Metadata-Based Clustering and Selection of Metadata Items for Similar Dataset Discovery and Data Combination Tasks
Data integration, which aims to solve problems and create new services by combining datasets, has attracted considerable attention. The discovery of similar datasets that can be combined is critical. In the literature on similar dataset discovery, it is important to select an appropriate discovery m...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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IEEE
2024-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/10464313/ |
| _version_ | 1797243275706368000 |
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| author | Takeshi Sakumoto Teruaki Hayashi Hiroki Sakaji Hirofumi Nonaka |
| author_facet | Takeshi Sakumoto Teruaki Hayashi Hiroki Sakaji Hirofumi Nonaka |
| author_sort | Takeshi Sakumoto |
| collection | DOAJ |
| description | Data integration, which aims to solve problems and create new services by combining datasets, has attracted considerable attention. The discovery of similar datasets that can be combined is critical. In the literature on similar dataset discovery, it is important to select an appropriate discovery method for each information need, such as the domain. However, conventional studies have evaluated discovery methods in different ways, such as domains, test datasets, and evaluation metrics. This factor prevents the appropriate method selection for each situation. Furthermore, the specific effects of the combination of different methods are not well known despite conventional studies arguing the importance of the combination. This study attempts to understand (1) the similarity indicators that should be employed for each domain and (2) the effects of a combination of different indicators on performance. We evaluated 16 inter-dataset clustering models based on different metadata-based similarity indicators, using unified evaluation metrics and datasets for 15 domains. Our results (1) suggest that similarity indicators should be used for each domain and (2) demonstrate that most of the combinations of different methods can improve clustering performance. |
| first_indexed | 2024-04-24T18:52:32Z |
| format | Article |
| id | doaj.art-35ad149ab17f48fbb1733fdb323e33ec |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-04-24T18:52:32Z |
| publishDate | 2024-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-35ad149ab17f48fbb1733fdb323e33ec2024-03-26T17:48:20ZengIEEEIEEE Access2169-35362024-01-0112402134022410.1109/ACCESS.2024.337575010464313Metadata-Based Clustering and Selection of Metadata Items for Similar Dataset Discovery and Data Combination TasksTakeshi Sakumoto0https://orcid.org/0000-0002-7589-3283Teruaki Hayashi1https://orcid.org/0000-0002-1806-5852Hiroki Sakaji2Hirofumi Nonaka3Department of Engineering, Nagaoka University of Technology, Nagaoka, Niigata, JapanDepartment of Engineering, The University of Tokyo, Bunkyo, Tokyo, JapanFaculty of Information Science and Technology, Hokkaido University, Sapporo, Hokkaido, JapanFaculty of Business Administration, Aichi Institute of Technology, Toyota, Aichi, JapanData integration, which aims to solve problems and create new services by combining datasets, has attracted considerable attention. The discovery of similar datasets that can be combined is critical. In the literature on similar dataset discovery, it is important to select an appropriate discovery method for each information need, such as the domain. However, conventional studies have evaluated discovery methods in different ways, such as domains, test datasets, and evaluation metrics. This factor prevents the appropriate method selection for each situation. Furthermore, the specific effects of the combination of different methods are not well known despite conventional studies arguing the importance of the combination. This study attempts to understand (1) the similarity indicators that should be employed for each domain and (2) the effects of a combination of different indicators on performance. We evaluated 16 inter-dataset clustering models based on different metadata-based similarity indicators, using unified evaluation metrics and datasets for 15 domains. Our results (1) suggest that similarity indicators should be used for each domain and (2) demonstrate that most of the combinations of different methods can improve clustering performance.https://ieeexplore.ieee.org/document/10464313/Dataset discoverydataset similarityclusteringdata exchange platformmetadata |
| spellingShingle | Takeshi Sakumoto Teruaki Hayashi Hiroki Sakaji Hirofumi Nonaka Metadata-Based Clustering and Selection of Metadata Items for Similar Dataset Discovery and Data Combination Tasks IEEE Access Dataset discovery dataset similarity clustering data exchange platform metadata |
| title | Metadata-Based Clustering and Selection of Metadata Items for Similar Dataset Discovery and Data Combination Tasks |
| title_full | Metadata-Based Clustering and Selection of Metadata Items for Similar Dataset Discovery and Data Combination Tasks |
| title_fullStr | Metadata-Based Clustering and Selection of Metadata Items for Similar Dataset Discovery and Data Combination Tasks |
| title_full_unstemmed | Metadata-Based Clustering and Selection of Metadata Items for Similar Dataset Discovery and Data Combination Tasks |
| title_short | Metadata-Based Clustering and Selection of Metadata Items for Similar Dataset Discovery and Data Combination Tasks |
| title_sort | metadata based clustering and selection of metadata items for similar dataset discovery and data combination tasks |
| topic | Dataset discovery dataset similarity clustering data exchange platform metadata |
| url | https://ieeexplore.ieee.org/document/10464313/ |
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