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

Pratik Pokharel; Juseung Lee; Oliver Kennedy; Marianthi Markatou; Andrew Talal; Jeff Good; Raktim Mukhopadhyay

doi:10.1145/3665939.3665965

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

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

SUNY Buffalo

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

Abstract

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.

Original language	English
Title of host publication	HILDA 2024 - Workshop on Human-In-the-Loop Data Analytics Co-located with SIGMOD 2024
Publisher	Association for Computing Machinery, Inc
ISBN (Electronic)	9798400706936
DOIs	https://doi.org/10.1145/3665939.3665965
State	Published - Jun 18 2024
Event	2024 Workshop on Human-In-the-Loop Data Analytics, HILDA 2024, Co-located with SIGMOD 2024 - Santiago, Chile Duration: Jun 18 2024 → Jun 18 2024

Publication series

Name	HILDA 2024 - Workshop on Human-In-the-Loop Data Analytics Co-located with SIGMOD 2024

Conference

Conference	2024 Workshop on Human-In-the-Loop Data Analytics, HILDA 2024, Co-located with SIGMOD 2024
Country/Territory	Chile
City	Santiago
Period	06/18/24 → 06/18/24

Access to Document

10.1145/3665939.3665965

Cite this

Pokharel, P., Lee, J., Kennedy, O., Markatou, M., Talal, A., Good, J., & Mukhopadhyay, R. (2024). Drag, Drop, Merge: A Tool for Streamlining Integration of Longitudinal Survey Instruments. In HILDA 2024 - Workshop on Human-In-the-Loop Data Analytics Co-located with SIGMOD 2024 (HILDA 2024 - Workshop on Human-In-the-Loop Data Analytics Co-located with SIGMOD 2024). Association for Computing Machinery, Inc. https://doi.org/10.1145/3665939.3665965

Pokharel, Pratik ; Lee, Juseung ; Kennedy, Oliver et al. / Drag, Drop, Merge : A Tool for Streamlining Integration of Longitudinal Survey Instruments. HILDA 2024 - Workshop on Human-In-the-Loop Data Analytics Co-located with SIGMOD 2024. Association for Computing Machinery, Inc, 2024. (HILDA 2024 - Workshop on Human-In-the-Loop Data Analytics Co-located with SIGMOD 2024).

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

abstract = "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.",

author = "Pratik Pokharel and Juseung Lee and Oliver Kennedy and Marianthi Markatou and Andrew Talal and Jeff Good and Raktim Mukhopadhyay",

note = "Publisher Copyright: {\textcopyright} 2024 Copyright is held by the owner/author(s).; 2024 Workshop on Human-In-the-Loop Data Analytics, HILDA 2024, Co-located with SIGMOD 2024 ; Conference date: 18-06-2024 Through 18-06-2024",

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doi = "10.1145/3665939.3665965",

language = "English",

series = "HILDA 2024 - Workshop on Human-In-the-Loop Data Analytics Co-located with SIGMOD 2024",

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Pokharel, P, Lee, J, Kennedy, O , Markatou, M , Talal, A , Good, J & Mukhopadhyay, R 2024, Drag, Drop, Merge: A Tool for Streamlining Integration of Longitudinal Survey Instruments. in HILDA 2024 - Workshop on Human-In-the-Loop Data Analytics Co-located with SIGMOD 2024. HILDA 2024 - Workshop on Human-In-the-Loop Data Analytics Co-located with SIGMOD 2024, Association for Computing Machinery, Inc, 2024 Workshop on Human-In-the-Loop Data Analytics, HILDA 2024, Co-located with SIGMOD 2024, Santiago, Chile, 06/18/24. https://doi.org/10.1145/3665939.3665965

Drag, Drop, Merge: A Tool for Streamlining Integration of Longitudinal Survey Instruments. / Pokharel, Pratik; Lee, Juseung; Kennedy, Oliver et al.
HILDA 2024 - Workshop on Human-In-the-Loop Data Analytics Co-located with SIGMOD 2024. Association for Computing Machinery, Inc, 2024. (HILDA 2024 - Workshop on Human-In-the-Loop Data Analytics Co-located with SIGMOD 2024).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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AU - Markatou, Marianthi

AU - Talal, Andrew

AU - Good, Jeff

AU - Mukhopadhyay, Raktim

PY - 2024/6/18

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N2 - 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.

AB - 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.

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Pokharel P, Lee J, Kennedy O , Markatou M , Talal A , Good J et al. Drag, Drop, Merge: A Tool for Streamlining Integration of Longitudinal Survey Instruments. In HILDA 2024 - Workshop on Human-In-the-Loop Data Analytics Co-located with SIGMOD 2024. Association for Computing Machinery, Inc. 2024. (HILDA 2024 - Workshop on Human-In-the-Loop Data Analytics Co-located with SIGMOD 2024). doi: 10.1145/3665939.3665965

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

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