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检索条件"任意字段=Conference on Visual Data Exploration and Analysis"

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SHAPELET BASED visual ASSESSMENT OF CLUSTER TENDENCY IN ANALYZING COMPLEX UPPER LIMBMOTION

SHAPELET BASED VISUAL ASSESSMENT OF CLUSTER TENDENCY IN ANAL...

引用

IEEE International conference on Acoustics, Speech and Signal Processing (ICASSP)

作者： Datta, Shreyasi Karmakar, Chandan Rathore, Punit Palaniswami, Marimuthu Univ Melbourne Dept Elect & Elect Engn Melbourne Vic Australia Deakin Univ Sch Informat Technol Melbourne Vic Australia MIT Senseable City Lab Cambridge MA 02139 USA

ISBN: (纸本)9781728176055

The evolution of ubiquitous sensors has led to the generation of copious amounts of waveform data. Human motion waveform analysis has found significance in clinical and home-based activity monitoring. exploration of cluster structure in such waveform data prior to developing learning models is an important pattern recognition problem. A prominent category of algorithms in this direction, known as visual Assessment of (cluster) Tendency (VAT), employs visual approaches to study cluster evolution through heat maps. This paper proposes shape-iVAT, a new relative of an improved VAT model, that captures local time-series characteristics through representative subsequences, known as shapelets, to identify interesting patterns in motion data. We propose an unsupervised method for shapelet extraction using maximin shape sampling and shape-based distance computation for selecting key shapelets representing characteristic motion patterns. These shapelets are used to transform waveform data into a dissimilarity matrix forVAT evaluation. We demonstrate that the proposed method outperforms standard VAT with global distance measures for identifying complex upper limb motion captured using a camera-based motion sensing device. We also show that our method has significance in efficient and interpretable cluster tendency assessment for anomaly detection and continuous motion monitoring.

关键词： Cluster tendency distance measures shapelet time-series upper limb motion

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data-driven visual analytics of Human Mobility data and green cover using Image Processing for Smart Cities

Data-driven visual analytics of Human Mobility data and gree...

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Intelligent Technologies (CONIT), International conference on

作者： Sivasubramanian Ramanathan Kulsoom Syed Tejaswini Chavan SVKMs NMIMS Navi Mumbai B. Tech Computer Science & Business Systems Mumbai India SVKMs NMIMS Navi Mumbai School of Technology Management & Engineering Mumbai India

This research paper presents an approach to data-driven visual analytics of human mobility data using Kernel Density Estimation visualized through heatmaps, highlighting the need for exploration of forecasting methods and intuitive visualizations using the ARIMA model. A specific geographic area is chosen for the demonstration of the scope of the proposed system. The system is a web application developed using Streamlit, an open-source python framework. To effectively implement the smart city concept, it is crucial to integrate diverse IoT systems, data sources, data streams, and analytical tools into a unified and seamless platform that facilitates the collection, analysis and presentation of information related to urban systems and subsystems. We propose taking into consideration several attributes when analyzing human mobility patterns, such as vehicle/traffic density, modes of transport, transport data, demographics, latitude, and longitude. Additionally, the system utilizes image processing as an efficient method for calculating urban green cover using morphological operations that are computationally cheaper and easy to use as compared to traditional surveys that are time and resource intensive. This information is used to develop smart plans to sustain or increase green cover in select areas, leading to the creation of sustainable and green smart cities.

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analysis and visual exploration of prediction algorithms for public bicycle sharing systems 15

Analysis and visual exploration of prediction algorithms for...

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International conference on Computer Graphics, visualization, Computer Vision and Image Processing 2021, CGVCVIP 2021, Connected Smart Cities 2021, CSC 2021 and Big data Analytics, data Mining and Computational Intelligence 2021, BIGDACI 2021 - Held at the 15th Multi-conference on Computer Science and Information Systems, MCCSIS 2021

作者： Ordoñez, Alexandra Cortez Vázquez, Pere-Pau ViRVIG Universitat Politècnica de Catalunya Carrer Llorens I Artigas 4-6 Barcelona08028 Spain

ISBN: (纸本)9789898704320

Public bicycle sharing systems have become an increasingly popular means of transportation in many cities around the world. However, the information shown in mobile apps or websites is commonly limited to the system's current status and is of little use for both citizens and responsible planning entities. The vast amount of data produced by these managing systems makes it feasible to elaborate and present predictive models that may help its users in the decision-making process. For example, if a user finds a station empty, the application could provide an estimation of when a new bicycle would be available. In this paper, we explore the suitability of several prediction algorithms applied to this case of bicycle availability, and we present a web-based tool to visually explore their prediction errors under different time frames. Even though a quick quantitative analysis may initially suggest that Random Forest yields a lower error, our visual exploration interface allows us to perform a more thorough analysis and detect subtle but relevant differences between algorithms depending on variables such as the station's behavior, hourly intervals, days, or types of days (weekdays and weekends). This case illustrates the potential of visual representation together with quantitative metrics to compare prediction algorithms with a higher level of detail, which can, in turn, assist application designers and decision-makers to dynamically adjust the best model for their specific scenarios. © 2021 International conference on Computer Graphics, visualization, Computer Vision and Image Processing 2021, CGVCVIP 2021, Connected Smart Cities 2021, CSC 2021 and Big data Analytics, data Mining and Computational Intelligence 2021, BIGDACI 2021 - Held at the 15th Multi-conference on Computer Science and Information Systems, MCCSIS 2021. All rights reserved.

关键词： Websites

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visual analysis of Intertextuality Based on Knowledge Graph 3

Visual Analysis of Intertextuality Based on Knowledge Graph

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3rd International conference on Artificial Intelligence and Advanced Manufacture, AIAM 2021

作者： Gao, Yi Duan, Chenming Cao, Qiuyang College of Arts & Information Engineering Dalian Polytechnic University Zhuanghe116400 China School of Computer Science and Technology Dalian University of Technology Dalian116024 China City Institute Dalian University of Technology Dalian116102 China

ISBN: (纸本)9781450385046

The rapid growth of the scientific literature is presenting researchers with increasing challenges. That means relying on human resources to complete the mining of massive resources or the exploration of cutting-edge trends in a short period of time is impossible. To address this problem, this paper presents a method for visual literature research based on knowledge graph. The method is to use the software CiteSpace to make a comprehensive visual analysis of the origin, development, themes and frontier trends of intertextuality theory, and use artificial intelligence to process data, information and images, so as to complete the construction of scientific knowledge graph of intertextuality theory, enrich literature resources and improve the scientific research environment. The knowledge graph is a scientific measurement method which can visually present literature research results and ultimately realize the grand goal of artificial intelligence serving the development of scientific research. © 2021 ACM.

关键词： Knowledge graph

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Multi-modal data exploration in a Mixed Reality Environment Using Coordinated Multiple Views

Multi-modal Data Exploration in a Mixed Reality Environment ...

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Human Interface and the Management of Information (HIMI) part of International conference on Human-Computer Interaction (HCII)

作者： Sardana, Disha Kahu, Sampanna Yashwant Gracanin, Denis Matkovic, Kresimir Virginia Tech Blacksburg VA 24061 USA VRVis Res Ctr Vienna Austria

ISBN: (纸本)9783030783204;9783030783211

Immersive analytics is an emerging field of data exploration and analysis in immersive environments. The main idea is to use visual analytics in a fully immersive 3D space. The availability of immersive Extended Reality systems has increased tremendously recently, but it is still not as widely used as conventional 2D displays. We describe an immersive analysis system for spatio-temporal data and compare how it performs in an immersive environment and on a conventional 2D display. We provide a novel view called map-plot that enables analysis and exploration of spatial time series data. We also design an embodied interaction for the map-plot view. The approach is realized based on the coordinated multiple views paradigm. In addition to the map-plot view, several standard views are available. Voice commands and spatial audio are used to support interaction and increase users' embodiment. The findings from a user study show that developed system is much more efficient in a real immersive environment than using conventional 2D displays.

关键词： Immersive analytics Mixed reality Embodied interaction Coordinated multiple views

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Effect of Gaze visualization on Task Efficiency and User Behavior for Guidance Scenarios in Co-Located AR Collaboration

Effect of Gaze Visualization on Task Efficiency and User Beh...

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Virtual Reality and 3D User Interfaces Abstracts and Workshops (VRW), IEEE conference on

作者： Froso Sarri Katerina Mania School of Electrical & Computer Engineering Technical University of Crete Greece

ISBN: (数字)9798331514846

ISBN: (纸本)9798331525637

Augmented Reality (AR)-enabled collaboration enhances contextual guidance in shared environments, where unambiguous cues are essential, for swift and accurate task completion. Gaze-based cues offer clear, non-verbal indication of intent, reducing interaction ambiguity. While prior research has studied the effectiveness of gaze-based visualization in scenarios where collaborators share the same level of knowledge, less attention has been shown to guidance scenarios, where one user provides task-related information to another. Existing studies often focus on task outcomes (e.g., completion time, error rate) without analyzing gaze data to gain further insight into user behavior and attention. This study leverages gaze-based cues as an active means of communication in guidance scenarios, assessing their impact on both task performance and user behavior through gaze data analysis. The developed system incorporates gaze-based visualizations, including dynamic representations of gaze behavior, to deliver task-critical guidance. These are compared to verbal instructions using quantitative metrics for task effectiveness and gaze analysis, alongside subjective feedback, to determine whether they reduce ambiguity and enhance collaboration. Our study showed that gaze-based visualizations significantly reduced task related errors, with contextual triggered cues offering the best balance between speed and correctness. Gaze analysis revealed that gaze-based visualizations effectively directed participants’ attention to task-relevant areas, reducing unnecessary visual exploration. Participants reported lower perceived workload in gaze-based conditions, further supporting their effectiveness in enhancing collaboration and reducing ambiguity.

关键词： Human computer interaction Measurement Hands visualization Three-dimensional displays Error analysis Collaboration data visualization Information sharing Augmented reality

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visual analysis of Spatio-temporal Phenomena with 1D Projections

Visual Analysis of Spatio-temporal Phenomena with 1D Project...

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Eurographics / IEEE VGTC conference on visualizaation

作者： Franke, M. Martin, H. Koch, S. Kurzhals, K. Univ Stuttgart Stuttgart Germany Swiss Fed Inst Technol Zurich Switzerland Inst Adv Res Artificial Intelligence Vienna Austria

It is crucial to visually extrapolate the characteristics of their evolution to understand critical spatio-temporal events such as earthquakes, fires, or the spreading of a disease. Animations embedded in the spatial context can be helpful for understanding details, but have proven to be less effective for overview and comparison tasks. We present an interactive approach for the exploration of spatio-temporal data, based on a set of neighborhood-preserving 1D projections which help identify patterns and support the comparison of numerous time steps and multivariate data. An important objective of the proposed approach is the visual description of local neighborhoods in the 1D projection to reveal patterns of similarity and propagation. As this locality cannot generally be guaranteed, we provide a selection of different projection techniques, as well as a hierarchical approach, to support the analysis of different data characteristics. In addition, we offer an interactive exploration technique to reorganize and improve the mapping locally to users' foci of interest. We demonstrate the usefulness of our approach with different real-world application scenarios and discuss the feedback we received from domain and visualization experts.

关键词： Computer graphics

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VEHICLE: Validation and exploration of the Hierarchical Integration of Conflict Event data

VEHICLE: Validation and Exploration of the Hierarchical Inte...

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Eurographics / IEEE VGTC conference on visualizaation

作者： Mayer, B. Lawonn, K. Donnay, K. Preim, B. Meuschke, M. Univ Magdeburg Dept Simulat & Graph Magdeburg Germany Univ Jena Dept Comp Sci Jena Germany Univ Zurich Dept Polit Sci Zurich Switzerland

The exploration of large-scale conflicts, as well as their causes and effects, is an important aspect of socio-political analysis. Since event data related to major conflicts are usually obtained from different sources, researchers developed a semi-automatic matching algorithm to integrate event data of different origins into one comprehensive dataset using hierarchical taxonomies. The validity of the corresponding integration results is not easy to assess since the results depend on user-defined input parameters and the relationships between the original data sources. However, only rudimentary visualization techniques have been used so far to analyze the results, allowing no trustworthy validation or exploration of how the final dataset is composed. To overcome this problem, we developed VEHICLE, a web-based tool to validate and explore the results of the hierarchical integration. For the design, we collaborated with a domain expert to identify the underlying domain problems and derive a task and workflow description. The tool combines both traditional and novel visual analysis techniques, employing statistical and map-based depictions as well as advanced interaction techniques. We showed the usefulness of VEHICLE in two case studies and by conducting an evaluation together with conflict researchers, confirming domain hypotheses and generating new insights.

关键词： Integration

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Adaptive visualizations for enhanced data understanding and interpretation 21

Adaptive visualizations for enhanced data understanding and ...

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29th ACM conference on User Modeling, Adaptation and Personalization, UMAP 2021

作者： Amyrotos, Christos UCLan Cyprus Pyla Cyprus

ISBN: (纸本)9781450383660

In a data driven economy where data volume and dimensions are explosively increasing, businesses rely on business intelligence and analytics (BI&A) platforms for analysing their data and coming to beneficial decisions. With the ever-growing generation of data, the process of data analysis is becoming more complicated for the business users, as the exploration of more demanding use cases increases. While the existing BI&A platforms provide myriads of data visualizations that support data exploration, none of those account for the user's individual differences, needs or requirements, and thus may hinder the user's understanding of visual data and consequently their decision-making processes. This work embarks on an interdisciplinary endeavour to introduce a human-centred adaptive data visualizations framework in the context of business, as the core of an adaptive data analytics platform, that aims to enhance the business user's decision making by increasing her understanding of data. The framework is built using a multi-dimensional human-centred user model that goes beyond traditional user characteristics and accounts for cognitive factors, domain expertise and experience and factors related to the business context i.e., data, visualizations and tasks;a data visualization engine that will recommend to the unique-user the best-fit data visualizations based on the abovementioned user model;and an intelligent data analytics component that enhances the efficiency and effectiveness of the data exploration process by leveraging user interactions during the explorations to further inform the user model on the user's expertise and experience. © 2021 Owner/Author.

关键词： data visualization

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Mondrian Abstraction and Language Model Embeddings for Differential Pathway analysis

Mondrian Abstraction and Language Model Embeddings for Diffe...

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IEEE International conference on Bioinformatics and Biomedicine (BIBM)

作者： Fuad Al Abir Jake Y. Chen Department of Computer Science University of Alabama at Birmingham Birmingham AL USA Systems Pharmacology AI Research Center (SPARC) School of Medicine University of Alabama at Birmingham Birmingham AL USA

ISBN: (数字)9798350386226

ISBN: (纸本)9798350386233

In this study, we introduce the Mondrian Map, an innovative visualization tool inspired by Piet Mondrian’s abstract art, to address the complexities inherent in visualizing biological networks. The Mondrian Map enables clear and meaningful representations of biological pathways by converting intricate biological data into a structured and intuitive format, facilitating a deeper understanding of molecular dynamics. Each pathway is represented by a square whose size corresponds to fold change, with color indicating the direction of regulation (up or down) and statistical significance. The spatial arrangement of pathways is derived from language model embeddings, preserving neighborhood relationships and enabling the identification of clusters of related pathways. Additionally, colored lines highlight potential crosstalk between pathways, with distinctions between short- and long-range functional interactions. In a glioblastoma multiforme (GBM) case study, the Mondrian Map effectively revealed distinct pathway patterns across patient profiles at different stages of disease progression. By surpassing traditional enriched pathway tables and presenting pathways in a relative context without causing visual information overload, this work highlights the tool’s potential to enhance downstream bioinformatics analyses by providing a more comprehensive and visually accessible overview of pathway interactions, offering new avenues for therapeutic exploration and personalized medicine.

关键词： Analytical models visualization Image color analysis Biological system modeling Precision medicine data visualization data models Regulation Bioinformatics Diseases

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