Labelling user data is a central part of the design and evaluation of pervasive systems that aim to support the user through situation-aware reasoning. It is essential both in designing and training the system to recognize and reason about the situation, either through the definition of a suitable situation model in knowledge-driven applications, or though the preparation of training data for learning tasks in data-driven models. Hence, the quality of annotations can have a significant impact on the performance of the derived systems.

Labelling is also vital for validating and quantifying the performance of applications.

With pervasive systems relying increasingly on large datasets for designing and testing models of users’ activities, the process of data labelling is becoming a major concern for the community. This also reflects the increasing need of intelligent interactive annotation tools, which can reduce the manual annotation effort and improve the annotation performance and quality in large datasets.