Details
| Original language | English |
|---|---|
| Title of host publication | Smart Sensors Networks |
| Subtitle of host publication | Communication Technologies and Intelligent Applications |
| Publisher | Elsevier Inc. |
| Pages | 123-142 |
| Number of pages | 20 |
| ISBN (electronic) | 9780128098653 |
| ISBN (print) | 9780128098592 |
| Publication status | Published - 23 Jun 2017 |
Abstract
The availability of new massive datasets about traffic, coming from Smart Sensor Networks composed of Vehicles, Mobile Phones, and other GPS-equipped devices, is enabling the development of novel Intelligent Applications for Mobility. Among these, a hot and recent research topic is to discover vehicular traffic patterns from these datasets, to provide better mobility predictions. Nevertheless, from a practical stance, there are many technological challenges limiting the applicability of these solutions on the market. Especially the scalability and performance of such systems raise major concerns, given the massive amount of spatio-temporal data to be processed. The current industrial solution is to impose constraints and/or simplifications on both the spatial component of the data and on the employed learning algorithms. This has the drawback that not all the potential information is exploited. To overcome this problem, in this chapter we present a scalable architecture aimed at exploit the computational and storage capabilities of the Cloud. Special emphasis is posed on the analysis of the underlying data models we defined to handle massive dataset for providing vehicular traffic predictions. This solution is actually being evaluated in an industrial context.
Keywords
- Distributed architectures, Intelligent transportation systems, NoSQL, Smart sensor networks
ASJC Scopus subject areas
- Computer Science(all)
- General Computer Science
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Smart Sensors Networks: Communication Technologies and Intelligent Applications. Elsevier Inc., 2017. p. 123-142.
Research output: Chapter in book/report/conference proceeding › Contribution to book/anthology › Research › peer review
}
TY - CHAP
T1 - Scalable Processing of Massive Traffic Datasets
AU - Di Martino, Sergio
AU - Kwoczek, Simon
AU - Nejdl, Wolfgang
PY - 2017/6/23
Y1 - 2017/6/23
N2 - The availability of new massive datasets about traffic, coming from Smart Sensor Networks composed of Vehicles, Mobile Phones, and other GPS-equipped devices, is enabling the development of novel Intelligent Applications for Mobility. Among these, a hot and recent research topic is to discover vehicular traffic patterns from these datasets, to provide better mobility predictions. Nevertheless, from a practical stance, there are many technological challenges limiting the applicability of these solutions on the market. Especially the scalability and performance of such systems raise major concerns, given the massive amount of spatio-temporal data to be processed. The current industrial solution is to impose constraints and/or simplifications on both the spatial component of the data and on the employed learning algorithms. This has the drawback that not all the potential information is exploited. To overcome this problem, in this chapter we present a scalable architecture aimed at exploit the computational and storage capabilities of the Cloud. Special emphasis is posed on the analysis of the underlying data models we defined to handle massive dataset for providing vehicular traffic predictions. This solution is actually being evaluated in an industrial context.
AB - The availability of new massive datasets about traffic, coming from Smart Sensor Networks composed of Vehicles, Mobile Phones, and other GPS-equipped devices, is enabling the development of novel Intelligent Applications for Mobility. Among these, a hot and recent research topic is to discover vehicular traffic patterns from these datasets, to provide better mobility predictions. Nevertheless, from a practical stance, there are many technological challenges limiting the applicability of these solutions on the market. Especially the scalability and performance of such systems raise major concerns, given the massive amount of spatio-temporal data to be processed. The current industrial solution is to impose constraints and/or simplifications on both the spatial component of the data and on the employed learning algorithms. This has the drawback that not all the potential information is exploited. To overcome this problem, in this chapter we present a scalable architecture aimed at exploit the computational and storage capabilities of the Cloud. Special emphasis is posed on the analysis of the underlying data models we defined to handle massive dataset for providing vehicular traffic predictions. This solution is actually being evaluated in an industrial context.
KW - Distributed architectures
KW - Intelligent transportation systems
KW - NoSQL
KW - Smart sensor networks
UR - http://www.scopus.com/inward/record.url?scp=85032156797&partnerID=8YFLogxK
U2 - 10.1016/b978-0-12-809859-2.00008-5
DO - 10.1016/b978-0-12-809859-2.00008-5
M3 - Contribution to book/anthology
AN - SCOPUS:85032156797
SN - 9780128098592
SP - 123
EP - 142
BT - Smart Sensors Networks
PB - Elsevier Inc.
ER -