Model-Driven Analytics: Connecting Data, Domain Knowledge, and Learning
April 05, 2017 Β· Declared Dead Β· π arXiv.org
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Authors
Thomas Hartmann, Assaad Moawad, Francois Fouquet, Gregory Nain, Jacques Klein, Yves Le Traon, Jean-Marc Jezequel
arXiv ID
1704.01320
Category
cs.SE: Software Engineering
Citations
10
Venue
arXiv.org
Last Checked
4 months ago
Abstract
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.
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