A Platform Solution of Data-Quality Improvement for Internet-of-Vehicle Services

Interconnection and intelligence have become the latest trends of the new generation of vehicle and transportation technologies. Applications built upon platforms of cloud-centered vehicle networking, i.e., Internet-of-Vehicles (IoVs), have been increasingly developed and deployed to provide data-centric services (e.g., driving assistance). Because these services are often safety critical, assuring service dependability has become an important requirement. In this paper, we propose DQI, a platform-level solution of Data-Quality Improvement designed to assure service dependability for Internet-of-Vehicle services. As an example, DQI is deployed in CarStream, an industrial system of big data processing designed for chauffeured car services. Via CarStream, over 30,000 vehicles are organized in a virtual vehicle network by sharing vehicle-status data in a near real-Time manner. Such data often have low-quality issues and compromise the dependability of data-centric services. DQI includes techniques of data-quality improvement, including detecting outliers, extracting frequent patterns, and interpolating sequences. DQI enhances the dependability of data-centric services in IoVs by addressing the common data-quality requirements at the platform level. Upper-level services can benefit from DQI for data-quality improvement and reduce the complexity of service logic. We evaluate DQI by using a three-year dataset of vehicles and real applications deployed in CarStream. The result shows that compared with existing approaches, DQI can effectively restore missing data and correct anomalies with more than 30.0% improvement in precision. By studying multiple real applications, we also show that this data-quality improvement can indeed enhance the dependability of IoV services.