Details
| Originalsprache | Englisch |
|---|---|
| Titel des Sammelwerks | Proceedings - 2015 10th International Conference on P2P, Parallel, Grid, Cloud and Internet Computing, 3PGCIC 2015 |
| Herausgeber/-innen | Fabrizio Messina, Fatos Xhafa, Marek R. Ogiela, Leonard Barolli |
| Herausgeber (Verlag) | Institute of Electrical and Electronics Engineers Inc. |
| Seiten | 515-520 |
| Seitenumfang | 6 |
| ISBN (elektronisch) | 9781467394734 |
| Publikationsstatus | Veröffentlicht - 2015 |
| Veranstaltung | 10th International Conference on P2P, Parallel, Grid, Cloud and Internet Computing, 3PGCIC 2015 - Krakow, Polen Dauer: 4 Nov. 2015 → 6 Nov. 2015 |
Abstract
Fostered by the "big data" hype in mobility, many research efforts have been aimed at improving techniques to model vehicular traffic patterns for mobility prediction. Nevertheless, from a practical stance, the industry still faces many technological challenges in bringing solutions on the market. Especially the scalability and performance of such systems raise major concerns, given the amount of spatio-temporal data to be processed. The common approach in dealing with these issues is to introduce constraints and/or simplifications on both the spatial component of the data and on the employed algorithms, leading to results that are somehow limited. To overcome these issues, in this paper we report on our experiences and our approaches in providing a solution that meets industrial needs with the aim to leverage the computational and storage capabilities of the Cloud to handle massive dataset for providing vehicular traffic predictions. In particular, we present an approach to deal with real-world datasets to facilitate the knowledge discovery process from this data while matching the business constraints given by the industrial use case.
ASJC Scopus Sachgebiete
- Informatik (insg.)
- Computernetzwerke und -kommunikation
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Proceedings - 2015 10th International Conference on P2P, Parallel, Grid, Cloud and Internet Computing, 3PGCIC 2015. Hrsg. / Fabrizio Messina; Fatos Xhafa; Marek R. Ogiela; Leonard Barolli. Institute of Electrical and Electronics Engineers Inc., 2015. S. 515-520 7424620.
Publikation: Beitrag in Buch/Bericht/Sammelwerk/Konferenzband › Aufsatz in Konferenzband › Forschung › Peer-Review
}
TY - GEN
T1 - An Architecture to Process Massive Vehicular Traffic Data
AU - Kwoczek, Simon
AU - Di Martino, Sergio
AU - Rustemeyer, Thomas
AU - Nejdl, Wolfgang
PY - 2015
Y1 - 2015
N2 - Fostered by the "big data" hype in mobility, many research efforts have been aimed at improving techniques to model vehicular traffic patterns for mobility prediction. Nevertheless, from a practical stance, the industry still faces many technological challenges in bringing solutions on the market. Especially the scalability and performance of such systems raise major concerns, given the amount of spatio-temporal data to be processed. The common approach in dealing with these issues is to introduce constraints and/or simplifications on both the spatial component of the data and on the employed algorithms, leading to results that are somehow limited. To overcome these issues, in this paper we report on our experiences and our approaches in providing a solution that meets industrial needs with the aim to leverage the computational and storage capabilities of the Cloud to handle massive dataset for providing vehicular traffic predictions. In particular, we present an approach to deal with real-world datasets to facilitate the knowledge discovery process from this data while matching the business constraints given by the industrial use case.
AB - Fostered by the "big data" hype in mobility, many research efforts have been aimed at improving techniques to model vehicular traffic patterns for mobility prediction. Nevertheless, from a practical stance, the industry still faces many technological challenges in bringing solutions on the market. Especially the scalability and performance of such systems raise major concerns, given the amount of spatio-temporal data to be processed. The common approach in dealing with these issues is to introduce constraints and/or simplifications on both the spatial component of the data and on the employed algorithms, leading to results that are somehow limited. To overcome these issues, in this paper we report on our experiences and our approaches in providing a solution that meets industrial needs with the aim to leverage the computational and storage capabilities of the Cloud to handle massive dataset for providing vehicular traffic predictions. In particular, we present an approach to deal with real-world datasets to facilitate the knowledge discovery process from this data while matching the business constraints given by the industrial use case.
KW - Mobility Data
KW - Storage Architecture
KW - Traffic Prediction
UR - http://www.scopus.com/inward/record.url?scp=84964507843&partnerID=8YFLogxK
U2 - 10.1109/3pgcic.2015.124
DO - 10.1109/3pgcic.2015.124
M3 - Conference contribution
AN - SCOPUS:84964507843
SP - 515
EP - 520
BT - Proceedings - 2015 10th International Conference on P2P, Parallel, Grid, Cloud and Internet Computing, 3PGCIC 2015
A2 - Messina, Fabrizio
A2 - Xhafa, Fatos
A2 - Ogiela, Marek R.
A2 - Barolli, Leonard
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 10th International Conference on P2P, Parallel, Grid, Cloud and Internet Computing, 3PGCIC 2015
Y2 - 4 November 2015 through 6 November 2015
ER -