Big Data

Research Goals and Vision

The Big Data research at the KAIST School of Electrical Engineering aims to provide innovative and practical solutions through the collection, storage, analysis, and utilization of large-scale data. This research covers database management, data mining, data-centric AI, responsible data management, AI for graphs and time series, anomaly detection, and interpretable decision making.

The department’s innovative research in database management, data mining, data-centric AI, responsible data management, AI for graphs and time series, anomaly detection, and interpretable decision making is applied across various industries, including IT, manufacturing, finance, and healthcare.

Key Research Areas

Database

Data Storage and Management : Designing and optimizing database systems for efficient storage and management of large-scale data.
Query Processing and Optimization : Developing techniques for fast and accurate data retrieval through query processing and optimization.

관련교수

FACULTIES

Whang, Steven Euijong

Data Mining

Pattern Discovery : Researching algorithms to discover meaningful patterns and trends within large datasets.
Association Analysis : Analyzing relationships between data items to provide business insights.

관련교수

FACULTIES

Data-Centric AI

AI Model Development : Developing AI models centered around data to perform prediction and classification tasks.
Improving Data Quality : Researching methods to evaluate and improve data quality to enhance AI model performance.

관련교수

FACULTIES

Responsible Data Management

Trustworthy AI : Researching responsible data management methods that improve fairness, robustness, privacy, and explainability of AI.
AI Ethics : Researching data management methods that enable AI to be ethical.

관련교수

FACULTIES

AI for Graphs and Time Series

Graph Data Analysis : Developing AI algorithms for the analysis and interpretation of graph-structured data.
Time Series Data Analysis : Researching AI techniques for pattern recognition and prediction in time series data.

관련교수

FACULTIES

Anomaly Detection

Identifying Anomalies : Developing algorithms to identify abnormal data that deviates from normal patterns.
Real-Time Monitoring : Researching systems to detect anomalies in real-time data streams.

관련교수

FACULTIES

Interpretable Decision Making

Explainable AI : Developing AI systems that can be understood and explained by humans in the decision-making process.
Transparent Modeling : Researching interpretable modeling techniques to make data-driven decisions more transparent.

관련교수

FACULTIES

Recent related activities in Big Data

See below for specifc ongoing research topics related to Big Data of KAIST EE.

Research

Research Areas

Big Data

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About Us

Research

EE-X

AI in EE

People & Life

Academics

External Relations

Admissions

News & Event