Methods for Assessing, Predicting, and Improving Data Veracity: A survey

Abstract

Data is an essential part of smart cities, and data can play an important role indecision making processes. Data generated through web applications and devicesutilize the Internet of Things (IoT) and related technologies. Thus, it is also importantto be able to create big data, which has historically been defined as having threekey dimensions: volume, variety, and velocity. However, recently, veracity has beenadded as the fourth dimension. Data veracity relates to the quality of the data. Anypotential issues with the quality of the data must be corrected because low-quality dataleads to poor software construction, and ultimately bad decision making. In this work,we reviewed the existing literature on related technical solutions that address dataveracity based on the domain of its application, including social media, web, and IoTapplications. The challenges or limitations and related gaps in existing work will bediscussed, and future research directions will be proposed to address the critical issuesof data veracity in the era of big data

Analyzing the Propagation of Disturbances in CPES considering the States of ICT-enabled Grid Services

“…There also exist other trust approaches in other domains like the so-called ABI model (ability, benevolence, integrity) in the field of organizational trust [29] or the concept of data veracity in the field of big data [4,21]. However, OC-Trust focuses on trust in and between autonomous software agents in complex systems and provides for this specific environment, a more specific (compared to data veracity) and fine-granular (compared to the ABI model) grouping of factors affecting trust.…”