RES.LL-005 D4M: Signal Processing on Databases, Fall 2012
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...
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| Format: | Learning Object |
| Language: | en-US |
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2020
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| Online Access: | https://hdl.handle.net/1721.1/126284 |
| _version_ | 1824458176115769344 |
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| author | Kepner, Jeremy |
| author_facet | Kepner, Jeremy |
| author_sort | Kepner, Jeremy |
| collection | MIT |
| description | 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. |
| first_indexed | 2024-09-23T12:52:02Z |
| format | Learning Object |
| id | mit-1721.1/126284 |
| institution | Massachusetts Institute of Technology |
| language | en-US |
| last_indexed | 2025-02-19T04:21:43Z |
| publishDate | 2020 |
| record_format | dspace |
| spelling | mit-1721.1/1262842025-02-12T15:55:58Z RES.LL-005 D4M: Signal Processing on Databases, Fall 2012 D4M: Signal Processing on Databases Kepner, Jeremy 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 140903 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. 2020-07-21T18:36:53Z 2020-07-21T18:36:53Z 2012-12 2020-07-21T18:37:10Z Learning Object RES.LL-005-Fall2012 RES.LL-005 IMSCP-MD5-796efd9c97dbf328be87fbe7cd9a203c https://hdl.handle.net/1721.1/126284 en-US This site (c) Massachusetts Institute of Technology 2020. Content within individual courses is (c) by the individual authors unless otherwise noted. The Massachusetts Institute of Technology is providing this Work (as defined below) under the terms of this Creative Commons public license ("CCPL" or "license") unless otherwise noted. The Work is protected by copyright and/or other applicable law. Any use of the work other than as authorized under this license is prohibited. By exercising any of the rights to the Work provided here, You (as defined below) accept and agree to be bound by the terms of this license. The Licensor, the Massachusetts Institute of Technology, grants You the rights contained here in consideration of Your acceptance of such terms and conditions. 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charset=utf-8 text/html image/png image/png image/jpeg image/png image/png image/png image/png image/png text/html text/html Fall 2012 |
| spellingShingle | 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 140903 Kepner, Jeremy RES.LL-005 D4M: Signal Processing on Databases, Fall 2012 |
| title | RES.LL-005 D4M: Signal Processing on Databases, Fall 2012 |
| title_full | RES.LL-005 D4M: Signal Processing on Databases, Fall 2012 |
| title_fullStr | RES.LL-005 D4M: Signal Processing on Databases, Fall 2012 |
| title_full_unstemmed | RES.LL-005 D4M: Signal Processing on Databases, Fall 2012 |
| title_short | RES.LL-005 D4M: Signal Processing on Databases, Fall 2012 |
| title_sort | res ll 005 d4m signal processing on databases fall 2012 |
| topic | 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 140903 |
| url | https://hdl.handle.net/1721.1/126284 |
| work_keys_str_mv | AT kepnerjeremy resll005d4msignalprocessingondatabasesfall2012 AT kepnerjeremy d4msignalprocessingondatabases |