CSE Profile: Mohamed Mokbel

Mohamed Mokbel
Assistant Professor

(612) 626-3025
Office: EE/CS 4-207

Interests

Databases, data stream management systems, spatio-temporal databases, scalable continuous query processing, spatial databases, indexing techniques, adaptive query optimization, and multimedia disk scheduling.

Education

PhD 2005, Computer Science, Purdue University.

MS 1999, BS 1996, computer Science, Faculty of Engineering, Alexandria University, Egypt.

Assistant Professor Mokbel specializes in database research, location privacy, sensor networks, indexing techniques, and query processing and optimization. Mokbel was nominated for the Council of Graduate Schools International Dissertation Award and the ACM SIGMOD Ph.D. Award in 2006. He was also awarded the Purdue Research Foundation Fellowship 2001-2003. Mokbel has served many committees and has co-authored numerous publications.

Research

My research is in the broad area of database systems. In particular, I am interested in data stream management systems, spatio-temporal databases, scalable continuous query processing, spatial databases, indexing techniques, and adaptive query optimization. My goal is to advance the state of the art in the design and implementation of database engines to cope with the requirements of emerging applications.

My current research focuses on leveraging database management systems to efficiently support large numbers of concurrent continuous queries. Unlike traditional queries, continuous queries require constant evaluation of the result as the query conditions or database contents change. Continuous queries are dominant in applications such as network monitoring, stock tickers, online transaction flow analysis, location-aware services, and sensor networks. In particular, I am interested in location-aware applications where virtually all objects of interest can determine their locations. In such applications, both queries and data have the ability to continuously change their locations and/or sizes over time. My ultimate goal is to provide location-aware query processor built into the database engine not layered on top.

In a typical location-aware application, multi-dimensional data is received from remote sources via network connections. Network traffic may be unpredictable, slow, or bursty which may result in blocking input data. This motivates the need for a family of adaptive non-blocking query operators for processing remote data retrieved via network connections. The main goal is to adapt the behavior of the query processing engine based on the fluctuations of the network traffic so that part of the processing can be done even if data sources are temporarily blocked.

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