Databases

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

  • Data modeling
  • Metadata management
  • Design of databases and applications
  • Integration of heterogeneous information
  • Data distribution
  • Data management performance

Our group’s most recent and active research includes projects in:

  • Data management in mobile and location-based services
  • Sensor and stream-based data management
  • Decision-support systems for patient treatment in clinical settings
  • Analysis of healthcare clinical and claim data
  • 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 computing systems 
  • Internet data management, Web computing, and big data systems and analytics

Our group has a proud history; 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, University of Texas-Dallas, LSU, and University of Illinois at Chicago, to name a few. Our alumni also populate research groups at Microsoft, LLNL, NCR, SAS, Rockwell Collins, Ask.com, Orbitz, 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.

Faculty

  • Ling Liu: Data management 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; data privacy; and security and trust management.
  • 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; efficient implementation of transaction-time databases; data mining; clustering and outlier detection.
  • Sham Navathe: Modeling, distribution, and schema integration for enterprise databases and data warehouses; decision support for healthcare in clinical applications; healthcare data analytics; 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: Shamkant B. Navathe