publications

(*: equal contribution)

2024

1. HILDA

   Drag, Drop, Merge: A Tool for Streamlining Integration of Longitudinal Survey Instruments

   Pratik Pokharel^*, Juseung Lee^*, Oliver Kennedy, Marianthi Markatou, Andrew Talal, Jeff Good, and Raktim Mukhopadhyay

   In Proceedings of the 2024 Workshop on Human-In-the-Loop Data Analytics, Santiago, AA, Chile, 2024

   Abs DOI PDF

   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 present DDM (Drag, Drop, Merge), a user interface for documenting relationships among attributes of source schemas into a form that can streamline subsequent efforts to generate task-specific datasets. DDM employs a "human-in-the-loop" approach, allowing users to validate and refine semantic mappings. Through a simulation of user interactions with DDM, we demonstrate its viability as a way to reduce cognitive overhead for longitudinal study data curators.

2022

1. VLDB

   CaJaDE: explaining query results by augmenting provenance with context

   Chenjie Li, Juseung Lee, Zhengjie Miao, Boris Glavic, and Sudeepa Roy

   Proc. VLDB Endow., Aug 2022

   Abs DOI

   In this work, we demonstrate CaJaDE (Context-Aware Join-Augmented Deep Explanations), a system that explains query results by augmenting provenance with contextual information from other related tables in the database. Given two query results whose difference the user wants to understand, we enumerate possible ways of joining the provenance (i.e., contributing input tuples) of these two query results with tuples from other relevant tables in the database that were not used in the query. We use patterns to concisely explain the difference between the augmented provenance of the two query results. CaJaDE, through a comprehensive UI, enables the user to formulate questions and explore explanations interactively.