**Acknowledgements**

This work has been sponsored by MINECO through the PERSEIDES project (ref. TIN2017-86885-R), by ERDF funds of project UMU-CAMPUS LIVING LAB EQC2019-006176-P by the European Comission through the H2020 IoTCrawler (contract 779852), and DEMETER (grant agreement 857202) EU Projects. It was also co-financed by the European Social Fund (ESF) and the Youth European Initiative (YEI) under the Spanish Seneca Foundation (CARM).

**5. Conclusions**

*Data without context outlier-based metric's histograms (II).*

**Figure 9.**

**Table 5.**

*Data without context subset.*

*Data Integrity and Quality*

an only manner.

systems.

limited.

**88**

The proliferation of datasets thanks to the new paradigm of the Internet of Things, is populating repositories and open data platforms with data that could be of great use for the scientific community and the technologists to catalyze the growth of scientific knowledge and to make proliferate the creation of new technological solutions. Although all data has value, a point has been reached in which it is necessary to rapidly recognize the quality of a dataset, or a data stream, ideally on

*q*outlier *q*inter 1.00 1.00 0.90 0.80 0.85 0.80 1.00 1.00 1.00 1.00

In this chapter, several concepts have been combined in order to measure the quality of data from IoT-based real-time streams (tested on real-world) sensor

Three sets of quality assurance methods, descriptive, analytic and geometrical have been developed that can be used as levels of a given evaluation, or indepen-

It has been shown that the metrics can be an standard on the calculation of data quality and the majority can be applied independently on the problem context. At the same time, basic concepts that must be present in any system in which the quality of the data is to be guaranteed have been reviewed. Furthermore, it has been shown how it is possible to obtain quality metrics when knowledge about the data is

The applications of this technology are linked to the proliferation of open data portals. There exist many initiatives and organizations that are working towards publishing data as open. The main funding body for engineering and physical sciences research in the UK, the Engineering and Physical Sciences Research Council (EPSRC) is supporting the management and provision of access to research data. They claim that *publicly funded research data should generally be made as widely and*

dently depending on the nature of the datasets to be evaluated.

<sup>2</sup> https://epsrc.ukri.org/about/standards/researchdata/

<sup>3</sup> https://data.europa.eu/euodp/en/home

<sup>4</sup> www.opendata.dk/city-of-aarhus

*Data Integrity and Quality*
