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| Type | Conference or Workshop Paper |
| Scope | Discipline-based scholarship |
| Published in Proceedings | Yes |
| Title | abcOD: Mining 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 | 3162 - 3165 |
| Event Title | 38th IEEE International Conference on Data Engineering, ICDE 2022 |
| Event Type | conference |
| Event Location | Kuala Lumpur, Malaysia |
| Event Start Date | May 9 - 2022 |
| Event End Date | May 12 - 2022 |
| Publisher | IEEE |
| Abstract Text | We present the design of and a demonstration plan for abcOD, a tool for efficiently discovering approximate band conditional order dependencies (abcODs) from data. abcOD utilizes a dynamic programming algorithm based on a longest monotonic band. Using real datasets, we demonstrate how the discovered abcODs can help users understand ordered data semantics, identify potential data quality problems, and interactively clean the data. |
| Digital Object Identifier | 10.1109/ICDE53745.2022.00288 |
| Other Identification Number | merlin-id:23073 |
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