A model-driven architecture-based data quality management framework for the internet of Things

Abstract

The internet of Things (IoT) is a data stream environment where a large scale deployment of smart things continuously report readings. These data streams are then consumed by pervasive applications, i.e. data consumers, to offer ubiquitous services. The data quality (DQ) is a key criteria for IoT data consumers especially when considering the inherent uncertainty of sensor-enabled data. However, DQ is a highly subjective concept and there is no standard agreement of how to determine “good” data. Moreover, the combinations of considered measured attributes and associated DQ information are as diverse as the needs of data consumers. This introduces expensive overheads for data consumers that desire a specifically built system for managing their DQ information. To effectively handle these various perceptions of DQ, we propose a Model-Driven Architecture-based approach that allows the data consumer to easily and efficiently express, through models, his vision of DQ and its requirements using an easy-to-use graphical model editor. The defined DQ specifications are then automatically transformed to generate an entire infrastructure for DQ management that fits perfectly the data consumer's requirements. We demonstrate the flexibility and the efficiency of our approach through a real life data stream environment scenario.