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abcOD: Mining Band Order Dependencies

Li, Pei; Jessica, Jessica; Tania, naida; Böhlen, Michael; Srivastava, Divesh; Szlichta, Jaroslaw (2022). abcOD: Mining Band Order Dependencies. In: 38th IEEE International Conference on Data Engineering, ICDE 2022, Kuala Lumpur, Malaysia, 9 May 2022 - 12 May 2022. IEEE, 3162-3165.

Abstract

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.

Additional indexing

Item Type:Conference or Workshop Item (Paper), refereed, original work
Communities & Collections:03 Faculty of Economics > Department of Informatics
Dewey Decimal Classification:000 Computer science, knowledge & systems
Scopus Subject Areas:Physical Sciences > Software
Physical Sciences > Signal Processing
Physical Sciences > Information Systems
Scope:Discipline-based scholarship (basic research)
Language:English
Event End Date:12 May 2022
Deposited On:03 Feb 2023 13:34
Last Modified:06 Mar 2024 14:38
Publisher:IEEE
OA Status:Green
Publisher DOI:https://doi.org/10.1109/ICDE53745.2022.00288
Other Identification Number:merlin-id:23073
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