Research Area – Databases

Our database group is known for work in all aspects of data management in many scientific, commercial and healthcare applications.

  • Modeling
  • Metadata management
  • Design
  • Integration
  • Distribution
  • Performance

Recent research includes work in:

  • Data management in mobile and location-based services
  • Sensor and stream-based data management
  • Modeling of bioinformatics and climate data
  • Privacy-preserving data mining
  • Outlier detection and analysis
  • Text mining of biomedical literature
  • Workflow and business process management
  • Database performance analysis in distributed, parallel and cloud-based settings.

Databases was the first Georgia Tech computing research area to be ranked by U.S. News & World Report in the early 1990s. We are particularly known for our database-centric research and expertise at the interfaces of systems, software engineering, machine learning, security and GIS.

Our Ph.D. graduates have taken faculty positions at major universities including Rice, Texas A&M and UT-Dallas, to name a few. Our alumni are also at research groups at Microsoft, LLNL, NCR, SAS, Rockwell Collins,, IBM Brazil and others.

We enjoy close research relationships with IBM Almaden, IBM T.J. Watson Center, HP labs, ATT and Wipro Technologies, and our major funders include NSF, NIH, CDC, IBM, Fujitsu Labs, Wipro Technologies and Samsung. Our faculty include:

Ling Liu: Data managements in distributed and networked computing systems, data analytics in cloud and virtualized environments, mobile data management, sensor and stream data management, privacy preserving data mining, business process mining, workflow management, healthcare information networks and services.

Leo Mark: Data modeling and application development. Metadata management for science data, climate models, communication protocols, archival, data compliance, database and software generators. Temporal databases, the efficient implementation of transaction-time. Data mining, clustering and outlier detection.

Sham Navathe: Modeling, distribution, and schema integration for enterprise databases and data warehouses; text mining of biomedical literature; biomedical data analysis and design; mobile and scalable database applications; data mining, data quality, and data privacy; data management for architecture, engineering design and GIS.

Edward Omiecinski: Data mining, database implementation and performance issues, database as a service, databases in the cloud, parallel database systems, database security.

Calton Pu: Automated management of N-tier applications in data centers; autonomic management of database servers; quality of information and denial of information; collaborative editing; cloud computing; service computing.

Coordinator: Sham Navathe