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Authors: Dongyun Nie and Mark Roantree

Affiliation: Insight Centre for Data Analytics, School of Computing, Dublin City University and Ireland

Keyword(s): Record Linkage, Relationships, Customer Knowledge.

Related Ontology Subjects/Areas/Topics: Data Warehouses and OLAP ; Databases and Information Systems Integration ; Enterprise Information Systems ; Legacy Systems ; Organisational Issues on Systems Integration

Abstract: Application areas such as healthcare and insurance see many patients or clients with their lifetime record spread across the databases of different providers. Record linkage is the task where algorithms are used to identify the same individual contained in different datasets. In cases where unique identifiers are found, linking those records is a trivial task. However, there are very high numbers of individuals who cannot be matched as common identifiers do not exist across datasets and their identifying information is not exact or often, quite different (e.g. a change of address). In this research, we provide a new approach to record linkage which also includes the ability to detect relationships between customers (e.g. family). A validation is presented which highlights the best parameter and configuration settings for the types of relationship links that are required.

CC BY-NC-ND 4.0

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Paper citation in several formats:
Nie, D. and Roantree, M. (2019). Detecting Multi-Relationship Links in Sparse Datasets. In Proceedings of the 21st International Conference on Enterprise Information Systems - Volume 1: ICEIS; ISBN 978-989-758-372-8; ISSN 2184-4984, SciTePress, pages 149-157. DOI: 10.5220/0007696901490157

@conference{iceis19,
author={Dongyun Nie. and Mark Roantree.},
title={Detecting Multi-Relationship Links in Sparse Datasets},
booktitle={Proceedings of the 21st International Conference on Enterprise Information Systems - Volume 1: ICEIS},
year={2019},
pages={149-157},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0007696901490157},
isbn={978-989-758-372-8},
issn={2184-4984},
}

TY - CONF

JO - Proceedings of the 21st International Conference on Enterprise Information Systems - Volume 1: ICEIS
TI - Detecting Multi-Relationship Links in Sparse Datasets
SN - 978-989-758-372-8
IS - 2184-4984
AU - Nie, D.
AU - Roantree, M.
PY - 2019
SP - 149
EP - 157
DO - 10.5220/0007696901490157
PB - SciTePress

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