University at Buffalo - Fall 2015
Addressing the challenges of big data requires a combination of human intuition and automation. Rather than tackling these challenges head-on with build-from-scratch solutions, or through general- purpose database systems, developer and analyst communities are turning to building blocks: Specialized languages, runtimes, data-structures, services, compilers, and frameworks that simplify the task of creating a system that is efficient enough to handle terabytes of data or more, while still being manageable.
Coursework consists of lectures and a multi-stage final project. Students are expected to attend all lectures. Projects may be performed individually or in groups. Projects will be evaluated in three stages through code deliverables, reports, and group meetings with either or both of the instructors. During these meetings, instructors will question the entire group extensively about the group’s report, deliverables, and any related tools and technology. After the taking the course, students should be able to: • Design domain specific query languages, by first developing an understanding the com- mon tropes of a target domain, exploring ways of allowing users to efficiently express those tropes, and developing ways of mapping the resulting programs to an efficient evaluation strategy. • Identify concurrency challenges in data-intensive computing tasks, and address them through locking, code associativity, and correctness analysis. • Understand a variety of index data structures, as well as their application and use in data management systems for high velocity, volume, veracity, and/or variety data. • Understand query and program compilation techniques, including the design of in- termediate representations, subexpression equivalence, cost estimation, and the construction of target-representation code.
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