matteo Marcel, Patrick Peralta, Veronika Vassiliadis, Panos Univ Tours Blois Loir & Cher France Univ Ioannina Ioannina Greece Univ Bologna Cesena Italy

ISBN: (纸本)9783030625221;9783030625214

data narration is the activity of producing stories supported by facts extracted from data analysis, possibly using interactive visualizations. In spite of the increasing interest in data narration in several communities (e.g. journalism, business, e-government), there is no consensual definition of data narrative, let alone a conceptual or logical model of it. In this paper, we propose a conceptual model of data narrative for exploratory data analysis. It is based on four layers that reflect the transition from raw data to the visual rendering of the data story: factual, intentional, structural and presentational. This model aims to support the entire lifecycle of building a data narrative, starting from an intentional goal: fetch and explore data, bring out highlights, derive important messages, structure the plot of the data narrative, and render it in a visual manner. Our contributions include a description of the model and its instantiation for several real examples showing that it covers data narration needs.

关键词： data narrative visual narrative data storytelling data exploration

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JobViewer: Graph-based visualization for Monitoring High-Performance Computing System

JobViewer: Graph-based Visualization for Monitoring High-Per...

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IEEE/ACM International Symposium on Big data Computing (BDC)

作者： Tommy Dang Ngan V.T. Nguyen Jie Li Alan Sill Jon Hass Yong Chen Department of Computer Science Texas Tech University Lubbock Texas USA High Performance Computing Center Texas Tech University Lubbock Texas USA Dell Inc Austin Texas USA

visualization aims to strengthen data exploration and analysis, especially for complex and high-dimensional data. High-performance computing (HPC) systems are typically large and complicated instruments that generate massive performance and operation time series. Monitoring HPC systems’ performance is a daunting task for HPC admins and researchers due to their dynamic natures. This work proposes a visual design using the bipartite graph’s idea to visualize HPC clusters’ structure, metrics, and job scheduling data. We built a web-based prototype, called JobViewer, that integrates advanced methods in visualization and human-computer interaction (HCI) to demonstrate the benefits of visualization in real-time monitoring HPC centers. We also showed real use cases and a user study to validate the efficiency and highlight the current approach’s drawbacks.

关键词： Human computer interaction visualization High performance computing Source coding Time series analysis data visualization Bipartite graph

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A Language-based Interface for analysis of Digital Storytelling 20

A Language-based Interface for Analysis of Digital Storytell...

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Working conference on Advanced visual Interfaces (AVI)

作者： Gloder, Alberto Ducceschi, Luca Zancanaro, Massimo Univ Trento Dept Psychol & Cognit Sci Rovereto TN Italy Univ Trento FBK Rovereto TN Italy

ISBN: (纸本)9781450375351

In this paper, we introduce a tool aimed at supporting deep qualitative analysis of digital comics. The tool exploits language-based technologies to facilitate the exploration of relatively large sets of comics. The core idea is that the specific words used in the comics are both an important element of the analysis and an index to navigate and explore the dataset. The design concept has been validated in a pilot study and the findings provide evidence that the approach meets the needs of qualitative analysts with the potential of improving their practices.

关键词： data analysis Digital storytelling Comics Machine Learning Natural language processing

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visual Analytics Meets Process Mining: Challenges and Opportunities

Visual Analytics Meets Process Mining: Challenges and Opport...

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International conference on Process Mining (ICPM)

作者： Silvia Miksch Institute of Visual Computing and Human-Centered Technology Vienna University of Technology (TU Wien) Favoritenstraße 9-11/193-07 Vienna Austria

visual Analytics integrates the outstanding capabilities of humans in terms of visual information exploration with the enormous processing power of computers to form a powerful knowledge discovery environment. In other words, visual Analytics is the science of analytical reasoning facilitated by interactive interfaces and captures the information discovery process keeping the human in the loop. Process Mining aims to extract information and knowledge from event logs to discover, monitor, and improve processes in a variety of application domains. Such event data or traces of activities often possess data quality issues as well as exhibit unexpected behavior and complex relations. Consequently, before and during the implementation of interactive (automated or semi-automated) analysis methods, such as Process Mining algorithms, the analyst needs to explore, investigate, and understand the data at hand in order to decide which analysis methods might be appropriate. The combination of interactive visual data analyses and exploration with Process Mining algorithms makes complex information structures more comprehensible and facilitates new insights. In this talk, I will illustrate the concepts of visual Analytics, how visual Analytics combined with Process Mining techniques supports to extract more insights from complex event data, and elaborate about the challenges and opportunities for analyzing process data with visual Analytics methods. Various examples will illustrate what has been achieved so far and show possible future directions and challenges.

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27th International conference on MultiMedia Modeling, MMM 2021

27th International Conference on MultiMedia Modeling, MMM 20...

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27th International conference on MultiMedia Modeling, MMM 2021

ISBN: (纸本)9783030678319

The proceedings contain 50 papers. The special focus in this conference is on MultiMedia Modeling. The topics include: Unsupervised Gaze: exploration of Geometric Constraints for 3D Gaze Estimation;Median-Pooling Grad-CAM: An Efficient Inference Level visual Explanation for CNN Networks in Remote Sensing Image Classification;multi-granularity Recurrent Attention Graph Neural Network for Few-Shot Learning;EEG Emotion Recognition Based on Channel Attention for E-Healthcare Applications;the MovieWall: A New Interface for Browsing Large Video Collections;keystroke Dynamics as Part of Lifelogging;HTAD: A Home-Tasks Activities dataset with Wrist-Accelerometer and Audio Features;MNR-Air: An Economic and Dynamic Crowdsourcing Mechanism to Collect Personal Lifelog and Surrounding Environment dataset. A Case Study in Ho Chi Minh City, Vietnam;kvasir-Instrument: Diagnostic and Therapeutic Tool Segmentation dataset in Gastrointestinal Endoscopy;tropical Cyclones Tracking Based on Satellite Cloud Images: database and Comprehensive Study;catMeows: A Publicly-Available dataset of Cat Vocalizations;search and Explore Strategies for Interactive analysis of Real-Life Image Collections with Unknown and Unique Categories;graph-Based Indexing and Retrieval of Lifelog data;on Fusion of Learned and Designed Features for Video data Analytics;XQM: Interactive Learning on Mobile Phones;a Multimodal Tensor-Based Late Fusion Approach for Satellite Image Search in Sentinel 2 Images;canopy Height Estimation from Spaceborne Imagery Using Convolutional Encoder-Decoder;implementation of a Random Forest Classifier to Examine Wildfire Predictive Modelling in Greece Using Diachronically Collected Fire Occurrence and Fire Mapping data;mobile eHealth Platform for Home Monitoring of Bipolar Disorder;multimodal Sensor data analysis for Detection of Risk Situations of Fragile People in @home Environments.

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27th International conference on MultiMedia Modeling, MMM 2021

27th International Conference on MultiMedia Modeling, MMM 20...

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27th International conference on MultiMedia Modeling, MMM 2021

ISBN: (纸本)9783030678340

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Pioneering Pain Relief: Exploring Neuromodulation with Electrical Impulses and Mechanical Techniques for Effective Pain Management

Pioneering Pain Relief: Exploring Neuromodulation with Elect...

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Intelligent Systems and Computer Vision (ISCV)

作者： Asmaa Radouane Mohamed Touil Youness Kadil Imane Rahmoune Houda Filali Laboratory of Pharmacology and Toxicology Faculty of Medicine and Pharmacy of Casablanca Hassan II University Casablanca Morocco EEIS Laboratory Enset Mohammadia Hassan II University Casablanca Morocco

ISBN: (数字)9798350350180

ISBN: (纸本)9798350350197

According to a World Health Organization report, chronic pain is becoming a widespread issue in the larger context of global health, impacting about 20% of the world's population. This continuous problem includes several types of chronic pain that significantly affect people's everyday lives and overall health. Neuromodulation - the use of neuromodulators, electrical and mechanical stimulation - is at the forefront of innovative pain management strategies. Here we examine recent advances in the effects of neuromodulation on three different types of pain, such as dental neuralgia and orofacial pain, osteoarthritis, which is considered to be one of the most painful sensations, and the pain associated with Parkinson's disease. To carry out this study, we undertook an in-depth exploration of the relevant scientific literature. Our approach consisted of carefully reading and understanding numerous articles from major databases. Each article was carefully read through to ensure that the methodology, findings, and consequences were understood. The amalgamation of this data has facilitated the cultivation of a refined comprehension of the present status of knowledge in this particular field, establishing the fundamental groundwork for subsequent investigations in this domain. Our in-depth analysis of the effect of electrical stimulation through various neuromodulation methods reveals compelling insights into advances in the field of pain management. Through this review, we present the results of different methods, highlighting their efficacy and contributions to chronic pain relief, showing their ability to modulate in a complex way the neural circuits associated with pain processing. This review highlights the paradigm shifts in pain treatment facilitated by neuromodulation. The controlled application of electrical impulses offers a promising route to personalized and effective relief for people suffering from chronic pain.

关键词： Somatosensory Spinal cord Pain Reviews Transcranial magnetic stimulation Parkinson's disease Sociology

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A Model for the Progressive visualization of Multidimensional data Structure 14th

A Model for the Progressive Visualization of Multidimensiona...

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14th International Joint conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISAPP)

作者： Ventocilla, Elio Riveiro, Maria Univ Skovde Sch Informat Dept Comp & Syst Sci Skovde Sweden Jonkoping Univ Sch Engn Dept Comp Sci & Informat Jonkoping Sweden

ISBN: (纸本)9783030415907;9783030415891

This paper presents a model for the progressive visualization and exploration of the structure of large datasets. That is, an abstraction on different components and relations which provide means for constructing a visual representation of a dataset's structure, with continuous system feedback and enabled user interactions for computational steering, in spite of size. In this context, the structure of a dataset is regarded as the distance or neighborhood relationships among its data points. Size, on the other hand, is defined in terms of the number of data points. To prove the validity of the model, a proof-of-concept was developed as a visual Analytics library for Apache Zeppelin and Apache Spark. Moreover, nine user studies where carried in order to assess the usability of the library. The results from the user studies show that the library is useful for visualizing and understanding the emerging cluster patterns, for identifying relevant features, and for estimating the number of clusters k.

关键词： data structure Progressive visualization Large data analysis Multidimensional projection Multidimensional data Growing neural gas visual analytics Exploratory data analysis

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Sensitivity analysis by the PROMETHEE-GAIA method: Algorithms evaluation for COVID-19 prediction 8

Sensitivity Analysis by the PROMETHEE-GAIA method: Algorithm...

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8th International conference on Information Technology and Quantitative Management, ITQM 2020 and 2021

作者： Moreira, Miguel Ângelo Lellis Gomes, Carlos Francisco Simões dos Santos, Marcos da Silva Júnior, Antonio Carlos de Araújo Costa, Igor Pinheiro Fluminense Federal University RJ Niterói24210-240 Brazil Military Institute of Engineering Urca RJ 22290-270 Brazil Federal University of Paraná PR Curitiba80060-000 Brazil

With the expansion of coronavirus in the World, the search for technology solutions based on the analysis and prospecting of diseases has become constant. The paper addresses a machine learning algorithms analysis used to predict and identify infected patients. For analysis, we use a multicriteria approach using the PROMETHEE-GAIA method, providing the structuring of alternatives respective to a set of criteria, thus enabling the obtaining of their importance degree under the perspective of multiple criteria. The study approaches a sensitivity analysis, evaluating the alternatives using the PROMETHEE I and ii methods, along with the GAIA plan, both implemented by the visual PROMETHEE computational tool, exploring numerical and graphical resources. The analysis model proves to be effective, guaranteeing the ranking of alternatives by inter criterion evaluation and local results with intra criterion evaluation, providing a transparent analysis concerning the selection of prediction algorithms to combat the COVID-19 pandemic. © 2021 The Authors. Published by Elsevier B.V.

关键词： COVID-19

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visual Abstraction of Geographical Point data with Spatial Autocorrelations 15

Visual Abstraction of Geographical Point Data with Spatial A...

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15th IEEE conference on visual Analytics Science and Technology (VAST)

作者： Zhou, Zhiguang Zhang, Xinlong Yang, Zhendong Chen, Yuanyuan Liu, Yuhua Wen, Jin Chen, Binjie Zhao, Ying Chen, Wei Zhejiang Univ Finance & Econ Sch Informat Hangzhou Peoples R China Zhejiang Univ State Key Lab CAD & CG Hangzhou Peoples R China Cent South Univ Sch Comp Sci & Engn Changsha Peoples R China

ISBN: (纸本)9781728180090

Scatterplots are always employed to visualize geographical point datasets, which often suffer from an overdraw problem due to the increase of data sizes. A variety of sampling strategies have been proposed to reduce overdraw and visual clutter with the spatial densities of points taken into account. However, informative attributes associated with the points also play significant roles in the exploration of geographical datasets. In this paper, we propose an attribute-based abstraction method to simplify the cluttered visualization of large-scale geographical points. Spatial autocorrelations are utilized to measure the attribute relationships of points in local areas, and a novel attribute-based sampling model is designed to generate a subset of points to preserve both density and attribute characteristics of original geographical points. A set of visual designs and user-friendly interactions are implemented, enabling users to capture the spatial distribution of geographical points and get deeper insights into the attribute features across local areas. Case studies and quantitative comparisons based on the real-world datasets further demonstrate the effectiveness of our method in the abstraction and exploration of large-scale geographical point datasets.

关键词： visual Abstraction Spatial Autocorrelation Sampling Geospatial analysis

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