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10th International Workshop on Big data Management and Service, BDMS 2024, 9th International Workshop on Big data Quality Management, BDQM 2024, DASFAA 2024 Workshop on Emerging Results in data Science and Engineering, ERDSE 2024 and 8th International Workshop on Graph data Management and analysis, GDMA 2024 held in conjunction with 29th International conference on database Systems for Advanced Applications, DASFAA 2024

10th International Workshop on Big Data Management and Servi...

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10th International Workshop on Big data Management and Service, BDMS 2024, 9th International Workshop on Big data Quality Management, BDQM 2024, DASFAA 2024 Workshop on Emerging Results in data Science and Engineering, ERDSE 2024 and 8th International Workshop on Graph data Management and analysis, GDMA 2024 held in conjunction with 29th International conference on database Systems for Advanced Applications, DASFAA 2024

ISBN: (纸本)9789819609130

The proceedings contain 26 papers. The special focus in this conference is on Big data Management and Service. The topics include: FAITH: A Fast, Accurate, and Lightweight database-Agnostic Learned Cost Model;fast Approximate Temporal Butterfly Counting on Bipartite Graphs via Edge Sampling;Financial-ICS: Identifying Peer Firms via LongBERT from 10K Reports;establishing a Decentralized Diamond Quality Management System: Advancing Towards Global Standardization;co-estimation of data Types and Their Positional Distribution;Enhancing Load Forecasting with VAE-GAN-Based data Cleaning for Electric Vehicle Charging Loads;audio-Guided visual Knowledge Representation;boundary Point Detection Combining Gravity and Outlier Detection Methods;A Meta-learning Approach for Category-Aware Sequential Recommendation on POIs;automatic Post-editing of Speech Recognition System Output Using Large Language Models;comparative analysis with Multiple Large-Scale Language Models for Automatic Generation of Funny Dialogues;effectiveness of the Programmed visual Contents Comparison Method for Two Phase Collaborative Learning in Computer Programming Education: A Case Study;generating Achievement Relationship Graph Between Actions for Alternative Solution Recommendation;generating News Headline Containing Specific Person Name;Investigating Evidence in Sentence Similarity Using MASK in BERT;acceleration of Synopsis Construction for Bounded Approximate Query Processing;Query Expansion in Food Review Search with Synonymous Phrase Generation by LLM;Question Answer Summary Generation from Unstructured Texts by Using LLMs;Real Estate Information exploration in VR with LoD Control by Physical Distance;voices of Asynchronous Learning Students: Revealing Learning Characteristics Through Vocabulary analysis of Notes Tagged in Videos;review Search Interface Based on Search Result Summarization Using Large Language Model;yes-No Flowchart Generation for Interactive exploration of Personalized Health Improve

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Exploratory data analysis of Population Level Smartphone-Sensed data 16th

Exploratory Data Analysis of Population Level Smartphone-Sen...

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16th International Joint conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP) / 16th International conference on Computer Vision Theory and Applications (VISAPP)

作者： Mansoor, Hamid Gerych, Walter Alajaji, Abdulaziz Buquicchio, Luke Chandrasekaran, Kavin Agu, Emmanuel Rundensteiner, Elke Worcester Polytech Inst Worcester MA 01609 USA

ISBN: (纸本)9783031254765;9783031254772

Mobile health involves gathering smartphone-sensor data passively from user's phones, as they live their lives 'In-the-wild", periodically annotating data with health labels. Such data is used by machine learning models to predict health. Purely Computational approaches generally do not support interpretability of the results produced from such models. In addition, the interpretability of such results may become difficult with larger study cohorts which make population-level insights desirable. We propose Population Level exploration and analysis of smartphone DEtected Symptoms (PLEADES), an interactive visual analytics framework to present smartphone-sensed data. Our approach uses clustering and dimension reduction to discover similar days based on objective smartphone sensor data, across participants for population level analyses. PLEADES enables analysts to apply various clustering and projection algorithms to several smartphonesensed datasets. PLEADES overlays human-labelled symptom and contextual information from in-the-wild collected smartphone-sensed data, to empower the analyst to interpret findings. Such views enable the contextualization of the symptoms that can manifest in smartphone sensor data. We used PLEADES to visualize two real world in-the-wild collected datasets with objective sensor data and human-provided health labels. We validate our approach through evaluations with data visualization and human context recognition experts.

关键词： Interactive visual analytics In-the-wild smartphone-sensed data Exploratory data analysis

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SHEVA: A visual Analytics System for Statistical Hypothesis exploration 49th

SHEVA: A Visual Analytics System for Statistical Hypothesis ...

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49th International conference on Very Large data Bases, VLDB 2023

作者： de Almeida, Vicente Nejar Ribeiro, Eduardo Bouarour, Nassim Comba, João Luiz Dihl Amer-Yahia, Sihem Instituto de Informática UFRGS Porto Alegre Brazil Universidade Federal do Tocantins Palmas Brazil Univ. Grenoble Alpes Grenoble France CNRS Univ. Grenoble Alpes Grenoble France

We demonstrate SHEVA, a System for Hypothesis exploration with visual Analytics. SHEVA adopts an Exploratory data analysis (EDA) approach to discovering statistically-sound insights from large datasets. The system addresses three longstanding challenges in Multiple Hypothesis Testing: (i) the likelihood of rejecting the null hypothesis by chance, (ii) the pitfall of not being representative of the input data, and (iii) the ability to navigate among many data regions while preserving the user’s train of thought. To address (i) & (ii), SHEVA implements significance adjustment methods that account for data-informed properties such as coverage and novelty. To address (iii), SHEVA proposes to guide users by recommending one-sample and two-sample hypotheses in a stepwise fashion following a data hierarchy. Users may choose from a collection of pre-trained hypothesis exploration policies and let SHEVA guide them through the most significant hypotheses in the data, or intervene to override suggested hypotheses. Furthermore, SHEVA relies on data-to-visual element mappings to convey hypothesis testing results in an interpretable fashion, and allows hypothesis pipelines to be stored and retrieved later to be tested on new datasets. © 2023, VLDB Endowment. All rights reserved.

关键词： Large dataset

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AirLens: Multi-Level visual exploration of Air Quality Evolution in Urban Agglomerations

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COMPUTER GRAPHICS FORUM 2022年第3期41卷 223-234页

作者： Qu, Dezhan Lv, Cheng Lin, Yiming Zhang, Huijie Wang, Rong Northeast Normal Univ Sch Informat Sci & Technol Changchun Peoples R China Jilin Univ Key Lab Intelligent Informat Proc Changchun Peoples R China

The precise prevention and control of air pollution is a great challenge faced by environmental experts in recent years. Understanding the air quality evolution in the urban agglomeration is important for coordinated control of air pollution. However, the complex pollutant interactions between different cities lead to the collaborative evolution of air quality. The existing statistical and machine learning methods cannot well support the comprehensive analysis of the dynamic air quality evolution. In this study, we propose AirLens, an interactive visual analytics system that can help domain experts explore and understand the air quality evolution in the urban agglomeration from multiple levels and multiple aspects. To facilitate the cognition of the complex multivariate spatiotemporal data, we first propose a multi-run clustering strategy with a novel glyph design for summarizing and understanding the typical pollutant patterns effectively. On this basis, the system supports the multi-level exploration of air quality evolution, namely, the overall level, stage level and detail level. Frequent pattern mining, city community extraction and useful filters are integrated into the system for discovering significant information comprehensively. The case study and positive feedback from domain experts demonstrate the effectiveness and usability of AirLens.

关键词： CCS Concepts • Human-centered computing → visual analytics Geographic visualization Information visualization

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The Concept of a New Neural Map for Clustering, data visualization and Prediction with Probability Distribution Approximation 19th

The Concept of a New Neural Map for Clustering, Data Visuali...

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19th European, Mediterranean, and Middle Eastern conference (EMCIS)

作者： Morajda, Janusz Krakow Univ Econ Krakow Poland

ISBN: (纸本)9783031306938;9783031306945

The paper submits a proposition of a new data analysis tool (named PDCM - Probability Distribution generating and Clustering Maps) constructed in the form of an extended neural map and dedicated to a variety of tasks, such as specific clustering, visualization, prediction (together with its possible visual analysis and justification) and generation of approximated Bayesian a posteriori probability density distribution for dependent variable. Basic theoretical aspects concerning the structure and training process of the proposed model have been presented. Also, research involving application of the PDCM method for real estate market data (Boston dataset) has been shown together with promising research results and conclusions.

关键词： neural maps neural networks SOM Self-Organizing Prediction Maps data analysis data exploration clustering prediction data visualization probability distribution management real estate

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Space-Time Urban Explorer: A visual Tool for Exploring Spatiotemporal Crime and Patrolling data 37

Space-Time Urban Explorer: A Visual Tool for Exploring Spati...

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37th SIBGRAPI conference on Graphics, Patterns and Images (SIBGRAPI)

作者： Santos, Tiago Paulino Souza, Joao Matheus Siqueira Vieira, Thales Nonato, Luis Gustavo Fed Univ Alagoas UFAL Inst Comp Maceio Alagoas Brazil Univ Sao Paulo ICMC USP Inst Math & Comp Sci Sao Carlos Brazil

ISBN: (纸本)9798350376043;9798350376036

Spatiotemporal urban data gathered by public security departments holds immense potential for in-depth analysis, enhancing decision-making in areas such as crime prevention and patrolling strategies. However, extracting meaningful patterns from vast and complex spatiotemporal datasets presents a considerable challenge in the field of big data analytics. We introduce a novel visualization-assisted tool tailored for handling massive spatiotemporal urban datasets, with a specific focus on public security data. At the core of this tool are spatial graph vertex ordering algorithms that perform dimensionality reduction on the vertices' locations. To make this tool practical for handling massive spatiotemporal datasets, we present efficient preprocessing techniques. These techniques are carefully crafted to distill and represent urban spatiotemporal datasets, ensuring efficient data exploration. The effectiveness of the proposed solution is validated through case studies using real datasets from the Military Police of the State of Alagoas - Brazil. We demonstrate the effectiveness of the proposed solutions, showcasing the versatility of the visual tool in accomplishing various relevant analytical tasks.

关键词： Spatio-temporal data

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ICCECE 2024 - International conference on Computer, Electrical and Communication Engineering

ICCECE 2024 - International Conference on Computer, Electric...

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2024 International conference on Computer, Electrical and Communication Engineering, ICCECE 2024

ISBN: (纸本)9798350386479

The proceedings contain 35 papers. The topics discussed include: automated texture feature based bone tumor segmentation and image analysis using supervised machine learning;analysis of barrel electroplating line with process mining and Petri-Net model;an effective EEG-based image classification methodology under low data and resource requirements using machine learning;identification of hub genes in interstitial lung disease and their association with lung cancer: an in-silico analysis;impact of basic communication modules and techniques in humanitarian crisis-a brief survey;a comparative analysis between deep learning and machine learning algorithms based on user review sentiment analysis from various OTT applications;FPGA implementation of serial peripheral interface transceiver module;and EmoEyes: a machine learning exploration of emotional states through eye movement tracking in visual content.

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visual analysis of Tobacco Leaf Recognition Research Based on CiteSpace 14

Visual Analysis of Tobacco Leaf Recognition Research Based o...

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14th International conference on Software Technology and Engineering, ICSTE 2024

作者： Dai, Yanting Zhou, Hua Southwest Forestry University College of Big Data and Intelligent Engineering Kunming China

ISBN: (纸本)9798350378955

This study utilizes CiteSpace software to conduct a visual analysis of the literature in the field of tobacco leaf recognition technology in recent years, aiming to reveal the research hotspots, development trends, and contributions of key institutions and scholars in this domain. By analyzing the changes in the number of publications, keyword co-occurrence networks, author collaboration patterns, and keyword burst situations, this paper demonstrates the in-depth exploration and innovation in both basic research and practical applications of tobacco leaf recognition technology. Firstly, through the analysis of the annual publication volume, we found that research on tobacco leaf recognition has shown a significant growth trend over the past decade, indicating the increasing importance of this field in both academia and industrial applications. Secondly, the analysis of research institutions and authors reveals the dominant positions of institutions such as the Chinese Academy of Agricultural Sciences, the Chinese Academy of Sciences, and N anjing Agricultural University, as well as the outstanding contributions of scholars like Wang Ying, Wang Lu, and Guo Yongfeng in terms of publication volume and collaboration relationships. Furthermore, the keyword co-occurrence network and cluster analysis show that the research focus of tobacco leaf recognition technology covers multiple keyword groups, including gene expression, transcription factors, plant resistance, and biosynthesis, reflecting researchers' multifaceted efforts in theoretical exploration and practical applications. Finally, the keyword burst analysis reveals the rise and fall of keywords in different periods. For instance, the period from 2014 to 2017 was mainly concentrated on 'gene expression' and 'transgenic tobacco,' while from 2018 to 2020, emerging research hotspots such as 'functional analysis' and 'cell death' became prominent. This study provides profound insights and significant references for the

关键词： Agricultural Intelligence Deep Learning Tobacco Leaf Recognition

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SimGlim: Simplifying glimpse based active visual reconstruction 23

SimGlim: Simplifying glimpse based active visual reconstruct...

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23rd IEEE/CVF Winter conference on Applications of Computer Vision (WACV)

作者： Jha, Abhishek Seifi, Soroush Tuytelaars, Tinne Katholieke Univ Leuven ESAT PSI Leuven Belgium

ISBN: (纸本)9781665493468

In active visual exploration, an agent with a limited field of view needs to sample the most informative local observations of an environment in order to model the global context. Current works train this selection strategy by defining a complex architecture built upon features learned through convolutional encoders. In this paper, we first discuss why vision transformers are better suited than CNNs for such an agent. Next, we propose a simple transformer-based active visual sampling model, called "SimGlim", which utilises transformer's inherent self-attention architecture to sequentially predict the best next location based on the current observable environment. We show the efficacy of our proposed method on the task of image reconstruction in the partial observable setting and compare our model against existing state-of-the-art active visual reconstruction methods. Finally, we provide ablations for the parameters of our design choice to understand their importance in the overall architecture.

关键词： visualization Computer vision Reconstruction algorithms Predictive models Transformers data models Task analysis

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The TMMTA Model: Momentum Tracking and analysis of Tennis Matches 24

The TMMTA Model: Momentum Tracking and Analysis of Tennis Ma...

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2024 International conference on Smart City and Information System, ICSCIS 2024

作者： Chen, Feiyang Guo, Yang Wang, Tianqi School of Management Northeastern University at Qinhuangdao Qinhuangdao Hebei066099 China School of Mathematics and Statistics Northeastern University at Qinhuangdao Qinhuangdao Hebei066099 China

ISBN: (纸本)9798400710155

In sports, the generation and change of momentum are always accompanied, and tennis is no exception. It is of great significance for winning to analyze how the momentum generates, changes and its impact on the score in the tennis match. Most of the existing studies theoretically study the nature and influence of momentum in tennis matches or focus on the point results and whether winning or losing the match. However, the analysis of momentum changes in data series and the exploration of the relationship and influence degree of specific behavior and data features with momentum are missing. To this end, this study constructs a Tennis Matches Momentum Tracking and analysis Model (TMMTA) based on the time-series analysis to analyze the data of five games Carlos Alcaraz participated in and the model is applied to multiple players and multiple matches to verify the prediction effect and conclusion of the model. Experiments show that the visual analysis results are very good, which can effectively analyze the momentum changes and specific performance in players' matches and provide insight for player training and match strategy. The three game behaviors, double errors, untouchable winning shots and unforced errors, have the most weight on scoring and are closely related to strategic momentum. Strategic momentum has a higher impact on matches score than mental momentum, and the combination of the two is the best prediction. This has important implications for coaches to value and utilize the role of Momentum. © 2024 ACM.

关键词： Tennis

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