Gautam B. Singh, Ph.D. Associate Professor Computer Science and Engineering Department 146 DHE; (248) 370-2129 ; Fax: (248) 370-4625 singh@oakland.edu secs.oakland.edu/~singh Ph.D., Wayne State University, 1993

• Joined Oakland University in 1998
• Recognition: INTERNATIONAL WHO’s WHO in Information Technology, 1997-98.
• Member: ACM, IEEE Computer Society, and AAAS (American Association for Advancement of Science).

RESEARCH

• Information System Modeling, Management and Efficient Data Retrieval
• Temporal Database Design for Applications in Manufacturing and Process Monitoring
• Data Mining, Predictive Modeling and Forecasting
• Applications of Intelligent and Adaptive Algorithms for Discovering Data Patterns

1. Information System Modeling, Management and Efficient Data Retrieval. This research is focused at correlating the application level data-modeling requirements to the data-representation power of the database system architectures. Complex applications in engineering, financial, and biological systems are being studied with their systemic implementations using the relational, object-relational, object-oriented, and deductive database models. The challenge faced in efficient evaluation of complex content-based queries constitutes a significant component of this research.
   
   
2. Temporal Database Design for Applications in Manufacturing and Process Monitoring. Manufacturing applications involve access to time-constrained and temporally valid data. Such applications range from automatically tracking and directing objects on a factory floor to recording and analyzing process related data with the objective of removing anomalies and building reliable systems. Generalized schemas developed to satisfy temporal integrity constraints and meet the application’s transaction processing deadlines represent the issues addressed by this research.
   
   
3. Data Mining, Predictive Modeling and Forecasting. Very Large Databases (VLDB) maintain detailed logs of transactions and represent a significant source of information for utilizing the power of data-mining and predictive modeling techniques. Our research focus in this area has been to use both the deterministic and stochastic techniques to learn model parameters from the available data. Time series methods represent a useful modeling paradigm for forecasting in temporal databases. These capabilities are quite useful for building failure prediction models for meeting the reliability constraints of engineering systems.
   
   
4. Applications of Intelligent and Adaptive Algorithms for Discovering Data Patterns Adaptive systems based on learning principles are reaching a higher level of maturity and represent a useful tool for discovering new patterns and associations from the information in a database. Such a discovery of data inter-relations derived using conceptual clustering; harmony theory and competitive learning can be subsequently utilized for developing systemic data models.