Multi-Sensor Data and Knowledge Fusion -- A Proposal for a Terminology Definition
This work addresses a terminology gap for researchers and practitioners in data mining and machine learning, but it is incremental as it builds on existing literature without introducing new methods.
The paper tackles the problem of inconsistent terminology in multi-sensor data and knowledge fusion by proposing clear definitions and an ontology for fusion components and levels, and it presents common fusion techniques as part of the solution.
Fusion is a common tool for the analysis and utilization of available datasets and so an essential part of data mining and machine learning processes. However, a clear definition of the type of fusion is not always provided due to inconsistent literature. In the following, the process of fusion is defined depending on the fusion components and the abstraction level on which the fusion occurs. The focus in the first part of the paper at hand is on the clear definition of the terminology and the development of an appropriate ontology of the fusion components and the fusion level. In the second part, common fusion techniques are presented.