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Contribution Details
| Type | Conference or Workshop Paper |
| Scope | Discipline-based scholarship |
| Published in Proceedings | Yes |
| Title | Preserving Contextual Information in Relational Matrix Operations |
| Organization Unit | |
| Authors |
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| Presentation Type | paper |
| Item Subtype | Original Work |
| Refereed | Yes |
| Status | Published in final form |
| Language |
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| Page Range | 1894 - 1897 |
| Event Title | 36th IEEE International Conference on Data Engineering, ICDE 2020 |
| Event Type | conference |
| Event Location | Dallas, TX, USA |
| Event Start Date | April 20 - 2020 |
| Event End Date | April 24 - 2020 |
| Publisher | IEEE |
| Abstract Text | There exist large amounts of numerical data that are stored in databases and must be analyzed. Database tables come with a schema and include non-numerical attributes; this is crucial contextual information that is needed for interpreting the numerical values. We propose relational matrix operations that support the analysis of data stored in tables and that preserve contextual information. The result of our approach are precisely defined relational matrix operations and a system implementation in MonetDB that illustrates the seamless integration of relational matrix operations into a relational DBMS. |
| Digital Object Identifier | 10.1109/ICDE48307.2020.00197 |
| Other Identification Number | merlin-id:20728 |
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