Multi-Scale Data Integration Challenges in the Observational Science Data Space
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Laure Berti-Équille
Abstract
In Europe, more than one thousand of laboratories intensively collect data to measure various properties of the Earth. Scientists observe environmental conditions, ecosystems and biological species. The ability to understand complex phenomena (e. g., global warming) and predict trends from spatio-temporal data becomes a major issue in observational science. However, theoretical and technical advances in multi-scale data integration are necessary to achieve this. This paper will present some challenging research directions for integrating such massive multi-scale scientific data.
Zusammenfassung
In Europa sammeln mehr als tausend Laboratorien Daten, um verschiedene Eigenschaften der Erde zu messen. Die Fähigkeit, komplexe Phänomene (z. B. die globale Erwärmung) zu verstehen und Trends aus räumlich-zeitlichen Daten zu prognostizieren ist ein wichtiges Thema in der beobachtenden Wissenschaft. Allerdings sind theoretische und technische Fortschritte in der Integration von Daten unterschiedlichster Größenordnung (Multi-Scale Data Integration) notwendig, um dies zu erreichen. Dieser Beitrag stellt einige Forschungsherausforderungen in diesem Kontext vor.
© by Oldenbourg Wissenschaftsverlag, Aix-en-Provence cedex 04, Germany
Artikel in diesem Heft
- Datenintegration
- Schema Mapping and Data Exchange Tools: Time for the Golden Age
- Industrializing Data Integration Projects using a Metadata Driven Assembly Line
- Multi-Scale Data Integration Challenges in the Observational Science Data Space
- Frontiers in Crowdsourced Data Integration
- Managing Uncertainty: The Road Towards Better Data Interoperability
- Source-Path-Goal: Investigating the Cross-Linguistic Potential of Frame-Semantic Text Analysis
Artikel in diesem Heft
- Datenintegration
- Schema Mapping and Data Exchange Tools: Time for the Golden Age
- Industrializing Data Integration Projects using a Metadata Driven Assembly Line
- Multi-Scale Data Integration Challenges in the Observational Science Data Space
- Frontiers in Crowdsourced Data Integration
- Managing Uncertainty: The Road Towards Better Data Interoperability
- Source-Path-Goal: Investigating the Cross-Linguistic Potential of Frame-Semantic Text Analysis
