@article{Assiri_2020, title={Methods for Assessing, Predicting, and Improving Data Veracity: A survey}, volume={9}, url={https://revistas.usal.es/cinco/index.php/2255-2863/article/view/ADCAIJ202094530}, DOI={10.14201/ADCAIJ202094530}, abstractNote={<p>Data is an essential part of smart cities, and data can play an important role in<br>decision making processes. Data generated through web applications and devices<br>utilize the Internet of Things (IoT) and related technologies. Thus, it is also important<br>to be able to create big data, which has historically been defined as having three<br>key dimensions: volume, variety, and velocity. However, recently, veracity has been<br>added as the fourth dimension. Data veracity relates to the quality of the data. Any<br>potential issues with the quality of the data must be corrected because low-quality data<br>leads to poor software construction, and ultimately bad decision making. In this work,<br>we reviewed the existing literature on related technical solutions that address data<br>veracity based on the domain of its application, including social media, web, and IoT<br>applications. The challenges or limitations and related gaps in existing work will be<br>discussed, and future research directions will be proposed to address the critical issues<br>of data veracity in the era of big data</p>}, number={4}, journal={ADCAIJ: Advances in Distributed Computing and Artificial Intelligence Journal}, author={Assiri, Fatmah}, year={2020}, month={Nov.}, pages={5–30} }