Model-Driven Analytics: Connecting Data, Domain Knowledge, and Learning
This addresses the problem of extracting insights from high-complexity data in domains like IoT and healthcare, though it appears incremental as it builds on existing methods.
The paper tackles the challenge of analyzing complex domain data by proposing model-driven analytics, which refines raw data using a model that integrates domain knowledge and learning methods.
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.