| Autor: |
Hartmann, Thomas, Moawad, Assaad, Fouquet, Francois, Nain, Gregory, Klein, Jacques, Traon, Yves Le, Jezequel, Jean-Marc |
| Rok vydání: |
2017 |
| Předmět: |
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| Druh dokumentu: |
Working Paper |
| Popis: |
Gaining profound insights from collected data of today's application domains like IoT, cyber-physical systems, health care, or the financial sector is business-critical and can create the next multi-billion dollar market. However, analyzing these data and turning it into valuable insights is a huge challenge. This is often not alone due to the large volume of data but due to an incredibly high domain complexity, which makes it necessary to combine various extrapolation and prediction methods to understand the collected data. Model-driven analytics is a refinement process of raw data driven by a model reflecting deep domain understanding, connecting data, domain knowledge, and learning. |
| Databáze: |
arXiv |
| Externí odkaz: |
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