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ChartNavigator: An Interactive Pattern Identification and Annotation Framework for Charts

ieee TRANSACTIONS ON KNOWLEDGE AND data ENGINEERING 2023年第2期35卷 1258-1269页

作者： Zhang, Tianye Feng, Haozhe Chen, Wei Chen, Zexian Zheng, Wenting Luo, Xiaonan Huang, Wenqi Tung, Anthony Zhejiang Univ State Key Lab CAD & CG Hangzhou 310058 Peoples R China Zhejiang Univ China Southern Power Grid Joint Res Hangzhou 310058 Peoples R China Guilin Univ Elect Technol Guilin 541214 Peoples R China China Southern Power Grid Digital Grid Res Inst Guangzhou 510670 Peoples R China Natl Univ Singapore Dept Comp Sci Singapore 117417 Singapore

Patterns in charts refer to interesting visual features or forms. Identifying patterns not only helps analysts understand the 'shape' of the data but also supports better and faster decision-making. Existing solutions for identifying patterns in charts require a large number of labeled data instances, making it intractable without user supervision. In this paper, we propose ChartNavigator, an interactive pattern identification and annotation framework for unlabeled visualization charts. ChartNavigator leverages a novel chart-sensitive deep factor model to map patterns into a low-dimensional factor representation space, and facilitates rich analysis with the derived representations. We design and implement a visual interface to support efficient identification and annotation of potential patterns in charts. Evaluations with multiple datasets show that our approach outperforms the baseline models in identifying and annotating patterns.

关键词： visualization data models Annotations data visualization Solid modeling Inference algorithms Estimation Pattern identification chart variational autoencoder user interaction visual analysis

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EASYVIEW: Bringing Performance Profiles into Integrated Development Environments 24

EASYVIEW: Bringing Performance Profiles into Integrated Deve...

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22nd ieee/ACM International symposium on Code Generation and Optimization (CGO)

作者： Zhao, Qidong Chabbi, Milind Liu, Xu North Carolina State Univ Raleigh NC 27695 USA Scalable Machines Res San Francisco CA USA

ISBN: (纸本)9798350395099

Dynamic program performance analysis (also known as profiling) is well-known for its powerful capabilities of identifying performance inefficiencies in software packages. Although a large number of profiling techniques are developed in academia and industry, very few of them are widely used by software developers in their regular software developing activities. There are three major reasons. First, the profiling tools (also known as profilers) are disjoint from the coding environments such as IDEs and editors;frequently switching focus between them significantly complicates the entire cycle of software development. Second, mastering various tools to interpret their analysis results requires substantial efforts;even worse, many tools have their own design of graphical user interfaces (GUI) for data presentation, which steepens the learning curves. Third, most existing profilers expose few interfaces to support user-defined analysis, which makes the tools less customizable to fulfill diverse user demands. We develop EASYVIEW, a general solution to integrate the interpretation and visualization of various profiling results in the coding environments, which bridges software developers closer with profilers during the code development cycle. The novelty of EASYVIEW lies in its significant improvement on the usability of profilers. EASYVIEW not only provides deep insights to support intuitive analysis and optimization in a simple interface, but also enhances user experiences in using the profilers effectively and efficiently in the IDEs. Our evaluation shows that EASYVIEW is able to support various profilers for different languages and provide unique insights into performance inefficiencies in different domains. Our user studies show that EASYVIEW can largely improve the usability of profilers in software development cycles via facilitating performance debugging efforts.

关键词： Profiling Software optimization Performance measurement visualization Tools.

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Topological analysis of Ensembles of Hydrodynamic Turbulent Flows An Experimental Study 12

Topological Analysis of Ensembles of Hydrodynamic Turbulent ...

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12th ieee symposium on large-Scale data analysis and visualization (LDAV)

作者： Nauleau, Florent Vivodtzev, Fabien Bridel-Bertomeu, Thibault Beaugendre, Heloise Tierny, Julien CEA Paris France Univ Bordeaux Bordeaux France CNRS Paris France

ISBN: (数字)9781665491563

ISBN: (纸本)9781665491563

This application paper presents a comprehensive experimental evaluation of the suitability of Topological data analysis (TDA) for the quantitative comparison of turbulent flows. Specifically, our study documents the usage of the persistence diagram of the maxima of flow enstrophy (an established vorticity indicator), for the topological representation of 180 ensemble members, generated by a coarse sampling of the parameter space of five numerical solvers. We document five main hypotheses reported by domain experts, describing their expectations regarding the variability of the flows generated by the distinct solver configurations. We contribute three evaluation protocols to assess the validation of the above hypotheses by two comparison measures: (i) a standard distance used in scientific imaging (the L-2 norm) and (ii) an established topological distance between persistence diagrams (the L-2-Wasserstein metric). Extensive experiments on the input ensemble demonstrate the superiority of the topological distance (ii) to report as close to each other flows which are expected to be similar by domain experts, due to the configuration of their vortices. Overall, the insights reported by our study bring an experimental evidence of the suitability of TDA for representing and comparing turbulent flows, thereby providing to the fluid dynamics community confidence for its usage in future work. Also, our flow data and evaluation protocols provide to the TDA community an application-approved benchmark for the evaluation and design of further topological distances.

关键词： Protocols data analysis Three-dimensional displays Computational modeling Clustering methods Hydrodynamics Numerical models

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A User-Driven Sampling Model for large-Scale Geographical Point data visualization via Convolutional Neural Networks

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ieee TRANSACTIONS ON HUMAN-MACHINE SYSTEMS 2023年第5期53卷 885-894页

作者： Zhou, Zhiguang Zheng, Fengling Wen, Jin Chen, Yuanyuan Li, Xinyu Liu, Yuhua Wang, Yigang Chen, Wei Hangzhou Dianzi Univ Sch Media & Design Hangzhou 310018 Peoples R China Hangzhou Dianzi Univ Sch Comp Sci Hangzhou 310018 Peoples R China Zhejiang Univ Finance & Econ Sch Informat Management & Artificial Intelligence Hangzhou 310018 Peoples R China Zhejiang Univ Technol Zhejiang Carbon Neutral Innovat Inst Hangzhou 310023 Peoples R China Zhejiang Univ State Key Lab CAD & CG Hangzhou 310058 Peoples R China

Numerous sampling strategies have been proposed to reduce the visual clutter of large-scale geographical point data visualization, which focus on the preservation of original data features, such as randomness, spatial distribution, and associated relationship. However, user preferences and demands are not taken into account in the course of sampling, which will lead to the sampled results deviating from user requirements and impede personalized geospatial analysis in specific application scenarios. In this article, we propose a user-driven sampling model for the visual abstraction of the large-scale geographical point data based on convolutional neural networks (CNN). First, a blue noise sampling model is applied to partition the geographical space into local areas, and a set of visual interfaces are designed to present the data features of those points in the local areas, enabling users to visually select representative points according to their requirements. Then, user preferences are quantified with a CNN model based on the eigenvectors of the representative points, which are further utilized to guide the sampling courses of the other local areas. Thus, all the sampled points will retain the spatial distribution of original data points and fulfill the user preference as far as possible. In addition, we implement a visualization framework to integrate manual point selection, CNN training, automatic point sampling, and visual comparison, allowing users to easily obtain and evaluate the sampled points from the perspectives of data analysis and user requirements. Quantitative comparisons and case studies based on real-world datasets are conducted to demonstrate the effectiveness of our sampling model in the preservation of user preferences and visual exploration of large-scale geospatial point data.

关键词： Convolutional neural networks (CNN) geographical point data sampling user preference visual abstraction

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Visualizing Interaction Networks and Evidence in Biomedical Corpora 16

Visualizing Interaction Networks and Evidence in Biomedical ...

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ieee 16th Pacific visualization symposium (ieee PacificVis)

作者： Noriega-Atala, Enrique Rahat-Uz-Zaman, Md Bhat, Ruchika Jergovic, Mladen Kobourov, Stephen G. Nikolich-Zugich, Janko Univ Arizona Tucson AZ 85721 USA

ISBN: (纸本)9798350321241

The abundance of scientific articles published and indexed in publicly accessible repositories has spurred the research and development of automated information extraction systems. The output of such systems can be used to assemble large networks capturing the understanding of mechanistic pathways and their interactions as represented in the underlying body of research. We describe a system designed to help researchers search, visualize and interact with biological networks derived via information extraction tools. As input, the system takes a dataset of biological and biochemical interactions automatically generated by an information extraction system and provides an interface designed to search, visualize and interact with the data. The usage paradigm consists of identifying a starting point for a search, then using the data's network structure by incrementally exploring the immediate neighborhood of the elements displayed by the system. Our system differs from prior work as it leverages both the network structure in the data and the natural language text backing those connections: every connection displayed is traceable back to the documents and phrases in the corpus that support that specific piece of information. We also present two case studies with immunobiology researchers using the system to find previously unknown relationships between biological entities. While the evidence suggesting these relationships already existed, it was scattered across the literature, and existing specialized web databases and domain-search engines could not find it. The system is open-source, with the code publicly available on GitHub.

关键词： Information systems Search interfaces Computing methodologies visualization Information extraction

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Modular data Analytics

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ieee SOFTWARE 2024年第5期41卷 20-23页

作者： Spinellis, Diomidis Athens Univ Econ & Business Dept Management Sci & Technol Athens Greece

Relational online analytical processing (ROLAP) often involves complex queries with many intermediate steps, which can be expensive to run on large datasets. Modularizing SQL queries improves readability, testability, and incremental execution. While SQL common table expressions (CTEs) and views help in modularization, they have availability, performance, or development limitations. To address these, simple-rolap, an open-source framework, automates dependency analysis and orchestrates the execution of modular queries using GNU make. It uses a main database containing the infrequently modified base data and a secondary database for caching intermediate results. Simple-rolap ensures efficient, maintainable ROLAP queries by splitting complex queries into simple ones that create intermediate tables or report results, which can be unit-tested. It uses the make tool to manage dependencies and execution order, enhancing workflow control, testing, visualization, and troubleshooting.

关键词： Analytical Models Query Processing Online Services data analysis Open Source Modularity Intermediate Step Intermediate Results Relational database Simple Ones Complex Queries Relational database Management System SQL Queries N Makes Configuration Options

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NFTVis: Visual analysis of NFT Performance 16

NFTVis: Visual Analysis of NFT Performance

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ieee 16th Pacific visualization symposium (ieee PacificVis)

作者： Yan, Fan Wang, Xumeng Mao, Ketian Zhang, Wei Chen, Wei Zhejiang Univ State Key Lab CAD&CG Hangzhou Peoples R China Nankai Univ Coll Comp Sci Tianjin Peoples R China Zhejiang Univ Lab Art & Archaeol Image Minist Educ Hangzhou Peoples R China

ISBN: (纸本)9798350321241

A non-fungible token (NFT) is a data unit stored on the blockchain. Nowadays, more and more investors and collectors (NFT traders), who participate in transactions of NFTs, have an urgent need to assess the performance of NFTs. However, there are two challenges for NFT traders when analyzing the performance of NFT. First, the current rarity models have flaws and are sometimes not convincing. In addition, NFT performance is dependent on multiple factors, such as images (high-dimensional data), history transactions (network), and market evolution (time series). It is difficult to take comprehensive consideration and analyze NFT performance efficiently. To address these challenges, we propose NFTVis, a visual analysis system that facilitates assessing individual NFT performance. A new NFT rarity model is proposed to quantify NFTs with images. Four well-coordinated views are designed to represent the various factors affecting the performance of the NFT. Finally, we evaluate the usefulness and effectiveness of our system using two case studies and user studies.

关键词： Non-fungible token Finance visualization Rarity model

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Interactive Occlusion-Free System for Accessible Volume Exploration

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ieee ACCESS 2023年 11卷 86544-86560页

作者： Sun, Jing-Ru Josephine, Nathania Wang, Chao Wang, Ko-Chih Natl Taiwan Normal Univ Dept Comp Sci & Informat Engn Taipei 11677 Taiwan

When exploring a 3D medical dataset, data occlusion creates difficulties for users to understand the dataset in detail since many internal structures are occluded. Techniques such as transfer function design, isosurface extraction, etc., have been proposed in the past to address the occlusion problem and assist expert users in exploring volumetric data. However, these techniques may not be feasible for some non-expert users' scenarios, such as science museum visitors exploring a brain CT image data volume on a large touch screen or high school students learning the human body structure on a tablet because they have no need to conduct long-term data exploration and rigorous scientific analysis. To address the non-expert users' need for an easy-to-use data exploration tool, we propose an interactive data exploration system for non-expert users to interact with the dataset with a short learning time and simple inputs. Non-expert users can remove the obstructing material to reveal the occluded structures by simply clicking on the obstructing material. Our system also preserves the context for non-expert users to understand the dataset after the obstructing material is removed easily. Additionally, for data providers such as the engineers in a museum, our system provides a semi-automatic workflow to set up a dataset into our system for non-expert users. In this work, we conduct two user studies to evaluate our system's usability.

关键词： ~3D data visualization and exploration human-computer interaction occlusion management

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StrategyAtlas: Strategy analysis for Machine Learning Interpretability

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ieee TRANSACTIONS ON visualization AND COMPUTER GRAPHICS 2023年第6期29卷 2996-3008页

作者： Collaris, Dennis van Wijk, Jarke J. Eindhoven Univ Technol Dept Math & Comp Sci NL-5612 AZ Eindhoven Netherlands

Businesses in high-risk environments have been reluctant to adopt modern machine learning approaches due to their complex and uninterpretable nature. Most current solutions provide local, instance-level explanations, but this is insufficient for understanding the model as a whole. In this work, we show that strategy clusters (i.e., groups of data instances that are treated distinctly by the model) can be used to understand the global behavior of a complex ML model. To support effective exploration and understanding of these clusters, we introduce StrategyAtlas, a system designed to analyze and explain model strategies. Furthermore, it supports multiple ways to utilize these strategies for simplifying and improving the reference model. In collaboration with a large insurance company, we present a use case in automatic insurance acceptance, and show how professional data scientists were enabled to understand a complex model and improve the production model based on these insights.

关键词： data models Analytical models Machine learning Predictive models Computational modeling Insurance data visualization Visual analytics machine learning explainable AI

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CLeVer: Continual Learning Visualizer for Detecting Task Transition Failure 17

CLeVer: Continual Learning Visualizer for Detecting Task Tra...

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17th Pacific visualization Conference (PacificVis)

作者： Chang, Minsuk Kim, Donghun Jeon, Hyeon Jung, Seokweon Seo, Jinwook Seoul Natl Univ Seoul South Korea

ISBN: (纸本)9798350393811;9798350393804

We introduce CLeVer, a novel visualization system designed to analyze and enhance the performance of continual learning models by detecting task transition failures. Based on the literature review, we discovered and classified three primary causes of task transition failure in classification tasks: class/position inconsistency, biased/noisy samples, and diverse class scopes. These problems are critical in terms of model performance but were not tackled in prior research on continual learning. CLeVer is designed to address these challenges as an integrated system for model experiments and visual analysis. Firstly, users can easily configure the tasks for continual learning through a single JSON file. Then, our system automatically simulates the continual learning process in a relatively short time while generating data for visualization. Finally, the transition visualizer provides an effective visual representation of the task transition process where users can easily detect the task transition failures in continual learning. Our interview with machine learning experts and a case study with three participants demonstrate CLeVer's utility in detecting and addressing such failures. We also discuss the system's potential applicability and adaptability for various computer vision tasks while suggesting our future work.

关键词： Human-centered computing [visualization toolkits] Computing methodologies [Lifelong machine learning]

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