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Contribution Details
| Type | Conference or Workshop Paper |
| Scope | Discipline-based scholarship |
| Published in Proceedings | Yes |
| Title | Discovering Band Order Dependencies |
| 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 | 1878 - 1881 |
| 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 | We introduce band ODs to model the semantics of attributes that are monotonically related with small variations without there being an intrinsic violation of semantics. To make band ODs relevant to real-world applications, we make them less strict to hold approximately with some exceptions. Since formulating integrity constraints manually is cumbersome, we study the problem of automatic approximate band OD discovery. We devise an algorithm that determines the optimal solution in polynomial time. We perform a thorough experimental evaluation of our techniques over real-world and synthetic datasets. |
| Digital Object Identifier | 10.1109/ICDE48307.2020.00193 |
| Other Identification Number | merlin-id:20729 |
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