Abstract
We demonstrate a system that enables a data-centric approach in understanding data quality. Instead of directly quantifying data quality as traditionally done, it disrupts the quality of the dataset and monitors the deviations in the output of an analytic task at hand. It computes the correlation factor between the disruption and the deviation and uses it as the quality metric. This allows users to understand not only the quality of their dataset but also the effect that present and future quality issues have to the intended analytic tasks. This is a novel data-centric approach aimed at complementing existing solutions. On top of the new information that it provides, and in contrast to existing techniques of data quality, it neither requires knowledge of the clean datasets, nor of the constraints on which the data should comply.
Original language | English |
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Title of host publication | 2021 IEEE 37th International Conference on Data Engineering (ICDE) |
Publisher | IEEE |
Pages | 2717-2720 |
Number of pages | 4 |
ISBN (Electronic) | 978-1-7281-9184-3 |
ISBN (Print) | 978-1-7281-9185-0 |
DOIs | |
Publication status | Published - 22 Jun 2021 |
Event | 37th IEEE International Conference on Data Engineering, ICDE 2021 - Virtual, Chania, Greece Duration: 19 Apr 2021 → 22 Apr 2021 |
Publication series
Name | Proceedings - International Conference on Data Engineering |
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Volume | 2021-April |
ISSN (Print) | 1084-4627 |
Conference
Conference | 37th IEEE International Conference on Data Engineering, ICDE 2021 |
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Country/Territory | Greece |
City | Virtual, Chania |
Period | 19/04/21 → 22/04/21 |
Bibliographical note
Publisher Copyright:© 2021 IEEE.
Keywords
- Data Cleaning
- Data Mining
- Data Profiling
- Data Quality