Data tables are widely used to record critical information, enabling decision-makers to derive insights through table question answering (Table QA). However, the metadata from table schemas alone often fail to capture the underlying business semantics embedded in the tabular data, leading to reasoning errors. Existing automated approaches to semantic enrichment face challenges in insufficient data utilization, narrow feature coverage, and limited interpretability. To overcome these limitations, we propose SemTabla, an interactive system that employs a human-in-the-loop mechanism to extract comprehensive and interpretable semantics from tabular data. Our key contributions include: (1) a hierarchical framework for extracting semantic attributes; (2) a novel sampling method that identifies critical but rare row instances; and (3) an interactive interface that supports visualization, validation, and refinement of the extracted table semantics. A user study confirmed the system’s usability, and quantitative experiments demonstrate that the extracted semantics significantly enhance the reasoning capabilities of large language models.

The exploratory visual analysis (EVA) of time series data uses visualization as the main output medium and input interface for exploring new data. However, for users who lack visual analysis expertise, interpreting and manipulating EVA can be challenging. Thus, providing guidance on EVA is necessary and two relevant questions need to be answered. First, how to recommend interesting insights to provide a first glance at data and help develop an exploration goal. Second, how to provide step-by-step EVA suggestions to help identify which parts of the data to explore. In this work, we present a reinforcement learning (RL)-based system, Visail, which generates EVA sequences to guide the exploration of time series data. As a user uploads a time series dataset, Visail can generate step-by-step EVA suggestions, while each step is visualized as an annotated chart combined with textual descriptions. The RL-based algorithm uses exploratory data analysis knowledge to construct the state and action spaces for the agent to imitate human analysis behaviors in data exploration tasks. In this way, the agent learns the strategy of generating coherent EVA sequences through a well-designed network. To evaluate the effectiveness of our system, we conducted an ablation study, a user study, and two case studies. The results of our evaluation suggested that Visail can provide effective guidance on supporting EVA on time series data.

BACKGROUND: Patients in the intensive care unit (ICU) are often in critical condition and have a high mortality rate. Accurately predicting the survival probability of ICU patients is beneficial to timely care and prioritizing medical resources to improve the overall patient population survival. Models developed by deep learning (DL) algorithms show good performance on many models. However, few DL algorithms have been validated in the dimension of survival time or compared with traditional algorithms. METHODS: Variables from the Early Warning Score, Sequential Organ Failure Assessment Score, Simplified Acute Physiology Score II, Acute Physiology and Chronic Health Evaluation (APACHE) II, and APACHE IV models were selected for model development. The Cox regression, random survival forest (RSF), and DL methods were used to develop prediction models for the survival probability of ICU patients. The prediction performance was independently evaluated in the MIMIC-III Clinical Database (MIMIC-III), the eICU Collaborative Research Database (eICU), and Shanghai Pulmonary Hospital Database (SPH). RESULTS: Forty variables were collected in total for model development. 83 943 participants from 3 databases were included in the study. The New-DL model accurately stratified patients into different survival probability groups with a C-index of >0.7 in the MIMIC-III, eICU, and SPH, performing better than the other models. The calibration curves of the models at 3 and 10 days indicated that the prediction performance was good. A user-friendly interface was developed to enable the model's convenience. CONCLUSIONS: Compared with traditional algorithms, DL algorithms are more accurate in predicting the survival probability during ICU hospitalization. This novel model can provide reliable, individualized survival probability prediction.

Event sequence data record series of discrete events in the time order of occurrence. They are commonly observed in a variety of applications ranging from electronic health records to network logs, with the characteristics of large-scale, high-dimensional and heterogeneous. This high complexity of event sequence data makes it difficult for analysts to manually explore and find patterns, resulting in ever-increasing needs for computational and perceptual aids from visual analytics techniques to extract and communicate insights from event sequence datasets. In this paper, we review the state-of-the-art visual analytics approaches, characterize them with our proposed design space, and categorize them based on analytical tasks and applications. From our review of relevant literature, we have also identified several remaining research challenges and future research opportunities.

Anomaly detection is a common analytical task that aims to identify rare cases that differ from the typical cases that make up the majority of a dataset. When analyzing event sequence data, the task of anomaly detection can be complex because the sequential and temporal nature of such data results in diverse definitions and flexible forms of anomalies. This, in turn, increases the difficulty in interpreting detected anomalies. In this article, we propose a visual analytic approach for detecting anomalous sequences in an event sequence dataset via an unsupervised anomaly detection algorithm based on Variational AutoEncoders. We further compare the anomalous sequences with their reconstructions and with the normal sequences through a sequence matching algorithm to identify event anomalies. A visual analytics system is developed to support interactive exploration and interpretations of anomalies through novel visualization designs that facilitate the comparison between anomalous sequences and normal sequences. Finally, we quantitatively evaluate the performance of our anomaly detection algorithm, demonstrate the effectiveness of our system through case studies, and report feedback collected from study participants.

Advertising posters are a commonly used form of information presentation to promote a product. Producing advertising posters often takes much time and effort of designers when confronted with abundant choices of design elements and layouts. This paper presents Vinci, an intelligent system that supports the automatic generation of advertising posters. Given the user-specified product image and taglines, Vinci uses a deep generative model to match the product image with a set of design elements and layouts for generating an aesthetic poster. The system also integrates online editing-feedback that supports users in editing the posters and updating the generated results with their design preference. Through a series of user studies and a Turing test, we found that Vinci can generate posters as good as human designers and that the online editing-feedback improves the efficiency in poster modification.

Causality is crucial to understanding the mechanisms behind complex systems and making decisions that lead to intended outcomes. Event sequence data is widely collected from many real-world processes, such as electronic health records, web clickstreams, and financial transactions, which transmit a great deal of information reflecting the causal relations among event types. Unfortunately, recovering causalities from observational event sequences is challenging, as the heterogeneous and high-dimensional event variables are often connected to rather complex underlying event excitation mechanisms that are hard to infer from limited observations. Many existing automated causal analysis techniques suffer from poor explainability and fail to include an adequate amount of human knowledge. In this paper, we introduce a visual analytics method for recovering causalities in event sequence data. We extend the Granger causality analysis algorithm on Hawkes processes to incorporate user feedback into causal model refinement. The visualization system includes an interactive causal analysis framework that supports bottom-up causal exploration, iterative causal verification and refinement, and causal comparison through a set of novel visualizations and interactions. We report two forms of evaluation: a quantitative evaluation of the model improvements resulting from the user-feedback mechanism, and a qualitative evaluation through case studies in different application domains to demonstrate the usefulness of the system.

Clinical decision support systems are widely used to assist with medical decision making. However, clinical decision support systems typically require manually curated rules and other data that are difficult to maintain and keep up to date. Recent systems leverage advanced deep learning techniques and electronic health records to provide a more timely and precise result. Many of these techniques have been developed with a common focus on predicting upcoming medical events. However, although the prediction results from these approaches are promising, their value is limited by their lack of interpretability. To address this challenge, we introduce CarePre, an intelligent clinical decision assistance system. The system extends a state-of-the-art deep learning model to predict upcoming diagnosis events for a focal patient based on his or her historical medical records. The system includes an interactive framework together with intuitive visualizations designed to support diagnosis, treatment outcome analysis, and the interpretation of the analysis results. We demonstrate the effectiveness and usefulness of the CarePre system by reporting results from a quantities evaluation of the prediction algorithm, two case studies, and interviews with senior physicians and pulmonologists.

Anomaly detection is a common analytical task that aims to identify rare cases that differ from the typical cases that make up the majority of a dataset. When applied to the analysis of event sequence data, the task of anomaly detection can be complex because the sequential and temporal nature of such data results in diverse definitions and flexible forms of anomalies. This, in turn, increases the difficulty in interpreting detected anomalies. In this paper, we propose an unsupervised anomaly detection algorithm based on Variational AutoEncoders (VAE) to estimate underlying normal progressions for each given sequence represented as occurrence probabilities of events along the sequence progression. Events in violation of their occurrence probability are identified as abnormal. We also introduce a visualization system, EventThread3 (ET3, to support interactive exploration and interpretations of anomalies within the context of normal sequence progressions in the dataset through comprehensive one-to-many sequence comparison. Finally, we quantitatively evaluate the performance of our anomaly detection algorithm and demonstrate the effectiveness of our system through a case study.

Event sequence data is common to a broad range of application domains, from security to health care to scholarly communication. This form of data captures information about the progression of events for an individual entity (e.g., a computer network device; a patient; an author) in the form of a series of time-stamped observations. Moreover, each event is associated with an event type (e.g., a computer login attempt, or a hospital discharge). Analyses of event sequence data have been shown to help reveal important temporal patterns, such as clinical paths resulting in improved outcomes, or an understanding of common career trajectories for scholars. Moreover, recent research has demonstrated a variety of techniques designed to overcome methodological challenges such as large volumes of data and high dimensionality. However, the effective identification and analysis of latent stages of progression, which can allow for variation within different but similarly evolving event sequences, remain a significant challenge with important real-world motivations. In this paper, we propose an unsupervised stage analysis algorithm to identify semantically meaningful progression stages as well as the critical events which help define those stages. The algorithm follows three key steps: (1) event representation estimation, (2) event sequence warping and alignment, and (3) sequence segmentation. We also present a novel visualization system, ET2, which interactively illustrates the results of the stage analysis algorithm to help reveal evolution patterns across stages. Finally, we report three forms of evaluation for ET2: (1) case studies with two real-world datasets, (2) interviews with domain expert users, and (3) a performance evaluation on the progression analysis algorithm and the visualization design.