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visualization Tools for Machine Learning Pipelines: A Review

Visualization Tools for Machine Learning Pipelines: A Review

引用

Pacific (formerly Asia-Pacific APVIS) visualization Symposium

作者： Valentina Golendukhina Michael Felderer Lisa Sonnleithner University of Innsbruck Innsbruck Austria German Aerospace Center (DLR) Institute for Software Technology University of Cologne Cologne Germany CDL VaSiCS LIT CPS Lab Johannes Kepler University Linz Linz Austria

ISBN: (数字)9798331505813

ISBN: (纸本)9798331505820

The increasing complexity and adoption of machine learning (ML) pipelines has led to a rising demand for effective visualization tools. This paper presents a comprehensive review of existing tools for visualizing data flow in machine learning (ML) pipelines. We highlight the tools’ purposes, integration methods, and visualization techniques. We collected and analyzed 22 open-source tools and concepts, analyzing their features and classifying them based on their primary purpose. Our analysis revealed five main purposes of visualization tools: exploration, explanation, visual development, comparison, monitoring. We provide an analysis of their integration methods, from standalone visual interfaces to code-level libraries, as well as a review of various visualization techniques, including Directed Acyclic Graphs (DAGs), pipeline matrices, and annotated visualizations. Our findings highlight the importance of visualization in enhancing the interpretability and efficiency of ML workflows. Moreover, the paper provides key limitations and challenges in current visualization methods to promote future research directions enhancing the usability and functionality of ML pipeline visualization tools.

关键词： visualization Directed acyclic graph Reviews Pipelines data visualization Machine learning visual systems Complexity theory Usability Monitoring

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exploration and analysis of Image-based Simulation Ensembles

Exploration and Analysis of Image-based Simulation Ensembles

引用

24th Eurographics conference on visualization, EuroVis 2022

作者： Dahshan, M. Turton, T.L. Polys, N. University of North Florida United States Los Alamos National Laboratory United States Virgina Tech United States

ISBN: (纸本)9783038681854

Scientists run simulation ensembles to study the behavior of a phenomenon using varying initial conditions or input parameters. However, the I/O bottlenecks hinder performing large-scale multidimensional simulations. In situ visualization approaches address the variability of I/O performance by processing output data during simulation time and saving predetermined visualizations in image databases. This poster proposes a visual analytics approach to exploring and analyzing image-based simulation ensembles, taking advantage of semantic interaction, feature extraction, and deep learning techniques. Our approach uses deep learning and local feature techniques to learn image features and pass them along with the input parameters to the visualization pipeline for in-depth exploration and analysis of parameter and ensemble spaces simultaneously. © 2022 The Author(s) Eurographics Proceedings © 2022 The Eurographics Association.

关键词： visualization

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The Role of visualization in Genomics data analysis Workflows: The Interviews

The Role of Visualization in Genomics Data Analysis Workflow...

引用

International conference on visualisation, VIS

作者： Sehi L’Yi Qianwen Wang Nils Gehlenborg Harvard Medical School

The diversity of genome-mapped data and analysis tasks makes it challenging for a single visualization tool to fulfill all visualization needs. To design a visualization tool that supports various genomics workflows of users, it is critical to first gain insights into the diverse workflows and the limitations of existing genomics tools for supporting them. In this paper, we conducted semi-structured interviews (N=9) to understand the role of visualization in genomics data analysis workflows. Our main goals were to identify various genomics workflows, from data analysis to visual exploration and presentation, and to observe challenges that genomics analysts encounter in these workflows when using existing tools. Through the interviews, we found several unique characteristics of genomics workflows, such as the use of multiple visualization tools and many repetitive tasks, which can significantly affect the overall performance. Based on our findings, we discuss implications for designing effective visualization authoring tools that tightly support genomics workflows, such as supporting automation and reproducibility.

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Knowledge-based Graph exploration analysis on Customer’s Preference of Insurance Services

Knowledge-based Graph Exploration Analysis on Customer’s Pr...

引用

International ECTI Northern Section conference on Electrical, Electronics, Computer and Telecommunications Engineering (ECTI-NCON)

作者： Yingfan Chen Chalermpon Kongjit College of Arts Media and Technology Chiang Mai University Chiang Mai Thailand

ISBN: (数字)9798331543273

ISBN: (纸本)9798331543280

With the increasing number of Chinese students in Thailand, the demand for insurance services from these students has gradually shown diversity and personalized characteristics. However, the current research on the insurance demand of overseas students is relatively insufficient. Based on text analysis and knowledge graph technology, this study focuses on the needs and preferences of Chinese students in Thailand in terms of insurance choices, and presents the research results in a visual form. The researchers collected chat history content of some target groups, as well as public discussion data on social media platforms and forums frequently used by Chinese students in Thailand. The researchers used the text analysis method to extract high-frequency keywords related to international student insurance, and carried out sentiment analysis to obtain the main concerns of current international students in insurance selection. Then, a knowledge graph centered on insurance demand is constructed to visualize key nodes and relationships such as insurance categories, budget preferences, and demand characteristics.

关键词： visualization Sentiment analysis Text analysis Social networking (online) Knowledge based systems Insurance Knowledge graphs History

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USING REORDERABLE HEATMAPS AS A SUPPORT FOR data ASSIMILATION IN PETROLEUM RESERVOIR MANAGEMENT AND DEVELOPMENT PROCESSES 16

USING REORDERABLE HEATMAPS AS A SUPPORT FOR DATA ASSIMILATIO...

引用

16th International conference on Computer Graphics, visualization, Computer Vision and Image Processing, CGVCVIP 2022, 8th International conference on Connected Smart Cities, CSC 2022, 7th International conference on Big data Analytics, data Mining and Computational Intelligence, BigDaCI 2022, and 11th International conference on Theory and Practice in Modern Computing, TPMC 2022 - Held at the 16th Multi conference on Computer Science and Information Systems, MCCSIS 2022

作者： Turato, Gabriel Paulo de Oliveira, João Gabriel Nogueira Duarte Santos, Antonio Alberto S. Schiozer, Denis José da Silva, Celmar Guimarães School of Technology University of Campinas São Paulo Limeira Brazil Center for Petroleum Studies University of Campinas São Paulo Campinas Brazil School of Mechanical Engineering University of Campinas São Paulo Campinas Brazil

ISBN: (纸本)9789898704429

Petroleum exploration activities demand the creation of lots of models to represent the uncertainties of a given reservoir, aiming to guide decision-making regarding exploration strategies. As part of these activities, a data assimilation process may refine the model set and discard models that do not honor the history data of the reservoir, given an error tolerance level. Reservoir engineers may use visualizations to analyze the partial results of iterative data assimilation processes and make decisions such as accepting the models selected in an iteration or refining the model set. Among a set of available visualizations for this process, we did not find in the literature the use of interactive heatmaps based on reorderable matrices. In this paper, we propose to add this kind of visualization to the analysis toolset for data assimilation, aiming to help engineers to better overview data assimilation datasets and how their models honor the history data of a reservoir. The proposed visual mappings use a set of heatmaps that enable the engineers to analyze a misfit measure (NQDS) for a given iteration, regarding models, wells, and their dynamic attributes. Users may reorder and filter these heatmaps according to their needs. We implemented our proposal in a web-based prototype that was submitted to a preliminary evaluation with real users in a synthetic scenario. This evaluation revealed positive opinions about the potential of the tool, which may be used to complement other approaches, and provided some opportunities for improvement. © MCCSIS *** rights reserved.

关键词： visualization

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exploration on 3D Geological Modeling Technology Based on BIM Secondary Development - Taking Liangshuijing Tunnel as an Example 22

Exploration on 3D Geological Modeling Technology Based on BI...

引用

2022 International conference on Computational Infrastructure and Urban Planning, CIUP 2022

作者： Wang, Yan Jun Li, Rui Yang, Zhu Tan, Zhuoyue Xu, Zhong College of Environment and Civil Engineering Chengdu University of Technology China

ISBN: (纸本)9781450396363

Although the existing 3D geological software has great advantages in spatial geographical analysis, it still has some weaknesses such as complex modeling program. On this basis, 3D geological modeling technology was explored herein. Firstly, the representation method of 3D geological model and the data processing method of geological modeling are discussed;secondly, aiming at the problems of low modeling accuracy and little information data, Delaunay Triangulation Algorithm is applied to establish the correlation between 3D geological model and data;the theoretical basis and application skills for supplementing the measurement data by Kriging Interpolation Method are explored;finally, by using Dynamo visual programming plug-in, the topographic curved surface, unstratified 3D geological entity and the stratified 3D geological entity have been generated successively, the 3D geological modeling platform based on Revit has been improved, and the 3D geological model of the stratified structure has been quickly created and has been applied to Liangshuijing Tunnel Engineering Project. © 2022 ACM.

关键词： Interpolation

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data prophecy: Exploring the efects of belief elicitation in visual analytics

Data prophecy: Exploring the efects of belief elicitation in...

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2021 CHI conference on Human Factors in Computing Systems: Making Waves, Combining Strengths, CHI 2021

作者： Koonchanok, Ratanond Baser, Parul Sikharam, Abhinav Indiana University-Purdue University Indianapolis United States

ISBN: (纸本)9781450380966

Interactive visualizations are widely used in exploratory data analysis, but existing systems provide limited support for confrmatory analysis. We introduce PredictMe, a tool for belief-driven visual analysis, enabling users to draw and test their beliefs against data, as an alternative to data-driven exploration. PredictMe combines belief elicitation with traditional visualization interactions to support mixed analysis styles. In a comparative study, we investigated how these afordances impact participants' cognition. Results show that PredictMe prompts participants to incorporate their working knowledge more frequently in queries. Participants were more likely to attend to discrepancies between their mental models and the data. However, those same participants were also less likely to engage in interactions associated with exploration, and ultimately inspected fewer visualizations and made fewer discoveries. The results suggest that belief elicitation may moderate exploratory behaviors, instead nudging users to be more deliberate in their analysis. We discuss the implications for visualization design. © 2021 ACM.

关键词： visualization

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Logarithmic Lenses: Exploring Log RGB data for Image Classification

Logarithmic Lenses: Exploring Log RGB Data for Image Classif...

引用

conference on Computer Vision and Pattern Recognition (CVPR)

作者： Bruce A. Maxwell Sumegha Singhania Avnish Patel Rahul Kumar Heather Fryling Sihan Li Haonan Sun Ping He Zewen Li Northeastern University Boston USA

ISBN: (数字)9798350353006

ISBN: (纸本)9798350353013

The design of deep network architectures and training methods in computer vision has been well-explored. However, in almost all cases the images have been used as provided, with little exploration of pre-processing steps beyond normalization and data augmentation. Virtually all images posted on the web or captured by devices are processed for viewing by humans. Is the pipeline used for humans also best for use by computers and deep networks? The human visual system uses logarithmic sensors; differences and sums correspond to ratios and products. Features in log space will be invariant to intensity changes and robust to color balance changes. Log RGB space also reveals structure that is corrupted by typical pre-processing. We explore using linear and log RGB data for training standard backbone architectures on an image classification task using data derived directly from RAW images to guarantee its integrity. We found that networks trained on log RGB data exhibit improved performance on an unmodified test set and invariance to intensity and color balance modifications without additional training or data augmentation. Furthermore, we found that the gains from using high quality log data could also be partially or fully realized from data in 8-bit sRGB-JPG format by inverting the sRGB transform and taking the log. These results imply existing databases may benefit from this type of pre-processing. While working with log data, we found it was critical to retain the integrity of the log relationships and that networks using log data train best with meta-parameters different than those used for sRGB or linear data. Finally, we introduce a new 10-category 10k RAW image data set (RAW10) for image classification and other purposes to enable further the exploration of log RGB as an input format for deep networks in computer vision.

关键词： Training Computer vision Image color analysis Pipelines Transforms visual systems Sensor systems

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An Improvement on exploration Step of Whale Optimization Algorithm with Levy Distribution for Classification Problems

An Improvement on Exploration Step of Whale Optimization Alg...

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Big data Analytics and Practices (IBDAP), International conference on

作者： Sakkayaphop Pravesjit Krittika Kantawong Natdanai Kamkhad Saksit Sabaiporn Jantawan Monchanuan Duangjai Jitkongchuen Arit Thammano Panchit Longpradit School of Information and Communication Technology University of Phayao Phayao Thailand Big Data Instutite Bangkok Thailand Facluty of Information Technology King Mongkut's Institute of Technology Ladkrabang Ladkrabang Thailand Faculty of Social Sciences and Humanities Mahidol University Nakhon Pathom Thailand

ISBN: (数字)9798350391749

ISBN: (纸本)9798350391756

The proposed system represents an enhanced movement in search of food of Whale Optimization Algorithm (WOA), based on Levy distribution for image classification of grape leaf disease. In the preprocessing, the proposed system uses convolution kernels to transform images into input data within the range of (0,1). Thereafter, the Levy distribution is incorporated into the WOA model as an exploration search mechanism. A grape leaf dataset from the Plant Village project (***), consisting of 4062 labeled images with dimensions of 256 by 256 pixels and divided into four classes -healthy, Black Rot, Black Measles, and Isariopsis leaf spot -is used to evaluate the performance of the proposed system. Experimental results show that the proposed system is better than visual Geometry Group (VGG16), Gray Level Co-occurrence Matrix (GLCM) with SVM, Low contrast haze reduction-neighborhood component analysis with SVM, and whale optimization algorithm (WOA).

关键词： Support vector machines Convolution Pipelines data preprocessing Measles Transforms Whale optimization algorithms

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Enhancing Customer Experience in Retail for Adaptive Store Layouts with Reinforcement Learning

Enhancing Customer Experience in Retail for Adaptive Store L...

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visual Analytics and data visualization (ICVADV), International conference on

作者： P. Suresh T Sivakumar N. Revathy M. Pandi Department of Computer Science and Engineering Saveetha School of Engineering SIMATS Chennai Tamil Nadu India Department of Computer Science and Engineering Sri Eshwar College of Engineering Coimbatore Tamil Nadu India Department of Master of Computer Applications Hindustan College of Arts and Science Coimbatore Tamil Nadu India Department of Information Technology SRM Madurai College for Engineering and Technology Sivagangai Tamil Nadu India

ISBN: (数字)9798331521394

ISBN: (纸本)9798331521400

In the competitive retail industry, improving customer experience is essential for increasing sales and cultivating brand loyalty. A crucial determinant of consumer happiness is the shop layout, which impacts customers' navigation, exploration, and interaction with merchandise. Traditional store layouts are often inflexible and do not adjust to evolving customer behaviour, hence diminishing their efficacy in enhancing the shopping experience. This paper examines the use of Reinforcement Learning (RL) to create adaptable shop layouts that adjust based on real-time consumer interactions. Through the analysis of customer behaviour and feedback, RL algorithms may dynamically modify the layout of merchandise, aisles, and promotional areas, therefore optimizing the retail environment for both consumers and merchants. The RL algorithm analyses historical data, progressively optimizing store layouts to correspond with consumer preferences, so improving the entire shopping experience. It provides substantial advantages, such as enhanced consumer involvement, more operational efficiency, and elevated revenue designs. It examines the potential of RL in revolutionizing retail settings in optimizing adaptable shop layouts and its effects on consumer happiness and corporate performance.

关键词： Technological innovation IEEE merchandise Heuristic algorithms visual analytics Layout Reinforcement learning Real-time systems Internet of Things Artificial intelligence Optimization

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