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Courses - Graduate
The course covers advanced algorithms for learning, analysis, data management and visualization of large
datasets. Topics include indexing for text and DNA databases, searching medical and multimedia
databases by content, fundamental signal processing methods, compression, fractals in databases, data
mining, privacy and security issues, rule discovery and data visualization.
The course involves a project, whose goal is to give the
participants the opportunity to tackle a large, interesting problem, which may lead to a publication.
The goal of this course is to investigate
the traditional techniques and their interactions by studying several seminal papers and
survey studies in the area while building a complete, scaled-down, relational database
management system. An emphasis will be placed on a large,
semester-long DBMS implementation project. This intensive project will count for a
substantial portion of the grade.
Topics include buffer management, access path and indexes, query processing,
concurrency control, logging and crash recovery, benchmarking and data mining for
warehouses.
The course covers performance issues in today's database system design. Topics include query
processing and optimization, concurrency control, smart and efficient benchmarking, modern query
processing algorithms for internet applications, interaction between the database software and the
underlying hardware, and other topics related to database system performance.
The course involves a project, whose goal is to give the participants the opportunity to tackle a
large, interesting problem, which may lead to a publication.
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