Adaptive Relation Modeling Framework for Structured Data Learning

ARMOR is a lightweight framework tailored for structured data (or relational data, tabular data) analytics.

Most businesses to date rely on structured data for their data storage and predictive analytics. Extracting insights from structured data requires advanced analytics.

ARMOR is mainly based on more effective and interpretable ARM-Net, an adaptive relation modeling network tailored for structured data.

View our ARM-Net publication in the ACM Special Interest Group on Management of Data (SIGMOD) 2021:

ARM-Net: Adaptive Relation Modeling Network for Structured Data

Open Source

Research Highlights

Example Applications

Extracting insights from structured data requires advanced analytics. The major challenge for the predictive modeling of structured data is how to model dependencies and correlations among features.

ARMOR Overview

ARMOR focuses on relation modeling for structured data with state-of-the-art techniques. This framework is mainly based on more effective and interpretable ARM-Net.

People

Professors

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OOI Beng Chin

Lee Kong Chian Centennial Professor, Computer Science, School of Computing

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GANG Chen

Professor, Dean of computer college, Zhejiang University

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H. V. Jagadish

Bernard A Galler Collegiate Professor of Elec. Engg. and Computer Science. University of Michigan

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ZHANG Meihui

Professor, School of Computer Science & Technology, Beijing Institute of Technology

Researchers

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CAI Shaofeng

PhD student in the Database System Research Group at NUS

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ZHENG Kaiping

Research Fellow in the Database System Research Group at NUS

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