TY - GEN
T1 - A Platform Solution of Data-Quality Improvement for Internet-of-Vehicle Services
AU - Zhang, Mingming
AU - Wo, Tianyu
AU - Xie, Tao
N1 - Publisher Copyright:
© 2018 IEEE.
PY - 2018/8/22
Y1 - 2018/8/22
N2 - 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.
AB - 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.
KW - Big Data
KW - Data Quality
KW - Dependability
KW - Internet-of-Vehicles
KW - Interpolation
KW - Sequence Matching
UR - http://www.scopus.com/inward/record.url?scp=85053460764&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85053460764&partnerID=8YFLogxK
U2 - 10.1109/PERCOM.2018.8444581
DO - 10.1109/PERCOM.2018.8444581
M3 - Conference contribution
AN - SCOPUS:85053460764
SN - 9781538632246
T3 - 2018 IEEE International Conference on Pervasive Computing and Communications, PerCom 2018
BT - 2018 IEEE International Conference on Pervasive Computing and Communications, PerCom 2018
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2018 IEEE International Conference on Pervasive Computing and Communications, PerCom 2018
Y2 - 19 March 2018 through 23 March 2018
ER -