T-warehousing for hazardous materials transportation

T-warehousing for hazardous materials transportation

Azedine Boulmakoul Lamia Karim Ahmed Lbath

Computer Science Department, Faculty of Sciences and Technology (FSTM), University Hassan II, Mohammedia, Morocco

Computer Science Department, Laboratoire LIG, University Joseph Fourier, Grenoble, France

Corresponding Author Email: 
azedine.boulmakoul@gmail.com, lkarim.lkarim@gmail.com, ahmed.Lbath@ujf-grenoble.fr
28 February 2016
| Citation

In recent years, a significant portion of material transported is harmful to human and environment. Thus, the transportation of hazardous materials (HazMat) and its potential consequences raise public interest typically when there is a release of hazardous materials due to an accident. In this paper, we introduce HazMat Trajectory Warehouse (TWarehousing) that can be used for near real time decision making in different applications domain, using MongoDB as a NoSQL database for scalable, fault-tolerant and distributed space time paths big data storage and processing system. The system components are integrated into an interoperable software infrastructure respecting intelligent transport systems architecture. This infrastructure is distributed and based on a service-oriented architecture. It is also scalable by integration of MongoDB with Hadoop for large-scale distributed data processing.


T-Warehousing, HazMat transport trajectories

1. Introduction
2. Related work
3. Trajectories construction
4. HazMat T-Warehousing
5. Conclusion

Alex Holmes. (2012). Hadoop in Practice. Manning Publications Co. Benitez E., Collet C., Adiba M. (2001). Entrepôts de données : caractéristiques et problématique. Revue TSI, vol. 20, n° 2.

Boulmakoul A., Karim L. (2013). A framework for scalable NoSQL storing moving objects’ trajectories. Conférence Maghrébine sur les Avancées des Systèmes Décisionnels, ASD’13.

Boulmakoul A., Karim L., Lbath A. (2012). Moving Object Trajectories Meta-Model and Spatio-temporal Queries. International Journal of Database Management Systems, vol. 4, n° 2, p. 35-54.

Boulmakoul A, Karim L (2014). Construction et entreposage des trajectoires. Work. Int. sur l’Innovation Nouv. Tend. dans les Systèmes d’Information, 4e Ed.

Damiani M. L., Vangenot C., Frentzos E., Marketos G., Theodoridis Y., Veryklos V., Raffaeta A. (2007). Geographic privacy aware Knowledge Discovery and Delivery.

Doucet A., Gangarski S. (2001). Entrepôts de données et Bases de Données Multidimensionnelles, Chapter 12 Book: Bases de Données et Internet, Modèles, langages et systèmes. Hermès editions.

Elzbieta M., Esteban Z. (2008). Advanced Data Warehouse Design: From Conventional to Spatial and Temporal Applications. Data-Centric Systems and Applications. Springer; 1st ed. 2008. Corr. 2nd printing edition (April 6, 2011).

Fitzke J., Greve K. (2010). Frei oder umsonst? - Nutzergenerierte Geoinformation zwischen Freiheit und Kostenlosigkeit. In: Angewandte Geoinformatik - 22. GIT-Symposium. 1. Ed., Wichmann, Berlin, p. 732-741.

Freitas G., Alberto M., Laender H., Luiza M. (2002). Getting Users Involved in the Development of Data Warehouse Application, In Proc. of the 4th International Workshop (DMDW), Toronto, Canada, p. 3-12.

Fubédard Y., Merrett T., Han J. (2001). Fundamentals of spatial data warehousing for geographic knowledge discovery. Geographic Data Mining and Knowledge Discovery, London: Taylor and Francis, p. 53-73.

Giannotti F., Nanni M., Pedreschi D., Pinellin F. (2007). Trajectory Pattern Mining. International Conference on Knowledge Discovery and Data Mining, p. 330-339.

Güting R.H., Behr T., Almeida V., Ding Z., Hoffmann F., Spiekermann M. Secondo (2004). An extensible DBMS architecture and prototype. Technical report.

Hongbo Y., Shaw, S. (2007). Revisiting Hägerstrand’s time-geographic framework for individual activities in the age of instant access, Societies and Cities in the Age of Instant Access. In H. Miller (ed.) Dordrecht, The Netherlands: Springer Science, p. 103-118. http://www.spatial-eye.com/Engels/Applications/Spatial-DWH/page.aspx/117.

Jason Venner. (2009). Pro Hadoop. Build scalable, distributed applications in the cloud.

Kang Y., Kang K.-W. (2013). An Empirical Study of Hadoop Application running on Private Cloud Environment. Adv Sci Technol Lett 35, p. 70-73.

Leonardi L. Marketos G., Frentzos E., Giatrakos N., Orlando S., Pelekis N., Raffaeta A., Roncato A., Silvestri C., Theodoridis Y. (2010). T-Warehouse: Visual OLAP analysis on trajectory data. Data Engineering (ICDE), IEEE 26th International Conference.

Levene M., Loizou, G. (2003). Why is the Snowflake Schema a Good Data Warehouse Design? Information Systems, vol. 3, n° 28, p. 225-240.

MacEachren A. M., Kraak M. (2001). Research challenges in geovisualization. Cartography and Geographic Information Science.

Meng X., Ding Z. (2003). DSTTMOD: A Discrete Spatio-Temporal Trajectory Based Moving Object Databases System. DEXA, LNCS 2736, Springer; p. 444-453.

Mike L. (2012). Planning for Big Data. O’Reilly Media. chapter 8 The NoSQL Movement. ISBN: 978-1-449-32967-9.

MongoDB 10gen. (2013). Available from: http://www.mongodb.org.

Newson P., Krumm J. (2009). Hidden Markov Map Matching Through Noise and Sparseness. Proc. 17th ACM SIGSPATIAL Int. Symp. Adv. Geogr. Inf. Syst., p. 336-343.

OGC 07-022r1 Version: 1.0. (2008). Available from: httpnt Systems and Machine Learning.

Quine W. V. O. (1985). Events and reification. Actions and events: Perspectives on the philosophy of Donald Davidson, LePore E., McLaughlin B. P. (Eds.). Oxford, p. 162-171.

Salvatore O., Renzo O., Alessandra R., Alessandro R. (2007). Trajectory Data Warehouses: Design and Implementation Issues. Journal of Computing Science and Engineering, vol. 1, n° 2, December 2007, p. 211-232.

Shaw S. (2011). A Space-Time GIS for Analyzing Human Activities and Interactions in Physical and Virtual Spaces. Center for Intelligent Systems and Machine Learning.

Simone C., Macedo J., Spinsanti L. (2011). St-Toolkit: A Framework for Trajectory Data Warehousing. AGILE 2011, April 18-22.

Song I., Medsker W. (2001). An Analysis of Many-to- Many Relationships Between Fact and Dimension Tables in Dimension Modeling. In Proc. of the International Workshop on Design and Management of Data Warehouses, vol. 6, Interlaken, Switzerland, p. 1-13.