An exploratory approach to data driven knowledge creation
Abstract This paper describes a new approach to knowledge creation that is instrumental for the emerging paradigm of data-intensive science. The proposed approach enables the acquisition of new insights from the data by exploiting existing relationships between diverse types of datasets acquired thr...
| Main Authors: | , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
SpringerOpen
2023-03-01
|
| Series: | Journal of Big Data |
| Subjects: | |
| Online Access: | https://doi.org/10.1186/s40537-023-00702-x |
| _version_ | 1797864427663392768 |
|---|---|
| author | Costantino Thanos Carlo Meghini Valentina Bartalesi Gianpaolo Coro |
| author_facet | Costantino Thanos Carlo Meghini Valentina Bartalesi Gianpaolo Coro |
| author_sort | Costantino Thanos |
| collection | DOAJ |
| description | Abstract This paper describes a new approach to knowledge creation that is instrumental for the emerging paradigm of data-intensive science. The proposed approach enables the acquisition of new insights from the data by exploiting existing relationships between diverse types of datasets acquired through various modalities. The value of data consistently improves when it can be linked to other data because linking multiple types of datasets allows creating novel data patterns within a scientific data space. These patterns enable the exploratory data analysis, an analysis strategy that emphasizes incremental and adaptive access to the datasets constituting a scientific data space while maintaining an open mind to alternative possibilities of data interconnectivity. A technology, the Linked Open data (LOD), was developed to enable the linking of datasets. We argue that the LOD technology presents several limitations that prevent the full exploitation of this technology to acquire new insights. In this paper, we outline a new approach that enables researchers to dynamically create data patterns in a research data space by exploiting explicit and implicit/hidden relationships between distributed research datasets. This dynamic creation of data patterns enables the exploratory data analysis strategy. |
| first_indexed | 2024-04-09T22:52:51Z |
| format | Article |
| id | doaj.art-6874a5db571e4097baaf3a09ea3ba980 |
| institution | Directory Open Access Journal |
| issn | 2196-1115 |
| language | English |
| last_indexed | 2024-04-09T22:52:51Z |
| publishDate | 2023-03-01 |
| publisher | SpringerOpen |
| record_format | Article |
| series | Journal of Big Data |
| spelling | doaj.art-6874a5db571e4097baaf3a09ea3ba9802023-03-22T11:34:02ZengSpringerOpenJournal of Big Data2196-11152023-03-0110111510.1186/s40537-023-00702-xAn exploratory approach to data driven knowledge creationCostantino Thanos0Carlo Meghini1Valentina Bartalesi2Gianpaolo Coro3Istituto di Scienza e Tecnologie dell’Informazione “A. Faedo” (ISTI), CNRIstituto di Scienza e Tecnologie dell’Informazione “A. Faedo” (ISTI), CNRIstituto di Scienza e Tecnologie dell’Informazione “A. Faedo” (ISTI), CNRIstituto di Scienza e Tecnologie dell’Informazione “A. Faedo” (ISTI), CNRAbstract This paper describes a new approach to knowledge creation that is instrumental for the emerging paradigm of data-intensive science. The proposed approach enables the acquisition of new insights from the data by exploiting existing relationships between diverse types of datasets acquired through various modalities. The value of data consistently improves when it can be linked to other data because linking multiple types of datasets allows creating novel data patterns within a scientific data space. These patterns enable the exploratory data analysis, an analysis strategy that emphasizes incremental and adaptive access to the datasets constituting a scientific data space while maintaining an open mind to alternative possibilities of data interconnectivity. A technology, the Linked Open data (LOD), was developed to enable the linking of datasets. We argue that the LOD technology presents several limitations that prevent the full exploitation of this technology to acquire new insights. In this paper, we outline a new approach that enables researchers to dynamically create data patterns in a research data space by exploiting explicit and implicit/hidden relationships between distributed research datasets. This dynamic creation of data patterns enables the exploratory data analysis strategy.https://doi.org/10.1186/s40537-023-00702-xData explorationData relationshipsData patternsData analyzerData publication |
| spellingShingle | Costantino Thanos Carlo Meghini Valentina Bartalesi Gianpaolo Coro An exploratory approach to data driven knowledge creation Journal of Big Data Data exploration Data relationships Data patterns Data analyzer Data publication |
| title | An exploratory approach to data driven knowledge creation |
| title_full | An exploratory approach to data driven knowledge creation |
| title_fullStr | An exploratory approach to data driven knowledge creation |
| title_full_unstemmed | An exploratory approach to data driven knowledge creation |
| title_short | An exploratory approach to data driven knowledge creation |
| title_sort | exploratory approach to data driven knowledge creation |
| topic | Data exploration Data relationships Data patterns Data analyzer Data publication |
| url | https://doi.org/10.1186/s40537-023-00702-x |
| work_keys_str_mv | AT costantinothanos anexploratoryapproachtodatadrivenknowledgecreation AT carlomeghini anexploratoryapproachtodatadrivenknowledgecreation AT valentinabartalesi anexploratoryapproachtodatadrivenknowledgecreation AT gianpaolocoro anexploratoryapproachtodatadrivenknowledgecreation AT costantinothanos exploratoryapproachtodatadrivenknowledgecreation AT carlomeghini exploratoryapproachtodatadrivenknowledgecreation AT valentinabartalesi exploratoryapproachtodatadrivenknowledgecreation AT gianpaolocoro exploratoryapproachtodatadrivenknowledgecreation |