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Fall 2017 Semester
Sep 26, 2017
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CS 6240 - Large-Scale Parallel Data Processing
Covers big-data analysis techniques that scale out with increasing number of compute nodes, e.g., for cloud computing. Focuses on approaches for problem and data partitioning that distribute work effectively while keeping total cost for computation and data transfer low. Deterministic and random algorithms from a variety of domains, including graphs, data mining, linear algebra, and information retrieval, are studied and analyzed in terms of their cost, scalability, and robustness against skew. Coursework emphasizes hands-on programming experience with modern state-of-the-art big-data processing technology. Students who do not meet course prerequisites may seek permission of instructor.
4.000 Credit hours
4.000 Lecture hours

Levels: Graduate
Schedule Types: Lecture

Computer Science Department

Course Attributes:
GSCS Computer & Info Science

Restrictions:
Must be enrolled in one of the following Levels:     
      Graduate
Must be enrolled in one of the following Colleges:     
      Coll of Computer & Info Sci

Prerequisites:
Graduate level CS 5800 Minimum Grade of C- or Graduate level CS 7800 Minimum Grade of C-

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