MOD

Data profilers play a crucial role in the preprocessing phase of data analysis by identifying quality issues such as missing, extreme, or erroneous values. Traditionally, profilers have relied solely on statistical methods, which lead to high false ...

An ER diagram is a fundamental visual abstraction to design a database. Modern ER notation has evolved with UML symbols to represent both entities (logical level) and relational tables (physical level). On the other hand, flow diagrams (flowcharts, ...

Dataset search is a long-standing problem across both industry and academia. While most industry tools focus on identifying one or more datasets matching a user-specified query, most recent academic papers focus on the subsequent problems of join and ...

Data preprocessing is an important task in machine learning which can significantly improve model outcomes. However, evaluating the impact of data preprocessing is often difficult. There is a need for tools which make it transparent to the user on how ...

Multiple works in data management research focus on automating the processes of data augmentation and feature discovery to save users from having to perform these tasks manually. Yet, this automation often leads to a disconnect with the users, as it ...

We exploit large language models (LLMs) to automate the end-to-end process of descriptive analytics and visualization. A user simply declares who they are and provides their data set. Our tool LLM4Vis sets analysis goals or metrics, generates code to ...

Schema Matching, the task of finding correspondences among attributes of different schemata, plays an important role in data integration. The task has been extensively researched, leading to the development of multiple algorithmic approaches, many of ...

A critical challenge with querying video data is that the user is often unaware of the contents of the video, its structure, and the exact terminology to use in the query. While these problems exist in exploratory querying settings over traditional ...

We explore data management for longitudinal study survey instruments: (i) Survey instrument evolution presents a unique data integration challenge; and (ii) Longitudinal study data frequently requires repeated, task-specific integration efforts. We ...

Automatic information extraction, e.g., into a tabular format, is crucial for leveraging knowledge in large text collections. Yet, creating such extraction pipelines for custom target attributes can cause high overheads, while off-the-shelf tools might ...

Data preprocessing and engineering are essential parts of any AI system, as indicated by the current trend of data-centric AI. However, until now, explainability efforts have almost exclusively focused on models. We propose explanations for preprocessing ...

Causal data discovery is crucial in scientific research by uncovering causal links among a variety of observed variables. Causal dataset discovery is the task of identifying datasets that contain columns that have causal relationships with columns in a ...

Text2SQL is typically considered a one-shot process where the user gives a natural language query and receives an SQL query in return. This approach is fraught with potential concerns, such as syntactical errors, logical mismatches, and schema ...