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An intelligent linked data quality dashboard

Vaidyambath, Ramneesh, Debattista, Jeremy ORCID: 0000-0002-5592-8936, Srivatsa, Neha and Brennan, Rob ORCID: 0000-0001-8236-362X (2019) An intelligent linked data quality dashboard. In: AICS 27th AIAI Irish Conference on Artificial Intelligence and Cognitive Science., 5-6 Sept 2019, Galway, Ireland.

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Abstract

This paper describes a new intelligent, data-driven dashboard for linked data quality assessment. The development goal was to assist data quality engineers to interpret data quality problems found when evaluating a dataset us-ing a metrics-based data quality assessment. This required construction of a graph linking the problematic things identified in the data, the assessment metrics and the source data. This context and supporting user interfaces help the user to un-derstand data quality problems. An analysis widget also helped the user identify the root cause multiple problems. This supported the user in identification and prioritization of the problems that need to be fixed and to improve data quality. The dashboard was shown to be useful for users to clean data. A user evaluation was performed with both expert and novice data quality engineers.

Item Type:Conference or Workshop Item (Paper)
Event Type:Conference
Refereed:Yes
Uncontrolled Keywords:Linked Data; Data Quality Analysis; Root Cause Analysis
Subjects:UNSPECIFIED
DCU Faculties and Centres:DCU Faculties and Schools > Faculty of Engineering and Computing > School of Computing
Research Initiatives and Centres > ADAPT
Published in: Proceedings for the 27th AIAI Irish Conference on Artificial Intelligence and Cognitive Science. . CEUR-WS.
Publisher:CEUR-WS
Official URL:http://aics2019.datascienceinstitute.ie/papers/aics_32.pdf
Copyright Information:© 2019 The Authors
Use License:This item is licensed under a Creative Commons Attribution-NonCommercial-Share Alike 3.0 License. View License
Funders:Science Foundation Ireland (SFI) under Grant Number SFI/16/RC/3918 (Confirm), Science Foundation Ireland SFI/12/RC/2289 P2 (Insight) co-funded by the European Regional Development Fund
ID Code:24121
Deposited On:10 Jan 2020 13:06 by Vidatum Academic . Last Modified 10 Jan 2020 13:06

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