DataFuse: Optimizing Interview Analytics Through Multimodal Data Integration and Real-Time Visualization

Interview analytics plays a vital role in the recruitment process, serving as a framework to evaluate candidate performance and engagement. Traditional methods often rely on limited data sources, leading to suboptimal insights. To address this challenge, we introduce DataFuse, a groundbreaking framework that optimizes interview analytics through the integration of multimodal data sources, including video, audio, and text. DataFuse converts these various data types into a unified analytic platform. The system not only extracts key performance indicators but also provides an interactive visualization interface tailored for users. Its modular architecture ensures adaptability to different interview formats and allows for the integration of additional data sources. Moreover, AI-driven analytics enable the generation of actionable recommendations, significantly improving the evaluation process for both interviewers and organizations.

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Publication Details

Published
2025-07-11
DOI
https://doi.org/10.1109/cisat66811.2025.11181704
Primary Topic
Data Visualization and Analytics
Type
article
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article

DataFuse: Optimizing Interview Analytics Through Multimodal Data Integration and Real-Time Visualization

Minhui Xie, B. Liu
Data Visualization and Analytics
article

DataFuse: Optimizing Interview Analytics Through Multimodal Data Integration and Real-Time Visualization

Minhui Xie, B. Liu
article en

Abstract

Interview analytics plays a vital role in the recruitment process, serving as a framework to evaluate candidate performance and engagement. Traditional methods often rely on limited data sources, leading to suboptimal insights. To address this challenge, we introduce DataFuse, a groundbreaking framework that optimizes interview analytics through the integration of multimodal data sources, including video, audio, and text. DataFuse converts these various data types into a unified analytic platform. The system not only extracts key performance indicators but also provides an interactive visualization interface tailored for users. Its modular architecture ensures adaptability to different interview formats and allows for the integration of additional data sources. Moreover, AI-driven analytics enable the generation of actionable recommendations, significantly improving the evaluation process for both interviewers and organizations.

Trine University (US), Carnegie Mellon University (US)
Openalex Percentile: Top 73%
Data Visualization and Analytics
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DataFuse: Optimizing Interview Analytics Through Multimodal Data Integration and Real-Time Visualization — Minhui Xie, B. Liu · (2025) | TGRS Research Map | TGRS