Work place: Centre de Recherche en Géomatique, Université Laval, Québec, Canada G1V 0A6
E-mail: sylvie.daniel@ulaval.ca
Website:
Research Interests: Computational Engineering, Engineering
Biography
Sylvie Daniel, Eng., M.Sc., Ph.D. graduated in electrical engineering in 1994 (France).
She received the PhD degree in signal processing and telecommunications in 1998 from University of Rennes I (France). She has been working with imagery and geospatial data for over 20 years, including in the industry where she led and managed international research and developments projects. Since 2004, she has been at Laval University (Quebec, Canada) where she is now a FULL PROFESSOR. She has held competitive research funding from several agencies including the Natural Sciences and Engineering Research Council of Canada and the Canadian Foundation for Innovation. Strongly committed to research, her interests include data acquisition (images and LiDAR data), image processing and artificial intelligence, 3D modeling, data fusion and augmented reality. Her research projects focus on 3D modeling of infrastructure and urban environment, on new solutions for hydrographic data collection and on new edutainment tools based on geomatics technologies and augmented reality. She has been actively involved in smart cities and communities and more specifically on 3D technologies contribution towards citizen participation and education. In such a context, in 2014, she was the acting director of the Institute for Information Technology and Society at Laval University, a key player in the field of smart cities.
Dr. Daniel is a senior member of IEEE and member of ACM. She has over 45 papers published in peer-reviewed journals and conferences. She was involved in the publication of 2 books and 5 book chapters.
By Belko Abdoul Aziz Diallo Thierry Badard Frederic Hubert Sylvie Daniel
DOI: https://doi.org/10.5815/ijieeb.2018.06.01, Pub. Date: 8 Nov. 2018
To contribute in filling up the semantic gap in data warehouses and OLAP data cubes, and enable semantic exploration and reasoning on them, this paper highlights the need for semantically augmenting Geo/BI data with convenient semantic relations, and provides OWL-based ontologies (ODW and OOLAP) which are capable of replicating data warehouses (respectively OLAP data cubes) in the form semantic data with respect of Geo/BI data structures, and which enable the possibility of augmenting these semantic BI data with semantic relations. Moreover, the paper demonstrates how ODW and OOLAP ontologies can be combined to current Geo/BI data structures to deliver either pure semantic Geo/BI data or mixed semantically interrelated Geo/BI data to business professionals.
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