@conference {Guilavogui201543,
	title = {Integrating linked sensor data for on-line analytical processing on-the-fly},
	booktitle = {Colloquium in Information Science and Technology, CIST},
	volume = {2015-January},
	number = {January},
	year = {2015},
	note = {cited By 0},
	pages = {43-47},
	abstract = {Sensor networks are gaining more and more attention in the current technology landscape. It is undeniable that their use allows a better monitoring of events that occur in the real world. Many sensors have been deployed for monitoring applications such as environmental monitoring, and traffic monitoring. A number of governments, corporates, and academic organizations or agencies hold independently sensor systems that generate a large amount of dynamic information from data sources with various formats of schemas and data. They are making this sensor data openly accessible by publishing it as Linked Sensor Data (LSD) on the Linked Open Data (LOD) cloud. LSD is the concept that defines the publication of public or private organization sensor data without restrictions. This is achieved by transforming raw sensor observations to RDF format and by linking it with other datasets on the LOD cloud. The seamless integration of LSD sources from multiple providers is a great challenge. In this paper, we investigate the possibility of integrating diverse LSD sources using the hybrid ontology approach for on-line analytical processing (OLAP) on-the-fly. With such an ontology-based integration framework, organizations or individuals will have greater opportunity to make their respective analysis based on a large amount of sensor data openly accessible on the Web. {\textcopyright} 2014 IEEE.},
	doi = {10.1109/CIST.2014.7016592},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84938083080\&doi=10.1109\%2fCIST.2014.7016592\&partnerID=40\&md5=b7cd435ce00931bc4db7cd93a4aeaa1b},
	author = {Guilavogui, K. and Kjiri, L. and Fredj, M.}
}
@conference {Guilavogui2014281,
	title = {A hybrid strategy for integrating sensor information},
	booktitle = {ICEIS 2014 - Proceedings of the 16th International Conference on Enterprise Information Systems},
	volume = {1},
	year = {2014},
	note = {cited By 0},
	pages = {281-286},
	abstract = {The combination of sensor networks with databases has led to a large amount of real-time data to be managed, and this trend will still increase in the next coming years. With this data explosion, current integration systems have to adapt. One of the main challenges is the integration of information coming from autonomously deployed sensor networks, with different geographical scales, but also with the combination of such information with other sources, such as legacy systems. Two main approaches for integrating sensor information are generally used: virtual and warehousing approaches. In the virtual approach, sensor devices are considered as data sources and data are managed locally. In contrast, in the warehousing approach, sensor data are stored in a central database and queries are performed on it. However, these solutions turn out to be difficult to exploit in the current technology landscape. This paper focuses on the issue of integrating multiple heterogeneous sensor information and puts forward a framework for decision making process.},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84902319248\&partnerID=40\&md5=eed05f56f6af14fa6195619f958c6b85},
	author = {Guilavogui, K. and Kjiri, L. and Fredj, M.}
}
