Abstract
As the Internet-of-Vehicles (IoV) technology becomes an increasingly important trend for future transportation, designing large-scale IoV systems has become a critical task that aims to process big data uploaded by eet vehicles and to provide data-driven services. The IoV data, especially high-frequency vehicle statuses (e.g., location, engine parameters), are characterized as large volume with a low density of value and low data quality. Such characteristics pose challenges for developing real-time applications based on such data. In this paper, we address the challenges in designing a scalable IoV system by describing CarStream, an industrial system of big data processing for chaufieured car services. Connected with over 30,000 vehicles, CarStream collects and processes multiple types of driving data including vehicle status, driver activity, and passenger-trip information. Multiple services are provided based on the collected data. CarStream has been deployed and maintained for three years in industrial usage, collecting over 40 terabytes of driving data. This paper shares our experiences on designing CarStream based on large-scale driving-data streams, and the lessons learned from the process of addressing the challenges in designing and maintaining CarStream.
| Original language | English (US) |
|---|---|
| Pages (from-to) | 1766-1777 |
| Number of pages | 12 |
| Journal | Proceedings of the VLDB Endowment |
| Volume | 10 |
| Issue number | 12 |
| DOIs | |
| State | Published - Aug 1 2017 |
| Event | 43rd International Conference on Very Large Data Bases, VLDB 2017 - Munich, Germany Duration: Aug 28 2017 → Sep 1 2017 |
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ASJC Scopus subject areas
- Computer Science (miscellaneous)
- Computer Science(all)
Cite this
CarStream : An industrial system of big data processing for Internet-of-Vehicles. / Zhang, Mingming; Wo, Tianyu; Xie, Tao; Lin, Xuelian; Liu, Yaxiao.
In: Proceedings of the VLDB Endowment, Vol. 10, No. 12, 01.08.2017, p. 1766-1777.Research output: Contribution to journal › Conference article
}
TY - JOUR
T1 - CarStream
T2 - An industrial system of big data processing for Internet-of-Vehicles
AU - Zhang, Mingming
AU - Wo, Tianyu
AU - Xie, Tao
AU - Lin, Xuelian
AU - Liu, Yaxiao
PY - 2017/8/1
Y1 - 2017/8/1
N2 - As the Internet-of-Vehicles (IoV) technology becomes an increasingly important trend for future transportation, designing large-scale IoV systems has become a critical task that aims to process big data uploaded by eet vehicles and to provide data-driven services. The IoV data, especially high-frequency vehicle statuses (e.g., location, engine parameters), are characterized as large volume with a low density of value and low data quality. Such characteristics pose challenges for developing real-time applications based on such data. In this paper, we address the challenges in designing a scalable IoV system by describing CarStream, an industrial system of big data processing for chaufieured car services. Connected with over 30,000 vehicles, CarStream collects and processes multiple types of driving data including vehicle status, driver activity, and passenger-trip information. Multiple services are provided based on the collected data. CarStream has been deployed and maintained for three years in industrial usage, collecting over 40 terabytes of driving data. This paper shares our experiences on designing CarStream based on large-scale driving-data streams, and the lessons learned from the process of addressing the challenges in designing and maintaining CarStream.
AB - As the Internet-of-Vehicles (IoV) technology becomes an increasingly important trend for future transportation, designing large-scale IoV systems has become a critical task that aims to process big data uploaded by eet vehicles and to provide data-driven services. The IoV data, especially high-frequency vehicle statuses (e.g., location, engine parameters), are characterized as large volume with a low density of value and low data quality. Such characteristics pose challenges for developing real-time applications based on such data. In this paper, we address the challenges in designing a scalable IoV system by describing CarStream, an industrial system of big data processing for chaufieured car services. Connected with over 30,000 vehicles, CarStream collects and processes multiple types of driving data including vehicle status, driver activity, and passenger-trip information. Multiple services are provided based on the collected data. CarStream has been deployed and maintained for three years in industrial usage, collecting over 40 terabytes of driving data. This paper shares our experiences on designing CarStream based on large-scale driving-data streams, and the lessons learned from the process of addressing the challenges in designing and maintaining CarStream.
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U2 - 10.14778/3137765.3137781
DO - 10.14778/3137765.3137781
M3 - Conference article
AN - SCOPUS:85036660962
VL - 10
SP - 1766
EP - 1777
JO - Proceedings of the VLDB Endowment
JF - Proceedings of the VLDB Endowment
SN - 2150-8097
IS - 12
ER -