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What makes a Gantt chart? This question proved unexpectedly difficult to answer when we set out to build a design space for Gantt charts. Existing definitions, each shaped by their respective research goals, made different scope choices that we could not directly reconcile. We reasoned about what should and should not count as a Gantt chart, developing concepts and tools along the way. We distinguish features that are essential to a chart type’s identity from those that can vary, and use these distinctions to map how chart types relate through what they share and lack. Applying these ideas to Gantt charts, radar charts, and table cartograms, we produce key insights on what boundary work reveals: definitions diverge for functional reasons, drawing boundaries exposes hidden structure in descriptive vocabulary such as feature entanglements, and scope choices shape how far findings can generalize. We came to understand that there is not a definitive answer, but that working through the question produced a functional definition that guided the design space we originally set out to build. Additionally, we present vocabulary and tools for reasoning about chart type boundaries and surfacing these boundary decisions, alongside a documented Gantt chart design space. Our broader reflection is that scope choices in chart-type-centered research—which determine what design spaces include, what grammars generate, and what perceptual studies measure—are research decisions worth making visible.

Line charts surface many features in time series data, from trends to periodicity to peaks and valleys. However, not every potentially important feature in the data may correspond to a visual feature which readers can detect or prioritize. In this study, we conducted a visual stenography task, where participants re-drew line charts to solicit information about the visual features they believed to be important. We systematically varied noise levels (SNR ~5-30 dB) across line charts to observe how visual clutter influences which features people prioritize in their sketches. We identified three key strategies that correlated with the noise present in the stimuli: the Replicator attempted to retain all major features of the line chart, including noise; the Trend Keeper prioritized trends, disregarding periodicity and peaks; and the De-noiser filtered out noise while preserving other features. Further, we found that participants tended to faithfully retain trends and peaks and valleys when these features were present, while periodicity and noise were represented in more qualitative or gestural ways: semantically rather than accurately. These results suggest a need to consider more flexible and human-centric ways of presenting, summarizing, pre-processing, or clustering time series data.

Understanding and distinguishing temporal patterns in time series data is essential for scientific discovery and decision-making. For example, in biomedical research, uncovering meaningful patterns in physiological signals can improve diagnosis, risk assessment, and patient outcomes. However, existing methods for time series pattern discovery face major challenges, including high computational complexity, limited interpretability, and difficulty in capturing meaningful temporal structures. To address these gaps, we introduce a novel learning framework that jointly trains two Transformer models using complementary time series representations: shapelet-based representations to capture localized temporal structures and traditional feature engineering to encode statistical properties. The learned shapelets serve as interpretable signatures that differentiate time series across classification labels. Additionally, we develop a visual analytics system -- SigTime -- with coordinated views to facilitate exploration of time series signatures from multiple perspectives, aiding in useful insights generation. We quantitatively evaluate our learning framework on eight publicly available datasets and one proprietary clinical dataset. Additionally, we demonstrate the effectiveness of our system through two usage scenarios along with the domain experts: one involving public ECG data and the other focused on preterm labor analysis.

This paper presents a visual analytics workflow for detecting stable cross-impact patterns in time series pairs. A sliding window technique computes multiple impact measures, including a novel Kendall’s tau variant that tolerates minor fluctuations. Evaluating these measures across various time lags reveals dynamic relationships between time series. An interactive Ikat plot facilitates the exploration of impact distributions, helping identify intervals where specific cross-impacts remain stable (e.g., trends in one series followed by similar or opposite trends in another after a lag). These intervals are extracted as events, whose temporal (and, when applicable, spatial) distributions can be analyzed to uncover broader patterns across multiple time series pairs and over extended time spans. This includes identifying co-occurring cross-impacts and variations in cross-impact presence or type across different periods and data subsets. Experiments on real-world datasets demonstrate the framework’s ability to isolate robust patterns, providing a scalable and interpretable approach to analyzing complex temporal dynamics.

Neural representations have been widely investigated as surrogate models for ensemble data prediction. However, existing approaches typically learn local (spatial) and global (ensemble and temporal) representations in a coupled manner, which limits their ability to model time-varying scenarios effectively. To overcome this limitation, we propose EnsembleNGP, a hybrid neural representation tailored for time-varying ensemble data that explicitly decomposes parameter and spatial spaces. The framework consists of two stages: parameter representation and spatial refinement. In the parameter representation stage, a parameter embedder and a decoder are jointly optimized to learn the mapping from a parameter set (i.e., ensemble and temporal parameters) to volumetric data. In the spatial refinement stage, a hash table is used to interpolate spatial features at a given spatial coordinate based on neighborhoods defined by multiple distance measurements. These interpolated features, together with the parameter embedding, are fed into a lightweight implicit neural representation that progressively leverages the learned spatial features to predict one voxel value. We evaluate our method on multiple time-varying ensemble datasets with diverse characteristics. Both quantitative and qualitative results demonstrate that EnsembleNGP significantly outperforms the state-of-the-art approaches, including CoordNet, MoE-INR, fV-SRN, APMGSRN, Explorable INR, and DRR Net. Moreover, it better captures parameter correlations and temporal evolution while achieving substantially lower storage and generation costs than traditional simulations.

Kernel density estimation (KDE) is a fundamental tool across statistics, data science, visualization and machine learning. In particular, it is widely used in analyzing and visualizing multi-dimensional data sets, including, e.g., geo-spatial data. Spatio-temporal KDE is an important extension to the basic KDE problem that also takes into account the temporal relation of the data points, and provides a method to visualize evolving data patterns over time. With the explosive growth of Big Data in both size and complexity, it is typically not feasible to run naive exact algorithms due to the extremely high run time and memory-space requirements. Especially challenging is the streaming setting where the data points arrive in an online fashion, one time step at a time, and we can only access the data in one pass with a fixed, sub-linear memory. Such a challenging setting renders most of the existing methods to be inadmissible. In this paper, we take on this challenge by providing scalable approximate frameworks with low memory footprints under the streaming setting. We present randomized algorithms for efficient computation of spatio-temporal KDE with strong theoretical guarantees and empirical support. Specifically, we introduce a weighted sampling scheme and discuss a random-feature-based sketching method, and analyze their complexity and use cases. The proposed methods are sub-linear in memory and do not require the knowledge of the length of the data stream in advance. Also, the methods provide nice trade-offs between speed and accuracy. Moreover, the sketching method can easily support deletions of data points from the stream, which is not trivial for most sampling methods to perform robustly. In addition, we show how the sketching method can be easily used to answer aggregate density queries, with a computational advantage over any sampling approaches. Experiments on synthetic and real world datasets demonstrate the quality and efficiency of these proposed techniques.

Implicit neural representations (INRs) for time-varying volumetric data are typically trained using dense sampling over spatiotemporal coordinates, where each observation corresponds to a single point in space and time. This coordinate-wise formulation requires extensive sampling during optimization, leading to high computational cost and inefficient use of temporal structure. In this work, we revisit this design choice and show that dense spatiotemporal sampling is not necessary for learning time-varying fields. Instead, we represent the data as a collection of spatially indexed time series and train INRs using sequence-level supervision over each spatial location, rather than coordinate-wise scalar samples. This reformulation eliminates the need for dense spatiotemporal sampling and instead learns each spatial location from its full temporal evolution in a structured manner. We demonstrate that this representation is compatible with a range of existing INR architectures and consistently improves reconstruction quality, while significantly reducing training cost. Furthermore, we show that this formulation can be combined with mixture-of-experts architectures, and that our MoE instantiation further improves reconstruction quality compared to both the base reformulation and existing MoE-based INR methods, providing a stronger capacity allocation under heterogeneous temporal dynamics.