Aécio Santos

Aécio Santos, Eduardo H. M. Pena, Roque Lopez et al.

Data harmonization is an essential task that entails integrating datasets from diverse sources. Despite years of research in this area, it remains a time-consuming and challenging task due to schema mismatches, varying terminologies, and differences in data collection methodologies. This paper presents the case for agentic data harmonization as a means to both empower experts to harmonize their data and to streamline the process. We introduce Harmonia, a system that combines LLM-based reasoning, an interactive user interface, and a library of data harmonization primitives to automate the synthesis of data harmonization pipelines. We demonstrate Harmonia in a clinical data harmonization scenario, where it helps to interactively create reusable pipelines that map datasets to a standard format. Finally, we discuss challenges and open problems, and suggest research directions for advancing our vision.

Aécio Santos, Aline Bessa, Fernando Chirigati et al.

The increasing availability of structured datasets, from Web tables and open-data portals to enterprise data, opens up opportunities~to enrich analytics and improve machine learning models through relational data augmentation. In this paper, we introduce a new class of data augmentation queries: join-correlation queries. Given a column $Q$ and a join column $K_Q$ from a query table $\mathcal{T}_Q$, retrieve tables $\mathcal{T}_X$ in a dataset collection such that $\mathcal{T}_X$ is joinable with $\mathcal{T}_Q$ on $K_Q$ and there is a column $C \in \mathcal{T}_X$ such that $Q$ is correlated with $C$. A naïve approach to evaluate these queries, which first finds joinable tables and then explicitly joins and computes correlations between $Q$ and all columns of the discovered tables, is prohibitively expensive. To efficiently support correlated column discovery, we 1) propose a sketching method that enables the construction of an index for a large number of tables and that provides accurate estimates for join-correlation queries, and 2) explore different scoring strategies that effectively rank the query results based on how well the columns are correlated with the query. We carry out a detailed experimental evaluation, using both synthetic and real data, which shows that our sketches attain high accuracy and the scoring strategies lead to high-quality rankings.

Sonia Castelo, Rémi Rampin, Aécio Santos et al.

The large volumes of structured data currently available, from Web tables to open-data portals and enterprise data, open up new opportunities for progress in answering many important scientific, societal, and business questions. However, finding relevant data is difficult. While search engines have addressed this problem for Web documents, there are many new challenges involved in supporting the discovery of structured data. We demonstrate how the Auctus dataset search engine addresses some of these challenges. We describe the system architecture and how users can explore datasets through a rich set of queries. We also present case studies which show how Auctus supports data augmentation to improve machine learning models as well as to enrich analytics.

Sungsoo Ray Hong, Sonia Castelo, Vito D'Orazio et al.

The use of Automated Machine Learning (AutoML) systems are highly open-ended and exploratory. While rigorously evaluating how end-users interact with AutoML is crucial, establishing a robust evaluation methodology for such exploratory systems is challenging. First, AutoML is complex, including multiple sub-components that support a variety of sub-tasks for synthesizing ML pipelines, such as data preparation, problem specification, and model generation, making it difficult to yield insights that tell us which components were successful or not. Second, because the usage pattern of AutoML is highly exploratory, it is not possible to rely solely on widely used task efficiency and effectiveness metrics as success metrics. To tackle the challenges in evaluation, we propose an evaluation methodology that (1) guides AutoML builders to divide their AutoML system into multiple sub-system components, and (2) helps them reason about each component through visualization of end-users' behavioral patterns and attitudinal data. We conducted a study to understand when, how, why, and applying our methodology can help builders to better understand their systems and end-users. We recruited 3 teams of professional AutoML builders. The teams prepared their own systems and let 41 end-users use the systems. Using our methodology, we visualized end-users' behavioral and attitudinal data and distributed the results to the teams. We analyzed the results in two directions: what types of novel insights the AutoML builders learned from end-users, and (2) how the evaluation methodology helped the builders to understand workflows and the effectiveness of their systems. Our findings suggest new insights explaining future design opportunities in the AutoML domain as well as how using our methodology helped the builders to determine insights and let them draw concrete directions for improving their systems.

Aécio Santos, Sonia Castelo, Cristian Felix et al.

While the demand for machine learning (ML) applications is booming, there is a scarcity of data scientists capable of building such models. Automatic machine learning (AutoML) approaches have been proposed that help with this problem by synthesizing end-to-end ML data processing pipelines. However, these follow a best-effort approach and a user in the loop is necessary to curate and refine the derived pipelines. Since domain experts often have little or no expertise in machine learning, easy-to-use interactive interfaces that guide them throughout the model building process are necessary. In this paper, we present Visus, a system designed to support the model building process and curation of ML data processing pipelines generated by AutoML systems. We describe the framework used to ground our design choices and a usage scenario enabled by Visus. Finally, we discuss the feedback received in user testing sessions with domain experts.