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4th IEEE International conference on Quantum Computing and Engineering, QCE 2023

作者： Steed, Chad A. Chae, Junghoon Dasgupta, Samudra Humble, Travis S. Oak Ridge National Laboratory Oak Ridge United States

ISBN: (纸本)9798350343236

We present the preliminary design and results of QVis, a visual analytics tool for exploring quantum device performance data. QV is helps uncover temporal and multivariate variations in noise properties of quantum devices. We describe the implementations of these methods as well as applications to the analysis of a 127-qubit data set derived from the IBM washington processor over a 16-month period. Both human-interactive and semi-automated analytic methods are included to address requirements for visual exploration, thresholding, and clustering techniques. Our application of QVis to real-world scenarios demonstrates the ability to reveal noteworthy patterns in the behavior of the critical characterization metrics. © 2023 IEEE.

关键词： visualization

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A Survey of visualization and analysis in High-Resolution Connectomics

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COMPUTER GRAPHICS FORUM 2022年第3期41卷 573-607页

作者： Beyer, Johanna Troidl, Jakob Boorboor, Saeed Hadwiger, Markus Kaufman, Arie Pfister, Hanspeter Harvard Univ Visual Comp Grp Cambridge MA 02138 USA SUNY Stony Brook Dept Comp Sci Stony Brook NY 11794 USA King Abdullah Univ Sci & Technol Visual Comp Ctr Thuwal Saudi Arabia

The field of connectomics aims to reconstruct the wiring diagram of Neurons and synapses to enable new insights into the workings of the brain. Reconstructing and analyzing the Neuronal connectivity, however, relies on many individual steps, starting from high-resolution data acquisition to automated segmentation, proofreading, interactive data exploration, and circuit analysis. All of these steps have to handle large and complex datasets and rely on or benefit from integrated visualization methods. In this state-of-the-art report, we describe visualization methods that can be applied throughout the connectomics pipeline, from data acquisition to circuit analysis. We first define the different steps of the pipeline and focus on how visualization is currently integrated into these steps. We also survey open science initiatives in connectomics, including usable open-source tools and publicly available datasets. Finally, we discuss open challenges and possible future directions of this exciting research field.

关键词： CCS Concepts • Human-centered computing → visualization systems and tools Scientific visualization • Applied computing → Life and medical sciences Systems biology

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data Insights Nexus-Innovative Platform for Dynamic Modeling and visual Analytics 7

Data Insights Nexus-Innovative Platform for Dynamic Modeling...

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7th International conference on Inventive Computation Technologies, ICICT 2024

作者： Nagaraj, P. Muneeswaran, V. Gopal Reddy, B. Venu Naga Ramya Sri, P. Gayathri, N. Dastagiri Reddy, T. Venkata Kalasalingam Academy of Research And Education Department of Computer Science And Engineering Virudhunagar Krishnankoil India Kalasalingam Academy of Research And Education Department of Electronics And Communication Engineering Virudhunagar Krishnankoil India

ISBN: (纸本)9798350359299

The study explores the terrain of data analysis by delving into data Insights Nexus, a comprehensive system at the forefront of this field. The application integrates machine learning modeling, dynamic 2D/3D visualization, and robust anomaly detection within a unified framework. User-friendly interfaces empower effortless construction, fine-tuning, and deployment of intricate machine-learning models for deep insights from complex datasets. Advanced 2D/3D visualization transforms data relationships into visually intuitive representations. Furthermore, the system employs state-of-the-art anomaly detection, bolstering result accuracy by identifying elusive irregularities and outliers. data Insights Nexus, as a pioneering solution, holds the potential to redefine data exploration and analysis across diverse research domains. It not only enhances efficiency but also offers unprecedented effectiveness in dealing with complex data. This research meticulously explores its components, applications, and potential impacts, providing invaluable insights for data analysts and researchers. The application represents a groundbreaking tool that promises to revolutionize research methodologies, enabling researchers to uncover new facets of data-driven discoveries. © 2024 IEEE.

关键词： visualization

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exploration on Central Nervous System Injury Detection Technology Based on data analysis 15

Exploration on Central Nervous System Injury Detection Techn...

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15th IEEE International conference on Computational Intelligence and Communication Networks, CICN 2023

作者： Fu, Yunxia College of Humanities and Teachers Heilongjiang University of Technology Heilongjiang Ji'xi158100 China

ISBN: (纸本)9798350324433

With the continuous development of artificial intelligence technology, big data and deep learning algorithms are playing an increasingly important role in the field of Medical imaging analysis, which has become the main trend of Medical imaging analysis and research. How to establish an objective, comprehensive and accurate clinical diagnosis model through Medical imaging big data analysis technology has become the main development direction of Medical imaging data processing and clinical application. Taking data analysis as an example and combining with specific application cases, this paper introduced the research progress of the central nervous system damage detection technology based on data analysis in detail from the aspects of data acquisition, data preprocessing, Feature selection, model training, model evaluation and algorithm selection, and finally achieved the highest accuracy of 97.6%. At the same time, the article also prospected the prospects of this technology from the perspectives of technological development trends and clinical applications, in order to provide reference for future research work in this field. © 2023 IEEE.

关键词： Damage detection

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Scientometric analysis of Building Energy analysis in the Construction During 2005–2022

Scientometric Analysis of Building Energy Analysis in the Co...

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International conference on Civil Engineering Innovative Development in Engineering Advances, ICC-IDEA 2023

作者： Nakkeeran, G. Krishnaraj, L. Department of Civil Engineering Faculty of Engineering and Technology SRM Institute of Science and Technology Tamil Nadu Kattankulathur 603203 India

ISBN: (纸本)9789819962327

The aim of this paper is to carry out a comprehensive review of 921 papers in the Scopus database on "building energy analysis using BIM" from 2005 to 2022. Using a systematic approach to the primary data in each document, we gather the various types of publications, citations, and predictions. visual analysis software Biblioshiny 2.0 is used to show the evolution of publications. There are guidelines for selecting documents for citation analysis. All of these factors are considered when assigning ranks based on citation count, the total number of documents and the average number of citations per document. Author-keywords and analysis of the data were used to identify software development hotspots and trends for building energy analysis. Bibliometric analysis is beneficial to building energy analysis researchers. Building energy analysis publications are studied extensively in a bibliometric study. To aim researchers in software for building energy analysis exploration, scholars should be aware of this field's hotspots’ current and future state. © 2024, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Architectural design

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DM-Vis: A Graph-Based data Reconnaissance System for Multi-domain Urban data 41st

DM-Vis: A Graph-Based Data Reconnaissance System for Multi...

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41st Computer Graphics International conference, CGI 2024

作者： Wang, Hesong Wang, Song Wang, Yanru Long, Hao School of Computer Science and Technology Southwest University of Science and Technology Mianyang China

ISBN: (纸本)9783031818059

As urbanization accelerates, data on the diverse aspects of urban life, including the environment, finance, and transportation, are increasing exponentially. Single-domain data analysis falls short for complex tasks, necessitating multi-source data integration to offer decision-makers a holistic view of interconnections and trends. However, challenges arise in effectively representing data associations and swiftly identifying relevant datasets. This paper proposes techniques to integrate multi-domain urban data relationships and facilitate the acquisition of target datasets. We introduce a method to transform data entities into association graphs and a data extraction method to aid users in understanding data relationships and acquiring target datasets. Using these methods, we developed DM-Vis, a data reconnaissance visual analysis system, to facilitate data integration and exploration from multiple sources. Real-data case studies and positive feedback from users and experts validate the utility and reliability of our approach. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： data extract data integration data reconnaissance multi-domain data query embedding

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visual analysis of Cyclic Time Series with Semantic Zoom

Visual Analysis of Cyclic Time Series with Semantic Zoom

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International conference on Information visualisation (iv)

作者： Patrick Louis Belgin Mutlu Josef Suschnigg Tobias Schreck Pro2Future GmbH Graz Austria Graz University of Technology Graz Austria

ISBN: (数字)9798350380163

ISBN: (纸本)9798350380170

visual analysis (VA) tasks often involve exploring large and complex multi-dimensional datasets to identify trends and anomalies. However, the challenge lies in displaying all the data and maintaining the desired level of detail within the limited screen space. In this paper, we propose a solution that incorporates multiple visualizations and semantic zooming to address this compromise. Our visualization tool focuses on cycle-dependent data, showcasing time series with repetitive behavior. Through semantic zooming, cyclic time series data can be displayed in large quantities and high levels of detail without the need for multiple views. Our proposed tool includes three independent visualizations: line plots, horizon graphs, and adaptive heatmaps. By offering different visualization options, we aim to provide a rich and flexible analytical experience that response to the different user needs and encourages comprehensive data exploration. The tool accommodates both novice and expert users, allowing for intuitive analysis as well as advanced techniques for detailed examination. Our approach follows the mantra of “overview first, zoom and filter, then details-on-demand” facilitating rapid detection and exploration of patterns and trends. In this paper, we present the detailed design, interaction capabilities with semantic zoom, and the results of a user study that demonstrate the effectiveness and usefulness of our proposed tool.

关键词： Heating systems visualization Semantics Time series analysis data visualization Market research Usability

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The use of image graph in the medical data analyzing 1

The use of image graph in the medical data analyzing

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1st IEEE International conference on Advances in data-Driven Analytics and Intelligent Systems, ADACIS 2023

作者： Laghridat, Charifa Essalih, Mohamed Mounir, Ilham Cadi Ayyad University Lapssii The Safi's Graduate School of Technology Marrakesh Morocco Mohammed v University in Rabat Lrit Cnrst Urac 29 Rabat It Center Faculty of Sciences Morocco

ISBN: (纸本)9798350344158

Medical data analysis is a critical process aimed at extracting valuable insights and knowledge from complex healthcare information. It plays a vital role in enhancing diagnostics, treatment planning, and medical research. In this context, medical images serve as a fundamental source of information, providing visual representations of anatomical structures and pathological conditions. Image processing techniques, utilizing mesh-based representations, offer unique opportunities for advancing the analysis of medical *** article presents a new method for analyzing medical data based on the concept of meshing. By representing medical images as undirected graphs, the proposed approach enables efficient exploration and analysis of spatial relationships. © 2023 IEEE.

关键词： Medical imaging

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PCAPFunnel: A Tool for Rapid exploration of Packet Capture Files 25

PCAPFunnel: A Tool for Rapid Exploration of Packet Capture F...

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25th International conference Information visualisation (iv) - AI and visual Analytics and data Science

作者： Uhlar, Juraj Holkovic, Martin Rusnak, Vit Flowmon Networks Brno Czech Republic Brno Univ Technol Fac Informat Technol Brno Czech Republic Masaryk Univ Inst Comp Sci Brno Czech Republic

ISBN: (纸本)9781665438278

Analyzing network traffic is one of the fundamental tasks in both network operations and security incident analysis. Despite the immense efforts in workflow automation, an ample portion of the work still relies on manual data exploration and analytical insights by domain specialists. Current state-of-the-art network analysis tools provide high flexibility at the expense of usability and have a steep learning curve. Recent-often web-based-analytical tools emphasize interactive visualizations and provide simple user interfaces but only limited analytical support. This paper describes the tool that supports the analytical work of network and security operators. We introduce typical user tasks and requirements. We also present the filtering funnel metaphor for exploring packet capture (PCAP) files through visualizations of linked filter steps. We have created PCAPFunnel, a novel tool that improves the user experience and speeds up packet capture data analysis. The tool provides an overview of the communication, intuitive data filtering, and details of individual network nodes and connections between them. The qualitative usability study with nine domain experts confirmed the usability and usefulness of our approach for the initial data exploration in a wide range of tasks and usage scenarios, from educational purposes to exploratory network data analysis.

关键词： data analysis data visualization Network traffic analysis Packet captures

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A Tool for visual exploration and analysis of Solar Photovoltaic Module data

A Tool for Visual Exploration and Analysis of Solar Photovol...

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2023 Workshops of the EDBT/ICDT Joint conference, EDBT/ICDT-WS 2023

作者： Stamatopoulos, Vassilis Maroulis, Stavros Kozanis, Konstantinos Psarros, Ioannis Papastefanatos, George Giannopoulos, Giorgos Terrovitis, Manolis ATHENA Research Center Greece

Solar photovoltaic (PV) modules are a popular source of clean energy, and effective monitoring and optimization of their performance requires the ability to explore and analyze the measurement data from their sensors. In this work, we introduce a tool for the visual analysis of such data stored in raw measurement files, offering efficient interactive visualization directly on the files. The tool includes a soiling detection module, and novel UI modules that combine time series data visualization with advanced solar panel analytics presentation. © 2023 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0). CEUR Workshop Proceedings (***)

关键词： Solar energy

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