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25th IEEE International conference on Mobile data Management (IEEE MDM)

作者： Graser, Anita AIT Austrian Inst Technol Vienna Austria Paris Lodron Univ Salzburg Salzburg Austria

ISBN: (纸本)9798350374551;9798350374568

This dissertation combines both novel methodological work as well as high-quality scientific software development for mobility data science. It presents novel methods enabling movement data exploration that scale to massive datasets, describes the development of a novel open source scientific Python library for EDA of movement data (MovingPandas), and proposes the first structured EDA protocol for movement data.

关键词： exploratory analysis trajectory data mobility data science spatial data science

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A Conceptual Framework for Immersive Behavioral data exploration Assisted by a Deep Learned Representation of data 7

A Conceptual Framework for Immersive Behavioral Data Explora...

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7th IEEE International conference on Artificial Intelligence and eXtended and Virtual Reality, AIxVR 2025

作者： Duvivier, Victor Da Silva, Matthieu Perreira Prié, Yannick Nantes Université École Centrale Nantes Cnrs LS2N Umr 6004 Nantes44300 France

ISBN: (纸本)9798331521578

By allowing to conduct experiments involving eco-logically valid tasks within controlled environments, Virtual Real-ity (VR) offers novel opportunities for studying human behavior. Several modalities can be leveraged, including event logs, motion trajectories, eye-tracking data, or physiological signals. However, analyzing such multimodal data presents considerable challenges due to their inherent complexity, the varied structures they imply, as well as the necessity to use exploratory approaches. There is therefore a need to design visual analytics tools for the exploration of immersive behavioral data without prior knowledge. Our idea is to integrate deep learned computational models -which leverage advanced techniques to extract valuable high-level features from unlabeled data- into exploratory visual Analytics tools. We introduce a conceptual framework for integrating deep learning models into the visual Analytics process of immersive behavioral data analysis, also focusing on the services that such systems should provide to the analysts. © 2025 IEEE.

关键词： visual analytics

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Towards a visual Perception-Based analysis of Clustering Quality Metrics

Towards a Visual Perception-Based Analysis of Clustering Qua...

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2024 conference on visualization in data Science

作者： Blasilli, Graziano Kerrigan, Daniel Bertini, Enrico Santucci, Giuseppe Sapienza Univ Roma Rome Italy Northeastern Univ Boston MA USA

ISBN: (纸本)9798331528423;9798331528430

Clustering is an essential technique across various domains, such as data science, machine learning, and explainable artificial intelligence. Information visualization and visual analytics techniques have been proven to effectively support human involvement in the visual exploration of clustered data to enhance the understanding and refinement of cluster assignments. To support the human involvement, several perceptual studies and visual quality metrics have already been proposed. However, the visual perception of clustering quality metrics, also known as Cluster Validity Indexes (CVIs), still remains to be further explored. This paper presents the first attempt of a deep and exhaustive evaluation of the perceptive aspects of clustering quality metrics, focusing on the Davies-Bouldin Index, Dunn Index, Calinski-Harabasz Index, and Silhouette Score. Our research is centered around two main objectives: a) assessing the human perception of common CVIs in 2D scatterplots and b) exploring the potential of Large Multimodal Models, in particular GPT-4o, to emulate the assessed human perception. To this end, we conducted two systematic data studies and a user study covering a broad collection of datasets. By discussing the obtained results, highlighting limitations, and areas for further exploration, this paper aims to propose a foundation for future research activities.

关键词： Clustering Clustering Quality Metrics Clustering Validity Indexes visual Perception Large Multimodal Models LMM

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Analyzing the Shifts in Users data Focus in Exploratory visual analysis 25

Analyzing the Shifts in Users Data Focus in Exploratory Visu...

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30th International conference on Intelligent User Interfaces, IUI 2025

作者： Saha, Sanad Aryal, Nischal Battle, Leilani Termehchy, Arash Computer Science Oregon State University CorvallisOR United States Paul G. Allen School of Computer Science and Engineering University of Washington SeattleWA United States

ISBN: (纸本)9798400713064

Users often begin exploratory visual analysis (EVA) without clear analysis goals but iteratively refine them as they learn more about their data. As an essential step in data science, researchers want to aid EVA by developing responsive and personalized visualization tools. For this, accurate models of users' exploration behavior are becoming increasingly vital. However, many computational models assume that the human exploration behavior isstatic, which goes against thedynamic nature of EVA. In this benchmark study, we investigate how users dynamically shift their data focus in EVA and seek to find the best online learning methods for modeling users' data focus shifts. Through empirical analyses, we find reinforcement learning algorithms are better in this regard than existing approaches from visualization research. Furthermore, we discuss our findings and their impact on the future of user modeling for visualization system design. © 2025 Copyright held by the owner/author(s).

关键词： Human-centered Computing

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A privacy-aware visual query approach for location-based data

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COMPUTERS & GRAPHICS-UK 2023年第1期115卷 263-273页

作者： Liu, Hongbo Wu, Ziliang Zhang, Erqing Huang, Zhaosong Xu, Mingliang Cheng, Lechao Zhu, Minfeng Chen, Wei Zhejiang Univ State Key Lab CAD&CG Hangzhou 310000 Peoples R China Hangzhou Haolian Intelligent Technol Co Ltd Hangzhou 310000 Peoples R China Zhejiang Univ Hangzhou 310000 Peoples R China Zhengzhou Univ Zhengzhou 450000 Peoples R China Zhejiang Lab Hangzhou 310000 Peoples R China Zhejiang Univ China Southern Power Grid Joint Res Ctr AI Zhengzhou Peoples R China

visual querying of location-based data assists users in expressing query requirements, investigating query results and making inferences. However, directly accessing data records exposes individual location information and may cause privacy issues. Conventional aggregation-based methods can preserve location-relevant privacy but may lead to the loss of detailed information and failure of analysis. visualization aids users in gaining a deeper comprehension of the query process and the variation of information concerning privacy-preservation. In this paper, we present a privacy-aware visual query approach for location-based data. We propose a graph-based privacy-preserving scheme to protect location privacy in the visualization, and two visual metaphors to enhance understandings of information-variation in the privacy-preserving process. We design and implement a visual interface that supports a progressive process of query conditions specification and query results exploration. Experiments on real-world urban datasets demonstrate that our approach is capable of making a fair balance between location privacy and data analysis.& COPY;2023 Elsevier Ltd. All rights reserved.

关键词： visual query Privacy protection Location-based data data privacy

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visual analysis of Cyclic Time Series with Semantic Zoom 28

Visual Analysis of Cyclic Time Series with Semantic Zoom

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28th International conference on Information visualisation (IV)

作者： Louis, Patrick Mutlu, Belgin Suschnigg, Josef Schreck, Tobias Pro2Future GmbH Graz Austria Graz Univ Technol Graz Austria

ISBN: (纸本)9798350380170;9798350380163

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： visualization semantic zooming time series

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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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TiVEx: Optimized Processing for Time Series visual exploration 20

TiVEx: Optimized Processing for Time Series Visual Explorati...

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20th ACS/IEEE International conference on Computer Systems and Applications (AICCSA)

作者： Helal, Heba Sharaf, Mohamed A. Masud, Mohammad M. Chrysanthis, Panos K. United Arab Emirates Univ Coll Informat Technol Al Ain U Arab Emirates Univ Pittsburgh Sch Comp & Informat Pittsburgh PA USA Mansoura Univ Dept Comp Engn & Syst Coll Engn Mansoura Egypt

ISBN: (纸本)9798350319439

To facilitate fast-visual data analysis, there is a need for recommending top-k views with "interesting" insights automatically. However, working with high-dimensional time series data makes the process of view recommendations difficult. The primary obstacle lies in finding an automatic way to generate views with less processing time (efficiency) while still closely aligning with the ground truth (effectiveness). In this paper, we propose TiVEx (Time Series visual exploration), a technique to address this challenge. TiVEx aims to achieve a balance between efficiency and effectiveness in generating view recommendations. Through extensive experiments, we demonstrate significant cost savings achieved by TiVEx, indicating its efficiency. Furthermore, our analysis delves into the exploration of striking the right balance between efficiency and effectiveness.

关键词： visualization Recommendation Time series data Optimization

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Enabling Activity Based Intelligence with visual Analytics

Enabling Activity Based Intelligence with Visual Analytics

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International conference on Military Communications and Information Systems (ICMCIS)

作者： Lavigne, Valerie Bergeron-Guyard, Alexandre Def Res & Dev Canada Valcartier Res Ctr Quebec City PQ Canada

ISBN: (纸本)9798350343854

ThornViz is a visual analytics tool presenting data along multiple perspectives for discovering the unexpected and performing Activity Based Intelligence (ABI) analysis. It leverages a flexible data model along with a graph database to support ABI principles through interactive visual exploration of intelligence data including dynamic filtering and social network analysis techniques. ThornViz was applied to explore adversarial communication network data, to investigate crypto-currency transactions for suspicious financial activity, as well as to look for potential collaborations between NATO scientific activities, which demonstrated its multi-domain support. This paper was originally presented at the NATO Science and Technology Organization Symposium (ICMCIS) organized by the Information Systems Technology (IST) Panel, IST-200 RSY - the ICMCIS, held in Skopje, North Macedonia, 16-17 May 2023

关键词： visual analytics activity based intelligence social network analysis temporal analysis geospatial analysis Sankey diagram

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Spatial exploration Indicators in the Remote Assessment of visual Neglect 22nd

Spatial Exploration Indicators in the Remote Assessment of V...

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22nd International conference on Image analysis and Processing (ICIAP)

作者： Ferraro, Federica Iaconi, Giulia Genesio, Giulia Truffelli, Romina Amella, Roberta Simonini, Marina Dellepiane, Silvana Univ Genoa Dept Elect Elect & Telecommun Engn & Naval Archit Genoa Italy RAISE Ecosyst Genoa Italy La Colletta Hosp Struttura Complessa & Recupero Rieducaz Funz ASL3 Sistema Sanit Reg Liguria Genoa Italy

ISBN: (纸本)9783031431524;9783031431531

The visual neglect is a visuo-spatial attention disorder associated with stroke events and its presence is considered a negative prognostic factor of functional recovery. The specific assessment tests that are most frequently administered to evaluate the presence of this disorder are "paper and pencil tests", such as barrage tests. The current work presents the digital version of the Albert's barrage test for the evaluation of the Unilateral Spatial Neglect (USN) as it has been integrated into the ReMoVES tele-rehabilitation system. The data captured by this activity is broader and more complete than the information extractable from the paper version, including the order of the exploration sequence, work trajectories, punctual speed and other indicators. The procedure is preliminarily validated both on a control group of healthy subjects and on some patients. Several sessions are examined with the aim of observing which parameter of the digital Albert Test is statistically more significant for verifying the similarity in behavior between non-pathological subjects and the possible improvement over time of two pathological case studies.

关键词： Spatial exploration USN assessment test

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