The 13th IEEE Symposium on
Large Data Analysis and Visualization

in conjunction with IEEE VIS 2023 Melbourne, Australia
22-27 October 2023

Paper Type Categories

Technique & Algorithm

A technique or algorithm paper introduces a novel technique or algorithm that has not previously appeared in the literature, or that significantly extends known techniques or algorithms. The technique or algorithm description provided in the paper should be complete enough that a competent graduate student in visualization could implement the work, and the authors should create a prototype implementation of the methods. This technique should ideally be of general application rather than be restricted to a single task or single source of data, and the exposition should be focused on what the technique does, how it does it, the tasks and data sets for which this new method is appropriate, and what the computational and other costs are. An evaluation is likely to strengthen the paper, in particular on large data.


A system paper describes a solution to a problem where the major task is building a large complex software artifact, applying largely known visualization techniques. The system that is described is both novel and important, and has been implemented. Here, the focus should be on the design decisions, the implications for software / hardware structure, and comparison with other systems. The comparison includes specific discussion of how the described system differs from and is, in some significant respects, superior to those systems.

Application & Design Study

An application or design study paper explores the choices made when applying visualization techniques in an application area, for example relating the visual encodings and interaction techniques to the requirements of the target task. These papers typically include an encapsulated description of a problem domain and the questions to be resolved by visualization, then describes the application of visualization to the task, any novel techniques developed, and how the visualization solution answered the questions posed. The results of the study, including insights generated in the application domain and visualization knowledge generated through the research process, should be clearly conveyed. The work will be judged by the design lessons learned or insights gleaned for visualization research - which may or may not include novel visualization techniques, algorithms, or systems - on which future contributors can build. We invite submissions on any application area dealing with large data.

Empirical Study

An empirical study paper explores the usage of visualization by people or provides a detailed analysis of the characteristics of a visualization approach, for example, its technical performance. This kind of paper presents a study, either qualitative or quantitative, of visualization techniques or systems. The research contribution will be judged on the validity and importance of the results, including, where appropriate, the definition of hypotheses, tasks, data sets, the rigorous collection and examination/analysis/coding of data, the selection of subjects and cases, as well as validation, discussion, and conclusions.

State of the Practice

State of the practice reports provide an up-to-date and comprehensive overview of a special topic of current interest related to large data analysis and visualization. State of the art reports on cross-sectional, multi-disciplinary areas with a common theme of large data are highly welcomed.

Position Statements

A position paper presents an arguable opinion about an issue, accepted assumption, or technique relevant to large data analysis or visualization. This kind of paper takes an opinionated stance and justifies this stance with corroborating evidence. A position paper often takes evidence from previous publications and other existing information rather than making new studies. The paper is judged on the presentation of a convincing, evidence-based position and the importance of that position. Position papers that buck tradition by providing well-reasoned arguments for a stance that is counterintuitive or contrary to common knowledge are highly valued.