  Nowadays, data is becoming the new fuel for economic wealth and creation of
novel and profitable business models. Multitude of technologies are
contributing to an abundance of information sources which are already the
baseline for multi-millionaire services and applications. Internet of Things
(IoT), is probably the most representative one. However, for an economy of data
to actually flourish there are still several critical challenges that have to
be overcome. Among them, data quality can become an issue when data come from
heterogeneous sources or have different formats, standards and scale. Improving
data quality is of utmost importance for any domain since data are the basis
for any decision-making system and decisions will not be accurate if they are
based on inadequate low-quality data. In this paper we are presenting a
solution for assessing several quality dimensions of IoT data streams as they
are generated. Additionally, the solution described in the paper actually
improves the quality of data streams by curating them through the application
of Artificial Intelligence techniques. The approach followed in our work has
been to append data quality information as metadata linked to each individual
piece of curated data. We have leveraged linked-data principles and integrated
the developed AI-based IoT data curation mechanisms within a Data Enrichment
Toolchain (DET) that employs the NGSI-LD standard to harmonize and enrich
heterogeneous data sources. Furthermore, we have evaluated our design under
experimental research conditions, achieving a robust compromise between
functionality and overhead. Besides, it demonstrates a stable and scalable
performance.
