Uncertain spatiotemporal data management for the Semantic Web:
In the world of data management, one of the most formidable challenges faced by academic scholars is the effective handling of spatiotemporal data within the semantic web. As our world continues to change dynamically with time, nearly every aspect of our lives, from environmental monitoring to urban...
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| Other Authors: | , |
|---|---|
| Format: | Electronic eBook |
| Language: | English |
| Published: |
Hershey, Pennsylvania
IGI Global
2024
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| Subjects: | |
| Online Access: | DE-91 DE-898 DE-1050 Volltext |
| Summary: | In the world of data management, one of the most formidable challenges faced by academic scholars is the effective handling of spatiotemporal data within the semantic web. As our world continues to change dynamically with time, nearly every aspect of our lives, from environmental monitoring to urban planning and beyond, is intrinsically linked to time and space. This synergy has given rise to an avalanche of spatiotemporal data, and the pressing question is how to manage, model, and query this voluminous information effectively. The existing approaches often fall short in addressing the intricacies and uncertainties that come with spatiotemporal data, leaving scholars struggling to unlock its full potential.Uncertain Spatiotemporal Data Management for the Semantic Web is the definitive solution to the challenges faced by academic scholars in the realm of spatiotemporal data. This book offers a visionary approach to an all-encompassing guide in modeling and querying spatiotemporal data using innovative technologies like XML and RDF. Through a meticulously crafted set of chapters, this book sheds light on the nuances of spatiotemporal data and also provides practical solutions that empower scholars to navigate the complexities of this domain effectively. |
| Physical Description: | 1 Online-Ressource (518 Seiten) |
| ISBN: | 9781668491096 |
| DOI: | 10.4018/978-1-6684-9108-9 |
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| 520 | |a In the world of data management, one of the most formidable challenges faced by academic scholars is the effective handling of spatiotemporal data within the semantic web. As our world continues to change dynamically with time, nearly every aspect of our lives, from environmental monitoring to urban planning and beyond, is intrinsically linked to time and space. This synergy has given rise to an avalanche of spatiotemporal data, and the pressing question is how to manage, model, and query this voluminous information effectively. The existing approaches often fall short in addressing the intricacies and uncertainties that come with spatiotemporal data, leaving scholars struggling to unlock its full potential.Uncertain Spatiotemporal Data Management for the Semantic Web is the definitive solution to the challenges faced by academic scholars in the realm of spatiotemporal data. This book offers a visionary approach to an all-encompassing guide in modeling and querying spatiotemporal data using innovative technologies like XML and RDF. Through a meticulously crafted set of chapters, this book sheds light on the nuances of spatiotemporal data and also provides practical solutions that empower scholars to navigate the complexities of this domain effectively. | ||
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| spelling | Uncertain spatiotemporal data management for the Semantic Web Luyi Bai, Lin Zhu, editors Hershey, Pennsylvania IGI Global 2024 1 Online-Ressource (518 Seiten) txt rdacontent c rdamedia cr rdacarrier In the world of data management, one of the most formidable challenges faced by academic scholars is the effective handling of spatiotemporal data within the semantic web. As our world continues to change dynamically with time, nearly every aspect of our lives, from environmental monitoring to urban planning and beyond, is intrinsically linked to time and space. This synergy has given rise to an avalanche of spatiotemporal data, and the pressing question is how to manage, model, and query this voluminous information effectively. The existing approaches often fall short in addressing the intricacies and uncertainties that come with spatiotemporal data, leaving scholars struggling to unlock its full potential.Uncertain Spatiotemporal Data Management for the Semantic Web is the definitive solution to the challenges faced by academic scholars in the realm of spatiotemporal data. This book offers a visionary approach to an all-encompassing guide in modeling and querying spatiotemporal data using innovative technologies like XML and RDF. Through a meticulously crafted set of chapters, this book sheds light on the nuances of spatiotemporal data and also provides practical solutions that empower scholars to navigate the complexities of this domain effectively. Data structures Database management Linked data Semantic Web Bai, Luyi edt Zhu, Lin edt Erscheint auch als Druck-Ausgabe 1668491087 Erscheint auch als Druck-Ausgabe 9781668491089 https://doi.org/10.4018/978-1-6684-9108-9 Verlag URL des Erstveröffentlichers Volltext |
| spellingShingle | Uncertain spatiotemporal data management for the Semantic Web Data structures Database management Linked data Semantic Web |
| title | Uncertain spatiotemporal data management for the Semantic Web |
| title_auth | Uncertain spatiotemporal data management for the Semantic Web |
| title_exact_search | Uncertain spatiotemporal data management for the Semantic Web |
| title_exact_search_txtP | Uncertain spatiotemporal data management for the Semantic Web |
| title_full | Uncertain spatiotemporal data management for the Semantic Web Luyi Bai, Lin Zhu, editors |
| title_fullStr | Uncertain spatiotemporal data management for the Semantic Web Luyi Bai, Lin Zhu, editors |
| title_full_unstemmed | Uncertain spatiotemporal data management for the Semantic Web Luyi Bai, Lin Zhu, editors |
| title_short | Uncertain spatiotemporal data management for the Semantic Web |
| title_sort | uncertain spatiotemporal data management for the semantic web |
| topic | Data structures Database management Linked data Semantic Web |
| topic_facet | Data structures Database management Linked data Semantic Web |
| url | https://doi.org/10.4018/978-1-6684-9108-9 |
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