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Potential harms from the under-representation of minorities in data, particularly in multi-modal settings, is a well-recognized concern. While there has been extensive effort in detecting such under-representation, resolution has remained a challenge. With recent generative AI advancements, large language and foundation models have emerged as versatile tools across various domains. In this paper, we propose Chameleon, a system that efficiently utilizes these tools to augment a dataset with minimal addition of synthetically generated tuples to enhance the coverage of the under-represented groups. Our system applies quality and outlier-detection tests to ensure the quality and semantic integrity of the generated tuples. In order to minimize the rejection chance of the generated tuples, we propose multiple strategies to provide a guide for the foundation model. Our experiment results, in addition to confirming the efficiency of our proposed algorithms, illustrate our approach's effectiveness, as the model's unfairness in a downstream task significantly dropped after data repair using Chameleon. 

Cohort studies are of significant importance in the field of healthcare analytics. However, existing methods typically involve manual, labor-intensive, and expert-driven pattern definitions or rely on simplistic clustering techniques that lack medical relevance. Automating cohort studies with interpretable patterns has great potential to facilitate healthcare analytics and data management but remains an unmet need in prior research efforts. In this paper, we present a cohort auto-discovery framework for interpretable healthcare analytics. It focuses on the effective identification, representation, and exploitation of cohorts characterized by medically meaningful patterns. In the framework, we propose CohortNet, a core model that can learn fine-grained patient representations by separately processing each feature, considering both individual feature trends and feature interactions at each time step. Subsequently, it employs K-Means in an adaptive manner to classify each feature into distinct states and a heuristic cohort exploration strategy to effectively discover substantial cohorts with concrete patterns. For each identified cohort, it learns comprehensive cohort representations with credible evidence through associated patient retrieval. Ultimately, given a new patient, CohortNet can leverage relevant cohorts with distinguished importance which can provide a more holistic understanding of the patient's conditions. Extensive experiments on three real-world datasets demonstrate that it consistently outperforms state-of-the-art approaches, resulting in improvements in AUC-PR scores ranging from 2.8% to 4.1%, and offers interpretable insights from diverse perspectives in a top-down fashion. 

Existing table search techniques define table relatedness with unionablility and/or joinability. While these are valuable, they do not suffice for most data analysis tasks that involve numerical data, which is often aggregated over geographical, temporal, or other groups. In this demonstration, we showcase ARTS, a novel table search system centered on the unique concept of aggregate relatedness. By leveraging pre-trained language models, ARTS offers a superior column semantics understanding capability, with good labels created for both textual and numerical columns. This demonstration will offer attendees hands-on interaction with our system, revealing its potential in effectively addressing real-world data analysis challenges. 

Exploratory data analysis can uncover interesting data insights from data. Current methods utilize interestingness measures designed based on system designers' perspectives, thus inherently restricting the insights to their defined scope. These systems, consequently, may not adequately represent a broader range of user interests. Furthermore, most existing approaches that formulate interestingness measure are rule-based, which makes them inevitably brittle and often requires holistic re-design when new user needs are discovered. This paper presents a data-driven technique for deriving an interestingness measure that learns from annotated data. We further develop an innovative annotation algorithm that significantly reduces the annotation cost, and an insight synthesis algorithm based on the Markov Chain Monte Carlo method for efficient discovery of interesting insights. We consolidate these ideas into a system. Our experimental outcomes and user studies demonstrate that DAISY can effectively discover a broad range of interesting insights, thereby substantially advancing the current state-of-the-art.