arXiv subjects

Lu Ying

Publications and source records attributed to Lu Ying.

8 recordsLinked to original sources

arXiv2025

A Constructive Scientific Methodology to Improve Climate Figures from IPCC

We propose a methodology to improve figures from the Intergovernmental Panel on Climate Change (IPCC), ensuring that all modifications remain scientifically rigorous. IPCC figures are notoriously difficult to understand, and although designers have proposed alternatives, these lack formal IPCC validation and can be dismissed by skeptics. To address this gap, our approach starts from official IPCC figures. We gather their associated learning objectives and devise tests to score a pool of figure readers to assess how well they learn the objectives.We define improvement as higher scores obtained by a comparable reader pool after viewing a revised figure, where all modifications undergo review to ensure scientific validity. This assessment gives freedom to designers, who can deviate from the original design while making sure the objectives are still met and improved. We demonstrate the methodology through a case study and describe unexpected challenges encountered during the process.

cs.HC↗

arXiv2025

Composing Data Stories with Meta Relations

To facilitate the creation of compelling and engaging data stories, AI-powered tools have been introduced to automate the three stages in the workflow: analyzing data, organizing findings, and creating visuals. However, these tools rely on data-level information to derive inflexible relations between findings. Therefore, they often create one-size-fits-all data stories. Differently, our formative study reveals that humans heavily rely on meta relations between these findings from diverse domain knowledge and narrative intent, going beyond datasets, to compose their findings into stylized data stories. Such a gap indicates the importance of introducing meta relations to elevate AI-created stories to a satisfactory level. Though necessary, it is still unclear where and how AI should be involved in working with humans on meta relations. To answer the question, we conducted an exploratory user study with Remex, an AI-powered data storytelling tool that suggests meta relations in the analysis stage and applies meta relations for data story organization. The user study reveals various findings about introducing AI for meta relations into the storytelling workflow, such as the benefit of considering meta relations and their diverse expected usage scenarios. Finally, the paper concludes with lessons and suggestions about applying meta relations to compose data stories to hopefully inspire future research.

cs.HC↗

arXiv2024

RCInvestigator: Towards Better Investigation of Anomaly Root Causes in Cloud Computing Systems

Finding the root causes of anomalies in cloud computing systems quickly is crucial to ensure availability and efficiency since accurate root causes can guide engineers to take appropriate actions to address the anomalies and maintain customer satisfaction. However, it is difficult to investigate and identify the root causes based on large-scale and high-dimension monitoring data collected from complex cloud computing environments. Due to the inherently dynamic characteristics of cloud computing systems, the existing approaches in practice largely rely on manual analyses for flexibility and reliability, but massive unpredictable factors and high data complexity make the process time-consuming. Despite recent advances in automated detection and investigation approaches, the speed and quality of root cause analyses remain limited by the lack of expert involvement in these approaches. The limitations found in the current solutions motivate us to propose a visual analytics approach that facilitates the interactive investigation of the anomaly root causes in cloud computing systems. We identified three challenges, namely, a) modeling databases for the root cause investigation, b) inferring root causes from large-scale time series, and c) building comprehensible investigation results. In collaboration with domain experts, we addressed these challenges with RCInvestigator, a novel visual analytics system that establishes a tight collaboration between human and machine and assists experts in investigating the root causes of cloud computing system anomalies. We evaluated the effectiveness of RCInvestigator through two use cases based on real-world data and received positive feedback from experts.

cs.HC↗

arXiv2023

Reviving Static Charts into Live Charts

Data charts are prevalent across various fields due to their efficacy in conveying complex data relationships. However, static charts may sometimes struggle to engage readers and efficiently present intricate information, potentially resulting in limited understanding. We introduce "Live Charts," a new format of presentation that decomposes complex information within a chart and explains the information pieces sequentially through rich animations and accompanying audio narration. We propose an automated approach to revive static charts into Live Charts. Our method integrates GNN-based techniques to analyze the chart components and extract data from charts. Then we adopt large natural language models to generate appropriate animated visuals along with a voice-over to produce Live Charts from static ones. We conducted a thorough evaluation of our approach, which involved the model performance, use cases, a crowd-sourced user study, and expert interviews. The results demonstrate Live Charts offer a multi-sensory experience where readers can follow the information and understand the data insights better. We analyze the benefits and drawbacks of Live Charts over static charts as a new information consumption experience.

cs.HC↗

arXiv2023

Notable: On-the-fly Assistant for Data Storytelling in Computational Notebooks

Computational notebooks are widely used for data analysis. Their interleaved displays of code and execution results (e.g., visualizations) are welcomed since they enable iterative analysis and preserve the exploration process. However, the communication of data findings remains challenging in computational notebooks. Users have to carefully identify useful findings from useless ones, document them with texts and visual embellishments, and then organize them in different tools. Such workflow greatly increases their workload, according to our interviews with practitioners. To address the challenge, we designed Notable to offer on-the-fly assistance for data storytelling in computational notebooks. It provides intelligent support to minimize the work of documenting and organizing data findings and diminishes the cost of switching between data exploration and storytelling. To evaluate Notable, we conducted a user study with 12 data workers. The feedback from user study participants verifies its effectiveness and usability.

cs.HC↗

arXiv2022

VAID: Indexing View Designs in Visual Analytics System

Visual analytics (VA) systems have been widely used in various application domains. However, VA systems are complex in design, which imposes a serious problem: although the academic community constantly designs and implements new designs, the designs are difficult to query, understand, and refer to by subsequent designers. To mark a major step forward in tackling this problem, we index VA designs in an expressive and accessible way, transforming the designs into a structured format. We first conducted a workshop study with VA designers to learn user requirements for understanding and retrieving professional designs in VA systems. Thereafter, we came up with an index structure VAID to describe advanced and composited visualization designs with comprehensive labels about their analytical tasks and visual designs. The usefulness of VAID was validated through user studies. Our work opens new perspectives for enhancing the accessibility and reusability of professional visualization designs.

cs.HC↗

arXiv2022

MetaGlyph: Automatic Generation of Metaphoric Glyph-based Visualization

Glyph-based visualization achieves an impressive graphic design when associated with comprehensive visual metaphors, which help audiences effectively grasp the conveyed information through revealing data semantics. However, creating such metaphoric glyph-based visualization (MGV) is not an easy task, as it requires not only a deep understanding of data but also professional design skills. This paper proposes MetaGlyph, an automatic system for generating MGVs from a spreadsheet. To develop MetaGlyph, we first conduct a qualitative analysis to understand the design of current MGVs from the perspectives of metaphor embodiment and glyph design. Based on the results, we introduce a novel framework for generating MGVs by metaphoric image selection and an MGV construction. Specifically, MetaGlyph automatically selects metaphors with corresponding images from online resources based on the input data semantics. We then integrate a Monte Carlo tree search algorithm that explores the design of an MGV by associating visual elements with data dimensions given the data importance, semantic relevance, and glyph non-overlap. The system also provides editing feedback that allows users to customize the MGVs according to their design preferences. We demonstrate the use of MetaGlyph through a set of examples, one usage scenario, and validate its effectiveness through a series of expert interviews.

cs.HC↗

arXiv2020

Narrative Transitions in Data Videos

Transitions are widely used in data videos to seamlessly connect data-driven charts or connect visualizations and non-data-driven motion graphics. To inform the transition designs in data videos, we conduct a content analysis based on more than 3500 clips extracted from 284 data videos. We annotate visualization types and transition designs on these segments, and examine how these transitions help make connections between contexts. We propose a taxonomy of transitions in data videos, where two transition categories are defined in building fluent narratives by using visual variables.

cs.HC↗