RES.LL-005 D4M: Signal Processing on Databases, Fall 2012
Author(s)
Kepner, Jeremy
Alternative Title
D4M: Signal Processing on Databases
Date Issued
December 2012
Abstract
D4M is a breakthrough in computer programming that combines graph theory, linear algebra, and databases to address problems associated with Big Data. Search, social media, ad placement, mapping, tracking, spam filtering, fraud detection, wireless communication, drug discovery, and bioinformatics all attempt to find items of interest in vast quantities of data. This course teaches a signal processing approach to these problems by combining linear algebraic graph algorithms, group theory, and database design. This approach has been implemented in software The class will begin with a number of practical problems, introduce the appropriate theory and then apply the theory to these problems. Students will apply these ideas in the final project of their choosing. The course will contain a number of smaller assignments which will prepare the students with appropriate software infrastructure for completing their final projects.
Subjects
big data
data analytics
dynamic distributed dimensional data model
D4M
associate arrays
group theory
entity analysis
perfect Power Law
bio sequence correlation
Accumulo
Kronecker graphs
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