Antonis Mandamadiotis

For over five decades, enabling non-technical users to access databases without SQL has been a major research focus. Despite recent advances in Text-to-SQL, the primary focus remains on accuracy, often overlooking real-world deployment challenges. This demo presents DataDazzle, a natural language data exploration system. It allows users to query data, review and verify answers, and benefit from recommendations, offering a comprehensive data exploration experience.

In this demonstration, we present DatAgent, an intelligent data assistant system that allows users to ask queries in natural language, and can respond in natural language as well. Moreover, the system actively guides the user using different types of recommendations and hints, and learns from user actions. We will demonstrate different exploration scenarios that show how the system and the user engage in a human-like interaction inspired by the interaction paradigm of chatbots and virtual assistants.

The exploration of large, real-world databases poses major challenges to users due to their volume and complexity. SQL is the preferred language for data exploration. However, the process of iteratively refining SQL queries is tedious and time consuming. We formulate the automation of personalized SQL-based data exploration as the problem of suggesting the most relevant query and accounting for user feedback at each step. We develop an end-to-end solution and a system to assist users in exploring different components of a complex database. We instantiate our solution using Multi-Armed Bandits, a category of algorithms that are suitable for interactive online learning by balancing exploration with exploitation. We design a lightweight algorithm to personalize stepwise SQL recommendations that efficiently discovers the current user preferences in coordination with that user’s feedback and what other users prefer. We run extensive experiments that demonstrate the utility of our approach for large-scale data exploration.

Understanding how changes in actions or policies impact different population segments (users, items, etc.) is central to data-driven decision-making, but it is time-consuming and labor-intensive. We introduce a novel framework that combines user-defined search areas with causal inference to automatically identify responsive subgroups, i.e., subgroups most likely to benefit from a specific action or intervention, by estimating the impact of interventions, while also promoting subgroup diversity to ensure broad and representative insights. To support this, we develop a formal problem definition and an efficient search strategy to navigate the large combinatorial space of possible subgroups. We propose efficient algorithms that use pretrained causal effect estimators to dynamically surface the top- K subgroups. Experiments across multiple datasets and estimation techniques demonstrate the scalability and effectiveness of our framework for large-scale analysis.